TECH EXEC Working File 23_PAGES

THE FUTURE OF DATA & AI

Niresh Rajah

Chief Data & AI Officer at DLA Piper

WELCOME CONTRIBUTORS

IN THIS ISSUE…

t Tech-Exec, we believe in the power of collaboration and knowledge sharing among industry experts. In this issue, we showcase thought leaders, innovators, and visionaries who are shaping the future of technology and business. Our contributors are at the forefront of the latest trends, insights, and strategies that are driving success in today’s digital landscape. Join us as we dive into the minds of these exceptional individuals who are revolutionising the tech world and inspiring the next generation of leaders.

Franck Carassus

Franck Carassus is the co-founder and Chief Operating Officer of OpenDataSoft, a leading SaaS platform that enables organisations to share, visualise, and reuse data effortlessly. With over two decades of experience in the technology sector, Franck has dedicated his career to serving the public sector, providing software and services to enhance data accessibility and READ HERE

Cameron Bergen

Cameron Bergen is a seasoned technologist with a robust background in digital transformation. Cameron has held pivotal roles at leading companies, including Microsoft and Amazon Web Services (AWS), where he has driven innovation and spearheaded strategic initiatives. His expertise spans cloud computing, software development, and digital transformation, making him a valuable contributor to Digital Edge Magazine. READ HERE

Tom Perrone

Tom Perrone is the Senior Vice President of Global Professional Services at project44, a leading supply chain visibility platform. With extensive experience in supply chain management and logistics, Tom has been instrumental in forging strategic partnerships to enhance service offerings and address customer challenges. He has provided insights into emerging trends in supply chain technology and operations, emphasising the importance of transparency, automation, and resilience in modern supply chains. READ HERE

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JP’S EDITOR’S NOTES

EMBRACING THE FUTURE OF TECHNOLOGY LEADERSHIP

As Editor of Tech-Exec, a platform dedicated to bringing together C-suite leaders for meaningful discussions, thought leadership, and career development, I’m continually inspired by the quality of technology leadership that we see. It’s not just about adapting to change; it’s about leading it, and today’s technology leaders face unique challenges and immense opportunities, more than ever before.

One of the most pressing issues is bridging the skills gap in the workforce. Many long-standing executives are tasked with guiding companies through digital transformation while managing the integration of fresh talent into their teams. For many organisations, this often means balancing seasoned employees with a new generation of tech-savvy, innovative thinkers. Blending legacy staff and new talent is more than just a numbers game; it’s about fostering a culture of collaboration and continuous learning.

The magic happens when experienced leaders share their tried-and-tested strategies with fresh voices, offering new perspectives that challenge traditional thinking. This fusion of experience and innovation sparks conversations that drive businesses forward. This kind of cross-generational dialogue ensures progress while respecting the roots of established success.

At Tech-Exec, these discussions play out daily as our community thrives on exchanging ideas between seasoned leaders and the new blood-shaping industries. Together, they’re tackling the big questions— how do we evolve while maintaining the core values that have made us successful? How do we leverage technology to create more agile, resilient organisations?

In this issue, we explore these questions and many more - exploring how today’s leaders are navigating this complex intersection of legacy, innovation, and the future of work.

Our lead article and cover star is a seasoned data professional and joint runner-up of the Tech-Exec Data Leader Award for 2024. We welcome the expert insight and analysis of Niresh Rajah - I met up with Niresh for this ‘fireside’ chat in a hotel lounge in Shoreditch and enjoyed hearing his views on these very same topics as we had coffee…by the fireside!

Joining Niresh from our growing community of UK data leaders are Gareth Powell (IM LLP) Chris Wyard (Beazley), Phil Bishop (Fortescue Zero) and Kieran Poynton (Bromfords), all sharing their unique perspective on data management and technology

leadership, whilst the trained eyes reading these insights will of course see the similarities in the challenges faced in their day-to-day roles.

In addition to these exclusive interviews, we welcome contributions and insights from Cameron Bergen at mode40, Frank Carassus, CSO and Founder of OpenDataSoft and Tom Perrone, SVP of Global Professional Services at Project44, who imparts his knowledge on the topic of Gen AI‘s role in the future of Supply Chain Management.

I hope you enjoy the issue. By the way, please let me know if anyone is buying one of the Wayne Enterprise Tumblers. I’d love to go for a spin!

Until next time…

JP

CEO AND FOUNDER

© Articul8 Media Ltd 2025. All rights reserved. Whilst every effort is made to ensure that information is correct upon publishing, Articult8 Media Ltd is not responsible for any errors or omissions, or for the results obtained from the use of this information. All information in this magazine is provided “as is”, with no guarantee of completeness, accuracy,timeliness or of the results obtained from the use of this information. This magazine may not be reproduced or used in any manner whatsoever without the express written permission of the publisher except for the use of brief quotations in a book review.

PORTFOLIO

Domaine Vincey - Oger

Chardonnay Grand Cru 2017

exec.summary

A bitesize collection of news, content, ideas, thoughts, and papers from around the industry.

For the first time, Russia’s APT28 hacking group has been reported to breach an espionage target’s Wi-Fi network remotely. Leveraging advanced techniques, they hijacked a laptop in a building across the street. This operation underscores APT28's sophisticated capabilities, including using compromised routers to proxy traffic and intercept sensitive data. Their campaigns have historically targeted governments and key industries globally, exploiting widely-used hardware vulnerabilities. This latest revelation highlights the importance of robust cybersecurity measures for personal and organisational networks.

A new report by Publicis Sapient highlights a significant divide between C-suite executives and the “V-suite” (practitioners) in their approach to generative AI. While the C-suite emphasizes customer-facing applications like improving sales, service, and experience, practitioners identify broader potential in areas such as HR, finance, and operations. This disparity underscores differing priorities in AI adoption, with leaders favouring immediate business impact and practitioners seeking transformative efficiencies across organizational functions.

AI can now replicate human personality traits with surprising accuracy after a brief two hour interview, capturing values, preferences, and unique attributes. Research by Stanford University and Google DeepMind has refined neural network models to align more closely with human cognition and behaviour, emphasising context-based understanding. This development has potential applications in personalised AI assistants and ethical challenges in value alignment for AI systems.

Amnesty International's latest report, Recharge for Rights, evaluates the supply chains and human rights policies of 13 leading electric vehicle (EV) manufacturers. The findings reveal serious shortcomings, with none of the companies scoring above 51 out of 90 on a human rights due diligence scorecard. While MercedesBenz and Tesla lead the rankings, companies like BYD, Hyundai, and Mitsubishi are criticized for poor transparency and lack of robust policies. This highlights a pressing need for the EV industry to address human rights risks, including those tied to mining operations for essential battery minerals like cobalt and lithium.

AXIOM SPACE X PRADA SPACESUIT

SPACESUITS FOR THE NEXT LUNAR FRONTIER

When NASA's Artemis III mission touches down on the Moon’s surface, it will mark more than the first crewed lunar landing since Apollo 17 in 1972— it will be a sartorial leap into the future. Enter the Axiom Extravehicular Mobility Unit (AxEMU), the next-generation spacesuit that merges cutting-edge technology with high-fashion innovation.

A collaboration between aerospace contractor Axiom Space and iconic Italian fashion house Prada, the AxEMU redefines the role of design in space exploration. Beyond its sleek, futuristic aesthetics, the suit is built for extreme environments. Engineered to perform under the harsh conditions of the lunar south pole, it features advanced cooling systems to regulate heat, a carbon dioxide scrubbing system for breathable air, and precision coatings on the helmet and visor to optimize visibility.

Prada’s expertise in material sciences and sewing techniques has been pivotal, integrating durability with comfort and mobility. Custom gloves, pressure garments, and avionics ensure that astronauts are equipped with life-critical functionality, while maintaining a design ethos that wouldn’t look out of place in a Milanese atelier.

As NASA pushes forward with plans for sustained lunar exploration, the AxEMU represents a fusion of human ingenuity and design excellence. The spacesuit is slated to undergo a critical design review by 2025, setting the stage for future missions where form and function are equally prioritized.

In space and on Earth, collaborations like this remind us that innovation thrives at the intersection of disciplines. Welcome to the new era of space couture.

FERRARI F80 SUPERCAR

THE NEXT CHAPTER IN HYPERCAR EXCELLENCE

Every decade, Ferrari redefines automotive excellence with a halo car that embodies the brand’s relentless pursuit of speed, innovation, and design.

Following in the tire tracks of the 288 GTO, F40, Enzo, and LaFerrari, the upcoming F80 promises to cement its place in the lineage of legends when it begins production in 2026.

Breaking tradition, the F80 moves away from Ferrari’s signature V12 engines, opting instead for a cutting-edge hybrid drivetrain. At its heart is a 3.0-liter, twinturbocharged, 120-degree V6 engine paired with electric motors at both axles.

The result?

A staggering 1,184 horsepower, ensuring the F80 delivers both blistering performance and improved efficiency.

Aerodynamics play a starring role in the F80’s design, a result of Ferrari’s expertise honed in Formula 1 and Le Mans. The car generates an extraordinary 2,315 pounds of downforce, ensuring it hugs the tarmac even at high speeds.

Complementing this is a 48-volt active suspension system and a design that marries functional aerodynamics with bold, sci-fi aesthetics, under the direction of Ferrari’s head designer, Flavio Bertoni.

For purists, the F80 offers five-lug carbon fiber wheels instead of the centerlocking variants seen on its predecessors, ensuring a perfect blend of track-ready dynamics and everyday usability.

With just 799 units planned, exclusivity is guaranteed, and with Ferrari’s pedigree, the F80 is set to become the pinnacle of hybrid hypercar performance. Deliveries are expected to begin in 2026, marking the start of a new era for Ferrari aficionados.

WAYNE ENTERPRISES TUMBLER

Officially licensed by Warner Bros., this limited-edition Tumbler is not just a car but a statement of exclusivity. Only 10 units will be produced, and each one will be available by invitation only.

Powered by a formidable 6.2-liter LS3 V8 engine generating 525 horsepower, paired with a 4L85E paddle-shift transmission, this Tumbler packs both power and precision.

Add to that the inclusion of smoke screen systems and imitation gun turrets, and you’ve got a vehicle that embodies the ultimate in fictional technology. However, for those who dream of taking it out on the streets, there’s a catch: the Tumbler is not streetlegal. Yet, for those fortunate enough to secure one, it represents the pinnacle of fan-driven luxury — a highperformance vehicle that’s as much a work of art as it is an engineering marvel.

For those interested in this rare opportunity, inquiries are officially open, with the coveted spot available to only a handful of the world’s most discerning collectors. It’s the perfect marriage of pop culture and engineering excellence—truly a ride fit for the modern-day Bruce Wayne.

Officially licensed by Warner Bros., this limitededition Tumbler is not just a car but a statement of exclusivity. Only 10 units will be produced, and each one will be available by invitation only.

Powered by a formidable 6.2-liter LS3 V8 engine generating 525 horsepower, paired with a 4L85E paddle-shift transmission, this Tumbler packs both power and precision.

Add to that the inclusion of smoke screen systems and imitation gun turrets, and you’ve got a vehicle that embodies the ultimate in fictional technology.

A LEGENDARY RIDE FOR THE ELITE FEW

However, for those who dream of taking it out on the streets, there’s a catch: the Tumbler is not street-legal. Yet, for those fortunate enough to secure one, it represents the pinnacle of fandriven luxury — a high-performance vehicle that’s as much a work of art as it is an engineering marvel.

For those interested in this rare opportunity, inquiries are officially open, with the coveted spot available to only a handful of the world’s most discerning collectors. It’s the perfect marriage of pop culture and engineering excellence—truly a ride fit for the modern-day Bruce Wayne.

Niresh Rajah

A fireside chat with Chief Data & AI Officer at DLA Piper, Board Advisor at Sopra Steria and runner up in Tech-Exec Data Leader of the year.

"I firmly believe in the symbiotic relationship between Data & AI"

1: DEFINING IMPACT

How do you see generative AI reshaping how organisations harness and analyse data for strategic decision-making?

As you know, Gen AI relies heavily on vast amounts of ‘available’ , accessible and contextualised high-quality data. I don’t subscribe to ‘Data as the new Oil’ sentiment especially as we strive to move to a sustainable source of energy, however Data is the ‘lifeblood’ which fuels these AI systems. The availability (when correctly contextualised and accessible) of extensive and diverse datasets allows large language models (LLM’s) to learn patterns, generate insights, and provide innovative solutions especially in ‘knowledge rich’ environments .

I firmly believe in the symbiotic relationship between Data & AI – the strength of the data foundation results in the outcomes and benefits to be realized by AI/Gen AI.

I have seen throughout out my career the focus on being data driven and strategic decision making being attuned to decisions driven by data. Whereas we have been using analytics and visualization to tell the story backed up by Data, the nuance of AI undertaking sophisticated data interpretation and predictive analytics enables trends and correlations to be formulated on markets, revenue/ profitability , clients etc where the unsupervised learning of these models do result in some

insightful interpretations which isn’t immediately apparent with traditional data analytics.

2: REAL-WORLD APPLICATIONS

Can you share examples of how generative AI is leveraged innovatively?

I can talk about the two industries which I have been working in extensively since Gen AI exploded into the scene in Q1 2023 – Financial Services and Legal Services, both regulated, knowledge intensive industries. The knowledge rich industries are prime for automation (which has been going for a number of years especially in Financial Services), and more recently Gen AI.

In Financial Services, it is the development of detailed financial and risk models, performing risk assessments, and generating investment strategies based on historical and real-time data. This is especially pertinent where credit risk and model risk calculations for lending, liquidity and basis of capital ratios are based on a number of permutations and correlations dependent on modelling multiple data sets. More broader, there has been more doubling down on the use of LLM’s to review customer journey interactions, sentiment, complaints and customer interactions to accurately predict the scenarios from more advanced chatbots to the decision trees to enable a call-back and customer reach out.

The Legal industry has not been traditionally a bastion of innovation and utilization of automation etc to simplify the workflow. The post Gen AI world resulted in a disruptor of immense proportions for the industry which has been clients of law firms increasingly asking questions on the use of Technology and innovation.

There are multiple use cases including the the basis of contextualised knowledge rich legal research, the preparation for and analysis in due diligence and fundamentally in the drafting, review of legal documents especially complex contracts. In the knowledge and document heavy industry such as Legal , there is also a key area of efficiency and productivity where huge voluminous documents can be summarised together with a chatbot functionality to interrogate documents to pull out specific answers to questions and descriptions of clauses etc through the advent of ‘Legal AI Assistants’.

Given time actually is money in the Legal world , increasing efficiency and reducing the time required for basic and complex tasks is readily becoming the differentiator.

3: DATA FOUNDATION

& AI

We hear that Data Foundation is essential for Generative AI play, can you bring that to life for us?

I do think there is a huge misunderstanding at all levels of organisations that somehow

AI is the ‘magical bullet’ and just by purchasing the latest AI Software licences (at significant licence costs mind you), that the key use cases, challenges or innovation would immediately appear as if by magic.

This obviously comes from a number of a sources; one the technology vendors themselves extolling a fantastic UX/UI (front end) and user experience and the best presentation the uninitiated have ever seen. The second is that Board members suffer from ‘Board FOMO’, where the innovation by press release has convinced them that their firm needs to be on the bandwagon as fast as possible. Last and not least of course are the multiple consulting firms whipping up myths around benefits and time to market to launch scaled AI programmes through AI products !

In reality, establishing and leveraging the Data Foundation is a critical enabler for AI and Gen AI use cases. Data Foundation in simplest terms is the coupling of good old fashioned Data Management with an enterprise level cloud Data Platform. Data Management of course, covers all aspects of how to organize, curate and align the most critical data an organisation has covering client, employee, financial, product, and transactional data sets and where applicable relevant third party data. The focus is to understand and catalogue the most important data and organisation has and then

ensure the quality of the data, and establish processes so the providence of that data can be mapped and leveraged in a repeatable automated manner.

The second part of the couple , data platform does require the purchasing and provisioning of one of the leading cloud based data lake/ data warehouse technologies to ingest, transform, store and orchestrate the data in a manner by which it can be consumed for analytical or data science purposes to create value. Leading Data Platforms enable inter-operability with latest data analytics, data catalogue, ML Ops applications and data visualization tools, etc.

It goes without saying for any Gen AI use cases, the foundational models rely upon sound Data Foundation to deliver the outcomes and benefits…..this is being increasingly called ‘AI ready Data’. By maintaining high data quality, organisations can trust the insights generated by AI, leading to more reliable and actionable outcomes.

4: ETHICAL CONSIDERATIONS

Ethical Considerations: With the rapid adoption of generative AI, how do you address concerns about ethical data use and algorithmic bias?

This is the most critical aspect of AI and Gen AI – how do we address the ethical data use, algrothimic bias and Intellectual

Property rights especially in creative industries. Building on the previous items we discussed on Data Foundation, the data sets used for training the AI models are fundamental in the potential biases created in the algorithams and added to this is selection of the appropriate foundational models in Gen AI. The foundational models (very well known examples such as Open AI’s GPT-3 and GPT-4 for Chat GPT) which can be built on top of for a variety of use cases and applications and hence selection and curation are important.

Addressing these concerns requires a multi-faceted approach that encompasses robust data governance, transparency, and diversity. A comprehensive data governance framework is essential for ensuring that data is used legally and ethically . Aligned to the Data Foundation discussed we have just had, this involves creating policies and controls that govern data collection, storage, access, and usage. Establishing clear rules and guidelines on data privacy, consent, and security to protect individuals’ rights and maintain trust is fundamental.

Transparency is vital for building trust in AI and Gen AI models, and hence documenting and being transparent of how AI models are developed, trained, and deployed. This includes providing insights into the data sources used, the methodologies applied, and the decisionmaking processes of the AI/ Gen

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AI applications and use cases. This is one of the reasons I am skeptical of black box Gen AI solutions bought off the shelf and not one which we as the host organisation can have full explainability of the AI models.

As we have already seen some of the issues with Gemini and other AI Models, ongoing monitoring, sample testing and human in the loop to continuously review any bias in the algorithams is important.

5: HUMAN-AI COLLABORATION

How can organisations balance relying on AI-driven insights and maintaining human oversight in data-driven decisions?

This is interesting and we can already see the divergence which the U.S is taking with regard to the European Union in terms of regulation. Its important to incorporate human-in-theloop mechanisms where people can validate and refine AIdriven insights and outcomes. The design of the AI/Gen AI systems and how the models are trained, grounded in which data sets and the contextual understanding are the aspects ‘Humans’ fundamentally need to play a role. For some use cases, AI can be tasked with data processing, pattern recognition, and generating insights, while humans should focus on interpreting these insights, considering context, and making final decisions. It is the interpretation and decision making which are the crucial

aspects. This leads onto AI and Data Literacy for those with roles to be the human interface in this process. Good to encourage data literacy across all level of the organisational and train/coach employees with the skills to understand and interpret AI-generated insights.

Balancing AI-driven insights with human oversight is crucial for making informed and ethical decisions. By establishing clear roles, ensuring transparency, promoting data literacy, and fostering a collaborative culture, organisations can harness the power of AI while maintaining the critical element of human judgment

6: DATA-DRIVEN CULTURE

What are the key steps to fostering a data-driven culture that embraces change and innovation at all organisational levels?

This is something that all CEO’s and Boards are referencing ‘being Data Driven’ without necessarily understanding the investment, patience, commitment and changes in responsibilities and cultural change necessary to achieve sustainable success. As you become more senior at ExCo’s and Board’s, it is also something which is more and more difficult to do as ‘experience’, ‘gut instinct’ and good old fashioned hubris gets in the way. So how does one establish a data driven culture; first by setting a clear vision and direction that

we are going to be enable data driven decision making and, second by walking the walk or eating one’s own dog food as a previous leader used to tell me, which involves senior leadership meetings where decisions are continuously made utilizing data as an input. By the senior leadership team making decisions using data and also the stories which emerge about how decisions are being made utilizing this data at the top of the house – sends a very clear message to everyone in the company. Thirdly , being data driven from putting together numerical values and developing data visualization using some of the readily available tools such as Power BI and then the storytelling which comes with reading and interpreting the data is both an art and science simply termed ‘Data literacy’ and this needs to be trained (formal training ) and coached. We all get better at this by doing this more and more and making it an inherent part of our meetings and papers.

The challenge is then to drive adoption whereby through ongoing data transformation techniques – the adoption of data driven business cases, ongoing validation, role model behaviour from senior folk, and publicly rewarding data driven decision making starts to make an impact. The impact of data driven approach needs to be measured, tracked and those powerful stories continue to be told. One thing to look out for in 2025/2026 is the movement from 'data-driven' to 'decision intelligence' , purely as it has

been incredibly difficult to track the benefits and outcome measures from data-driventhis is about making sure the 'decisions' are at the heart of how to utilise the data and avoid over analysis. Decision Intelligence is also a method by which AI can be used to map all the complex data in an organisation and drive it towards decision making that results in tangible measurable outcomes. I think time will tell if 'decision intelligence' is a passing 'fad' /buzzword or if it can really begin to impact the bottom line of organisations. Finally and importantly, data driven or Decision Illelligence cultural change is a long game and we need the Board and ExCo to be patient to see the benefits come through.

7. OVERCOMING RESISTANCE

How do you address resistance to AI and data-driven change, particularly from teams or individuals accustomed to traditional methods?

Everyone is coming to grips with Generative AI and the impact its having on day to day business. The Gen AI foundational models themselves and the applications from the technology vendors and hyperscalers are also moving at break-neck speed. I feel that the biggest challenge isn’t overcoming resistance, its actually filtering through all the noise which is out there to come up with a handful of Gen AI use cases

which can deliver actual benefits and then establishing focus to land the pilots and then scale appropriately. Coupled with this is the time that needs to be spent on educating or coaching the impacted individuals on how to leverage the Gen AI use cases appropriately. Finally it is the continuous story telling and measuring and tracking actual benefits – the delivery of time saved on a task, as an example utilizing an AI assistant based on Open AI GPT 4 which we have built, it reduced the time taken on a task from 3 hours to 10minutes. Delivering on the value and then measuring this in a focused fashion on how it actually helps someone do their job, can do wonders in changing ‘naysaers’ to ‘cheerleaders’.

8: FUTURE OUTLOOK FOR AI AND DATA

What trends or technologies do you believe will define the next wave of AI and data's influence in organisational transformation, and how can companies prepare for them?

I can probably be a very rich man if I was able to predict that ! We are only at the start of the Gen AI revolution, and I think we will see the adoption of the use cases with the firms which are truly committed in this journey both in commitment and investments, actually translating the use cases for efficiency, revenue generations, customer acquisition at much greater scale in 2025/2026 with tangible benefits.

The pace of technological innovation and change with the hyperscalers is so vast that I am expecting Agentic AI to make the giant leap forward to reveloutinise work-flow in business.

Agentic AI whereby the AI takes autonomous decisions and selects the best response and best ‘AI Models & Engines’ to use for each problem or challenge adapting in real-time with an understanding of context and objectives will be revolutionary and what we will then experience the real intelligence in artificial intelligence.

I see the practice of Data through ‘enhanced Data Foundations’ really taking to the forefront so that AI ready Data is developed and optimized. Similar to BCBS 239 in the Financial services sector which really brought the professionalism of Data Management to the forefront, I think the advent of Data Foundation for Gen AI purposes will result in huge investments and focus on Data Platforms, Data Management & Governance, Architecture and Data Engineering.

As we have discussed, data is indispensable for generative AI, especially in knowledgerich industries. By effectively harnessing and managing data, organisations can unlock the full potential of AI to drive innovation, efficiency, and strategic advantage.

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BIO

Niresh is a visionary Chief Data & AI Officer and Board Advisor/NED with extensive experience as an executive leader in Data management, analytics, artificial intelligence, automation, regulatory change and innovation. He has held executive positions in Data & Analytics at Danske Bank, Barclays Bank and Lloyds Banking Group. He is also a NED and Board Advisor, with non-executive roles at Sopra Steria and Data for Good.

Recognised as joint runner up in the 2024 Tech-Exec Data Leader awards, Niresh has cemented his reputation as a transformative leader in data and artificial intelligence.

As an executive level Group Chief Data & AI Officer, he has led domains across Data & AI Strategy, Data Management, Data Science & Artificial Intelligence, Data Platform & Architecture, and Data Programmes. He has also led major complex regulatory and data transformations at Board level including Brexit changes, KYC/AML and GDPR working with regulators across Europe. Prior to working in Financial Services, he worked as a Management Consultant with Deloitte, Ernst & Young, and Grant Thornton, spanning multiple industries including Oil & Gas, Mining, FMCG and Financial Services.

His unique expertise is through being able to both, develop innovative data & AI strategies in complex highly regulated multi-national organizations, and then be able to steer and drive forward the implementation to achieve the desired results balancing benefits, capability build, stakeholder ambitions and regulatory considerations.

Niresh was selected in the Top 100 Global Chief Data Officers for 2024 and 2023 and won the European Data Management Industry professional of the year award for 2023. He has been selected in the Data IQ awards Top 100 influencers for 2024, 2023 and 2022.

Niresh’s is passionate for sharing his vast knowledge and experience through advancing the Data, Analytics and AI community, he is a co-chair of the UK chief data officer governing body for Evanta (a Gartner company); an advisory board member at A-Team Group (for both Data and RegTech), Data IQ and CDO Magazine. He has steered new innovation in the Data & AI space by being a mentor for start-up’s and scale-up’s through a number of mentor programmes at University of Oxford, Seedcamp and FinTech Alliance. He is also a judge at a number of awards such as the British Data Awards, Women in Governance Risk & Compliance, Data IQ, A-Team Data Management and A-Team RegTech Awards.

Gareth Powell

Group Data Officer & Partner at Irwin Mitchell LLP

TALKING TRANSFORMATION WITH TECH-EXEC’S DATA LEADER OF THE YEAR

Gareth Powell is a data powerhouse. Awarded Tech-Exec’s Data Leader of the Year for 2024, despite strong competition from an excellent pool of nominees, he’s also been named in the DataIQ 100 list eight times and in the HotTopics Global CDO 100.

With a career that’s crossed Studio Retail Group, TalkTalk and N Brown Group among others, he’s gained not only a huge amount of experience but also intellectual fulfilment, as he says, “I guess at the heart of it, I'm curious.”

Gareth is now using his vast knowledge in his role at Irwin Mitchell, a full-service law firm with offices across the UK. As Group Data Officer here, alongside his remit of driving the analytics agenda, maintaining data platforms to provide analytics and providing the data governance around that, he’s also responsible for maintaining, decommissioning and building out the data technology stack. Alongside this, he’s tackling business transformation projects, including supporting the implementation of – and data migration to – a new case management system and new people platform in collaboration with his IT colleagues.

Gareth sat down with us to discuss his approach to data-driven business transformation, the importance of customercentricity and how he has engaged nontechnical execs to get leadership buy-in through his career.

AN APPROACH TO EVOLVING THE DATA FUNCTION

Having spanned a range of sectors from retail, financial services, telecommunications and now professional services, we wanted to understand more about the similarities and differences there are in evolving the data function across sectors and some of the key parts in Gareth’s approach to this.

As Gareth points out, “Ultimately, when you're coming into a business, there's no one-size-fits-all. There are always subtleties. Irwin Mitchell is an LLP. I've worked in PLCs and in businesses backed by hedge funds or private equity in the past. Differences in company structures might determine how you evolve a structure or a data department. It is all down to needs.”

THE IMPORTANCE OF THE FIRST THREE MONTHS

Committing time to diagnosing the portfolio that he’s inherited is a vital first step for Gareth. He considers the first 90 days in a new data leader role crucial for listening, probing, developing the right relationships and then starting to form hypotheses that need testing to validate the strategic change required.

There’s one fundamental question that Gareth wants to get to grips with during this period. That is, as he explains, “What does the business need from its data? If you can't answer that question, you can't determine the approach that's required. For me it's always got to be an offensive as well as a defensive approach, and the exciting bit is always the offensive.”

FORMING FIRM RELATIONSHIPS

Focusing on the topic of relationships, Gareth pointed out, “If you're not indemand with the board and the senior leadership team that sits underneath them, there's something not right.”

Forming these relationships is really important to Gareth, not only as a way of truly understanding the business needs via these key people, but also as a way of engaging executives who are going to be vital in championing the data team.

As Gareth puts it, “If it's just the Chief Date Officer that's waxing lyrical about data, you're not going to get too far. Getting the board and senior leadership team bought into the vision is extremely important.”

DIAGNOSING DATA MATURITY

Evaluating the maturity of the analytics that's being delivered into the business is another point, whether it’s just simplistic reporting or diagnostics through to data science and artificial intelligence.

Gareth expanded on this, “Diagnosing where that maturity is will set a precedence I think in terms of where you move in future. So, is there hypothesis-based thinking in the business when it comes to solving business opportunities with Data? Equally, you have to have that alwayson analytical approach and mindset.”

“And every vertical trades. You have to understand how the P&L works. What are the revenue lines? What are the cost lines? Where do we make our money? Where do we lose our money? What can be optimised using data?”

“I'm a big believer in using a statistic that sticks and benchmarks.”

STRUCTURING THE DATA DEPARTMENT

Another point of similarity across sectors is the need for the same capabilities –data analytics, engineering, architecture and governance. However, Gareth points out, that you have to rightsize those capabilities to the organisation. “I'm a big believer in using a statistic that sticks and benchmarks. My rule of thumb is broadly that in a data department, 50% of it should be focused on analytics and data science. The other 50% needs to be focused on building out the data platforms, maintaining them, maintaining the quality of data through data governance and closely collaborating with the technology function.”

DECIDING ON A TARGET OPERATING MODEL

Understanding the existing target operating model before making changes is an important step, “What does the existing model enable the business and stakeholders to get value from the data? Is there an issue? If so, what needs to change?”

“You have to determine the maturity of thinking around data in the business coupled with good consideration of the culture of the business. This enables you to think about what the appropriate target operating model is.”

When it comes to target operating models, he’s followed a similar approach throughout his leadership career, “If it's never been centralised, you more than likely need to centralise it. You've got to establish that strong collaboration across data professionals in a business and think about the consistency in skills and if they have a common career framework.

“Every model has got to be flexible, but you need to get that commonality. The way that you're going to derive value from data is by splicing datasets together and through collaboration.”

Gareth favours a hub and spoke model. While the data team ultimately reports to him, he has domain SMEstyle teams into areas like finance or marketing or matter (product), “Because they need to understand the nuances of those areas and have extremely strong relationships with the data champions who know exactly what they want from the data.”

PRIORITISING PROJECTS

Creating a road map and being able to prioritise the data team’s tasks is something that’s always been helpful to Gareth, “Every business I've been in, data teams are massively in-demand. The minute you start to flag the opportunity in a business, you're going to

be more heavily in-demand. I think it's healthy to have that debate with key stakeholders about prioritising and getting to that place where you have discussion between senior leaders in the business. These are both indications of an organisation becoming more Insight-driven.”

COMMUNICATING THE VALUE OF DATA TO NONTECH EXECS

Gareth has a great ability to engage leaders who may have a varying degree of understanding of what the data function is aiming to achieve. He considers storytelling a great way to take people on the journey and secure agreement. “You've got such a mix of leaders in in any senior leadership team or boardroom. I try to understand what's keeping them awake at night. What are the things that can influence the priority KPIs of a business? How can data or technology support that?

Once he understands this and the data maturity in the business, he can begin to put a compelling story together. “You've got to frame a narrative that resonates with stakeholders and talk in business currency and focus on outcomes and business impacts.”

For Gareth, it’s all about being precise and creating visualisations that people are going to engage with

“Trends are friends. Trending of information provokes interest".

and get curious about, “Trends are friends. Trending of information provokes interest. Keeping that simple and keeping visualisations consistent so that you've got a logical flow means that people can follow the story more easily. You also need to walk people through how to use it or how to get the benefit out of the analysis you’ve developed or dashboard that you’ve shared.”

As part of this, Gareth thinks it’s vital to share outcomes and value creation. That means being clear to the business about what’s going to be delivered, whether it’s giving time back to people or enabling cost savings, incremental revenue or incremental profit.

This is something Gareth has stressed the importance of throughout his career, “I was lucky my first ever boss was very adamant about baking these things into our objectives. At the time I thought, ‘How on earth am I going to do that?’ But you need that mindset, and I try to instil it amongst all my teams. You've got to focus on the things that are going to make the boat go faster. I think it's far more fulfilling for an analyst or a data engineer to be able to articulate the value that they have created.”

“Also, landing in any business, you've got to get that licence to operate, so balancing

tangible, visible success in parallel with your data strategy is key. That starts to grease the wheels a little for building the investment case.”

A CUSTOMER-CENTRIC FOCUS

As well as understanding internal stakeholders and customers, Gareth places emphasis on understanding external customers. He’s interested in seeing whether common analytical approaches in other sectors can transpose in the legal sector.

“In telco, retail and financial services there’s quite mature thinking about how a customer base is viewed as an asset, almost as a subscription annuity. You can predict what revenue you're going to get from that customer base and derive enriched insights from it.

“You can forecast at quite a granular level and that can drive the whole customer experience including how you cross-sell and up-sell. I think there's an opportunity to do that in professional services, it's one of the reasons that attracted me to I where I am”.

The business at Irwin Mitchell is very focused on client experience, with skilled lawyers delivering a great service characterised by excellent Net Promoter Scores. Gareth explained, “The mantra is how do we just make

things as simple as possible for our lawyers to provide that excellent service to clients.

“It might be streamlining datadriven processes or providing more timely consolidated analytics, helping them to upskill commercially, but all of that helps enable us to make better decisions and enables the lawyers to focus on the needs of the client.”

DRIVING DIVERSITY IN TEAMS

Having built and led largescale teams across different businesses, Gareth has seen the benefits that a diverse team brings, “Diversity and inclusion is really at the heart of our responsible business strategy at Irwin Mitchell and I'm a big believer in how that brings different mindsets, different ways of working and different thought process into the business.”

There’s also a need for diversity in skill set, roles and mindset in the data team at Irwin Mitchell, “In a transformative environment you need domain experts –people who understand the nuances of different business areas. If you don't have that, the insight is maybe not going to be applicable or practical.”

In terms of what he looks for in individuals so they can thrive in this environment, Gareth explained, “You need people who are adaptable

and resilient. Soft skills are important as well, such as critical thinking and communication to distil quite complex information to a nontechnical audience.”

To attract talented team members, Gareth places importance on sharing the vision for data in the business and the wider sector, “In professional services it's going to be quite heavily disrupted by artificial intelligence over the next 10-year horizon. Professional services retain a huge amount of unstructured data which in-turn could be turned into an asset.

“How you develop your own proprietary large language models to extract that data to aid decision making is going to be key to this. That's quite interesting as a hook for data team recruitment into the legal sector, and professional services more generally.”

THE DUAL CHALLENGE OF DELIVERING ANALYTICS AND TRANSFORMATION

There’s a great deal of positive business transformation happening at Irwin Mitchell, but that’s not all Gareth needs to focus on. We wanted to understand how he and the team not only deliver for these transformation programmes, but also deliver analytics.

Having been in businesses where one set of data engineers supported analytics and another set were very busy working on transformation projects, Gareth is approaching

it slightly differently, “We've built out our data engineering team and they support on transformation projects as well as analytics.

“In addition to building a team, we've selected a data partner to take on board a lot of the data migration work. This means we can focus on building our new data platform, as an example.

“I think if you don't have these two things together, you struggle to pivot resources because they will get consumed on transformation. Whilst we have many of our data engineers focused on transformation at the minute, we have roles earmarked specifically for the build of a data platform and supporting some of the analytics initiatives as well.”

He notes that in transformation projects in any business, the data migration aspect can be challenging and underestimated, “Therefore it's either going to increase the cost of a project or you're going to pull more resources on it from, say, supporting analytics initiatives.”

THE IMPORTANCE OF ITERATING AND FEEDBACK LOOPS

To help ensure the projects the team is delivering bring tangible business benefits, Gareth emphasises an iterative approach to actionable insight, “If there’s a business requirement for something we build it in an agile, iterative

way, so that people see value and they can observe and feed into it. You don't want to just disappear into a dark room and then come out with a project at the end of it.”

He sees bringing in expertise from the business as valuable in making a data science project successful. Getting their thoughts through workshops in terms of what features might correlate with the outcome the team is trying to predict allows great ideas to be contributed, “Extracting that knowledge from business experts who aren’t in the data team is hugely important.”

Gareth commented that if you take business experts on the journey, it's also easier to implement and activate, “Building the model is the easy bit. It's the implementation and roll out that’s the challenge if you haven't brought people on the journey.”

He highlights feedback loops as important too, both in terms of getting consistent feedback to the data models themselves and working with the business teams to get feedback, “We track how people interact with the data products. We can be targeted and compare if one business unit uses it as much as another business unit. We also track how satisfied our internal clients are with the Data services we provide which enables us to course correct.

“Data has been slow to adopt a product mentality. If something is customer-facing

"The mantra is how do we just make things as simple as possible for our lawyers to provide that excellent service to clients"

“Extracting that knowledge from business EXPERTS who aren’t in the data team is hugely important”

on a website, you'd constantly monitor the performance of it and optimise it – and if users were not engaged with it, you’d remove it. It shouldn't be any different in the data world.

THE FUTURE FOR DATA IN PROFESSIONAL SERVICES

Even though there’s so much happening right now, it’s part of the job to be prepared for what’s next. Gareth has his eyes firmly on the future of data in the legal services sector specifically, and professional services more generally. The big opportunity he thinks is coming to the sector is digitisation.

Gareth talked us through this, “At Irwin Mitchell, we have a real breadth of service – family law, conveyancing, wills, personal injury, all types of litigation. What appealed to me, and remains an opportunity, is how you successfully land and integrate client relationship management.”

“That's endemic in retail, financial services and telco, but in those sectors, you've got the ability to use your data to cross sell and upsell, whereas this has to be approached more sensitively and appropriately in legal services. I do think there is a behavioural aspect to this as well. It’s not just about landing the technology and the data that underpins it, you have to create a change in mindset.”

He thinks data simplification of the technology estate in professional services is going to bring real benefits. “I think many professional services businesses are grappling with legacy and by simplifying the estate, it means that you're going to enhance data capture, simplify and improve data quality. That's a significant benefit.”

“Obviously, simplification of legacy tech stack is going to be beneficial on a number of levels, but a by-product will be better data. We're on that journey as we’re currently implementing Peppermint which will be our new case management system, reducing our legacy footprint of many case management systems.”

Another future focus is a need to move away from on-premise data centres, Gareth thinks. “You're not going to achieve all the aspirations we hear about today around artificial intelligence without a scalable cloud data platform. You can definitely deliver use cases on an older stack, but these are not scalable, and I think that's the opportunity.”

At Irwin Mitchell, they’re on a journey of building out a new cloud data platform that will replace their on-prem with a view to utilising their own proprietary data – with good data quality – to enact artificial intelligence use cases, as well as underpinning whatever types of analytics they want to conduct as a business.

As Gareth points out, “Organic growth is important. Data has got a massive role to play in getting the best bang for your buck when it comes to attracting and retaining clients, so I see that as a huge opportunity for our business, and probably most other law firms are in a similar space.”

There’s also work being done to match the right lawyers to the right work at the right time, “We're doing a lot in the resource planning space and that varies quite significantly in how you plan for family law versus personal injury, for example. It’s an exciting opportunity because, by getting that right, we know that we can influence utilisation, which will ultimately help enhance our P&L.”

And when it comes to AI, something we’ve touched on before is that opportunity with unstructured data in the legal sector. Gareth summarised the opportunity, “We hold so many documents like any other professional services business. There's a massive opportunity to develop your own proprietary large language models using that data, which will help to enrich decision making.

“There's a myriad of different types of use cases that I think are going to be applicable in the sector over the next 10 years, and that is hugely exciting.”

We couldn’t agree more.

Decentralising data ownership to enhance decision-making

Talking data leadership with Chris Wyard, Chief Data Officer at Beazley

With an emphasis on getting the data function close to its internal customers, Chris Wyard is leading a federated data product strategy at Beazley, the specialist insurance business that values being bold and striving for better.

Chris shared his approach to building and leading teams and creating stronger data literacy across the wider business. Plus, would it be a TechExec piece if we didn’t ask for a vision for the future? Read on to get his thoughts on what’s going to be big for the sector in coming years.

SHARING THE RESPONSIBILITY FOR BEING DATA-DRIVEN

When Chris stepped into the role at Beazley in July 2022, he brought with him a depth of experience from Allianz, where he spent 16 years rising from an IT graduate to Chief Data Officer.

And in the two years he’s been at Beazley, Chris has helped to establish a data office and enhance the role of data there. Key to this has been his focus on connecting data teams to the business, rather than seeing it as a centralised, separate entity.

Chris expanded on this when he told us, “The belief system I have is that we need to embed and leverage data as close to our stakeholders and

business processes as we can. Data can’t be an afterthought and should be available to help steer and drive our decisions. Now more than ever, there’s significant potential and opportunity if you can harness data capabilities effectively, unlocking significant potential across your business.”

While he’s used a range of methods to do this in the past, more recently he’s been designing it into the operating model, aligning domain teams for each data solution – or product, as Chris thinks of them – embedded into relevant business domains.

This data product strategy challenges the business to engage with data and ‘think data’.

Chris noted that this requires having the right guardrails and enablers around data governance, data quality and data management components, so there’s a level of cohesion about how these teams are working.

He shared, “We've been spending time in the last 12 to 18 months enhancing our data policy and our data management framework, implementing new solutions that will help us share and make data more available and accessible. All of this is creating ownership opportunities for data in the business.”

Chris Wyard

Chief Data Officer at Beazley

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Strategy

Delivery

Change

CREATING A POSITIVE DATA CULTURE

In any organisation, it’s not just about having the best team possible, it’s also about creating a positive culture for that team to grow and deliver within to the best of their ability. To support this at Beazley, they’ve been working on the psychological safety of the data team.

Chris shared, “What we've tried to do is remove that interpersonal risk about speaking up or disagreeing around the data, making sure we surface all the concerns that would exist so that we can deliver the best products and the best kind of service. So culturally, we're trying to make sure that the team has that environment both to interface with their stakeholders and then subsequently deliver into the business.”

The other thing which Chris considers compelling from a team perspective is helping them feel like they're working to common goals and objectives, “That alignment around activity and objective is quite key and we're seeing the benefits of that as we rebuild data products and share them with the business.”

IMPROVING OUTCOMES THROUGH DATA

With the data culture changing and the operating model embedding, Chris and his team have been focusing on four main themes where data can deliver value across the Beazley business.

First there’s productivity – what capacity can they unlock and provide back to the business through automation and AI to drive efficiencies and reuse of data, whilst helping decommission legacy environments.

Simplification and the level of efficiency that can be achieved as a result of leveraging the data foundations and capabilities that have been put into the data organisation is another focus.

As is maximising product coverage. Chris explains that in Beazley’s case, this is about contributing to business growth by enhancing their understanding of business outcomes by leveraging data and AI more effectively.

And last but importantly, derisking and making sure the control environment is well managed, ensuring Beazley is well-prepared to manage future opportunities and the development of regulatory requirements.

DATA TO DRIVE EFFICIENCIES

Chris took us through some of the work that’s gone into delivering on these themes. “We're making good progress around enhanced understanding of our business, with a strong focus around building new, improved datasets that we can reuse to drive analytics, insight and intelligence into our business and to ensure that we truly support the delivery of enhanced business outcomes from our data strategy.”

The approach to Mastering Customer data is being supported by a data management consultancy that delivers measurable results through a combination of expert consultancy and best-in-breed technology, which will provide consolidated data and intelligence across Beazley.

The Beazley team has also spent time building a new modern data architecture based on cloud where they’re publishing trusted reusable data products, “They’re more available with greater standardisation and more granularity than we've ever had in our business before” Chris explained.

“Our approach to data products requires us to focus on delivering data to our stakeholders that adds immediate value and more granular insights. It is critical that data products are designed to embed new insight into our business processes and that these are reused wherever possible. We also need to ensure that the data products are designed and adopted in way that ensures they quickly become the authoritative source of

Beazley data and definition in their respective domain.”

A global digital engineering partner is helping them build the data products and establish a factory approach so that they can increasingly deliver to the business need and iterate as requirements develop.

And a technology consultancy partner that specialises in building tailor-made mission critical platforms and that has in-depth expertise in financial services has developed the data platform with Beazley.

ENHANCED DATA CAPTURE TO DRIVE ADVANTAGE

With insurers having to carefully plan approaches to conditions that lead to claims, this is something that previously took a great deal of work in terms of optimising the approach to natural catastrophes and how they think about accumulation of risk.

Chris shared, “One of the most effective ways to optimise that process is by having enhanced data enrichment and leveraging unstructured data. I've done a lot of work in the past in General Insurance to move from postcode-level or zip code-level insights down to looking at the actual building and the use of satellite imagery to enhance our understanding of risk, and create new opportunities to drive competitive advantage about what we understand in terms of our portfolio and also its susceptibility to loss and claims events.”

LEVERAGING MACHINE LEARNING

Machine learning and AI have a big part to play in improving performance among other areas and the team at Beazley is using this in a number of ways.

Chris explained, “A broker will provide a submission to Beazley (typically a document or email) and we've been looking at how we take that unstructured submission, extract the data and reuse it into our systems and processes, so we are more efficient and reuse data effectively across all key parts of the insurance lifecycle.”

There's also opportunities around underwriting and claims, looking at patterns and trends, “What's important is anything that helps with speed. So how can we go from a whole host of unstructured data that enters our business being untapped, to ensuring we pull out the right information from these sources, the right nuggets of intelligence, and create decisionready outcomes for our teams? It’s about that fast time to market.”

Being market leading from a research perspective is also important – being able to look at insured assets and use data, machine learning or AI to drive out more intelligence that supports decisions and provide an edge. The team is looking at opportunities to structure data and insights from a diverse range of sources and provide an enhanced understanding of business performance and risk.

BUILDING DATA LITERACY ACROSS THE BUSINESS

Doing all this work requires a skilled team, but it also requires a shift in data culture within the business.

As well as a new data policy that provides guidelines and procedures about how the operating model, the culture around data products and federated ownership will change how data is collected, stored, processed, and managed across the business.

The business has also been investing in learning and development initiatives. A data literacy academy is due to launch in 2025 where colleagues can develop their understanding of data visualisation, data storytelling and data management principles. Chris tells us this is, “All with the aim of raising our internal capability, so that data’s not this thing that's happening to the business, but they're connected and part of that journey.”

They’ve also launched a new data library using a data catalogue partner that has a knowledge graph architecture to support with availability and accessibility of data. Chris explained, “One of the big challenges that needed to be overcome was we have lots of data, but at times it's availability or its accuracy or its provenance was not always fully understood.

“We now have a central repository that outlines all of our ownership and all our reporting dashboards. We've got hundreds and hundreds of users who have access to thousands and thousands of pieces of content.”

“We're constantly refining and refreshing that as an approach to make sure it reflects what's being used in the business and that it's relevant in terms of the content that we're sharing and, going forwards, we would like to evolve this to become more of an interaction and engagement hub for our data.”

That’s not all. They've also been looking at data lineage as a capability to provide a more holistic view about how data flows across the organisation. This is supporting them to pinpoint challenges or remediate any concerns that emerge in terms of their data processing and transformations across the organisation.

Chris explains, “Many of those capabilities are built out on a new platform. We're currently streaming enhanced data into our underwriting tools. Alongside the development of internal data, the use of unstructured sources, and third-party enrichment has also progressed.

“And we’re making sure that all of that data is catalogued so that in the areas of machine learning and AI we use the right data and have the right controls and governance around what data is being used across the business.

BUILDING A BALANCED TEAM

As you may have guessed, it’s not just the business-wide data culture that’s changing, but the data team too – both in terms of its footprint, its structure and its make-up.

Working on their operating model and a commitment to reflecting the global nature of Beazley’s

business has led to an expanded presence in the USA. This has given Chris the opportunity to do targeted recruitment to bring new expertise in-house, with successful new starters having curiosity, a product mindset and the ability to demonstrate that partnership and stakeholder engagement is part of their toolbox – which is important as the data team looks to federate capability and partner with business.

Chris explained that although their data team has historically been based in the London market for insurance, they want a data team that’s able to support territories that operate under different regulatory considerations and in different landscapes.

Chris noted, “In doing that, I think we've opened ourselves up to a broader talent pool. Of course, a focus on diversity and inclusion underpinned our recruitment efforts and we're pleased and delighted with the team that we've been able to create. The data team is balanced from a gender perspective, and we are representative in the other D&I characteristics.”

A FOCUS ON THE FUTURE OF DATA

With so much work underway at present, we wondered what Chris is keeping an eye on when it comes to the future of data and analytics withing the insurance industry.

Sharing data within the sector is something that he thinks needs to be a key focus. Chris stated, “I think there's been progress, but in an environment where we are often using similar technologies or similar cloud environments,

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my view is that there's still far too many frictional costs and what I call integration taxes for us to optimise our use of broader industry data and partnerships.”

“I think that there's an opportunity for us to leverage AI, modern platforms and capabilities to be more innovative with how we approach some of the opportunities in the market. I believe we can be collectively better about sharing data (where there is business opportunity and justification) with our partners and unlocking new opportunities.”

And as is to be expected when considering the future, AI emerges as a topic. “From a Beazley perspective, we see many opportunities afforded by AI. We have dedicated teams working to deliver on our AI objectives. Again, these come back to relatively simple themes of how do we increase the speed and accuracy of manual tasks and reduce the risk of errors?”

In the insurance sector, this could be harnessing AI to read, summarise and extract data from underwriting and claims documentation, as well as more common themes such as looking at using the potential for AI to improve decision-making and enrich the work and productivity of teams.

Chris commented, “Across the board in insurance, I think there's still too much manual toil. Everyone, I think, would benefit and would be more highly motivated and engaged by less friction in our day-to-day access to data and use of business systems.”

There’s also a focus to deploy AI responsibly and effectively, using key ethical principles such as fairness, transparency and human accountability

To support this, Beazley has a new, dedicated governance committees for AI that are representative of the business.

With all that Chris has covered, it’s clear that – for a team that’s already achieved a great deal in just two years – there’s plenty more on the horizon. It’s worth keeping an eye out for what they’ll accomplish in the next two years.

The

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Phil Bishop

Data and Analytics Manager at Fortescue Zero

“WE WORK ON HARD, IMPORTANT PROBLEMS – AND WE’RE GOING TO SOLVE THEM VERY FAST.”

From achieving a master’s in aerospace engineering to supporting motorsports teams that have won ten World Championships and two European Championships, Phil Bishop’s career so far has taken him from the nuts and bolts of engineering through to fronting a team of data specialists at Fortescue Zero, formerly known as Williams Advanced Engineering.

As part of the wider Fortescue group, Fortescue Zero are undertaking innovative work creating solutions to drive a zero emissions future for their business while also delivering nextgeneration, customer-facing products.

We caught up with Phil to talk about the similarities – if any – between using data to help drive battery performance in motorsport versus in autonomous heavy vehicles, the integration of data in the green energy transition and what he thinks the future of mobility and energy storage may look like.

ROUTE TO THE ROLE

Like many who’ve risen up the ranks to lead a data function, Phil’s journey to his current position was interestingly indirect.

After his studies, Phil went straight into aerodynamics engineering and then onto a role with Williams Advanced Engineering where his job was to do one important thing: make the racing car go fast.

Doing that required data – and lots of it. The small team Phil was part of were taking reams of data and trying to bring it together to compare to support them with making the best decision to improve the speed of the racing car.

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info@migso-pcubed.com

With Excel sheets flying around, the team realised they needed a data warehouse to consolidate the information and to make sure every hour that they spent working on the project counted towards their goal, rather than being wasted on admin. This is where Phil’s data journey began – through necessity.

Following the completion of these projects, Phil went onto a secondment in the design engineering team to help improve efficiency there by applying his learnings, resulting in a huge efficiency drive that freed up many people to focus on value add. With these successes in mind, Phil proactively paved the way to developing a more formal data function. In 2022, he went to his Chief Operating Officer with a business case and a plan, the gist of which was, as Phil explained,

“We can apply all of this great motorsport analysis and understanding of how to make decisions in high pressure scenarios and apply this across the wider business.”

leadership team said yes.

“Ever since then we’ve been working towards that goal of helping the business have the right information at the right time available for the right decision,” Phil told us.

A business team that does tech Under Phils’ leadership, the data team at Fortescue Zero has earned a reputation for taking on hard problems and helping business teams deliver. He shared his approach, “I very much want to always be in co-creation mode. As a data team we are a facilitator – we’re helping the business lead itself. We want to be everyone's preferred partner.

“We’re firmly a business team that does tech. The ambition is that we are living through the same challenges and opportunities as the business, and we want to be very close to feel that and help move them forward.”

With the data team having proved their value and embedded into the business, they’re now looking at bigger, more ambitious projects that work with the pivot of the business from consultancy to manufacturing products.

As Phil explained, “The business is changing and growing, so we’re moving and adapting fast. We work in very short time spans. We’re aiming to constantly deliver small pieces of value and drip feed that through the business because, if it took us six months to do something, the business would have moved on.”

When moving fast having access to flexible resources has been key to our success, that’s where MIGSO-PCUBED have helped. Providing business analysts and data specialists to accelerate the delivery of projects has enabled us to make improvements quickly where it matters.

The

PHIL

LEVERAGING DATA IN INNOVATION

Something there’s no shortage of at Fortescue Zero is innovation. Or data. Phil shared, “As engineers and as people who are developing products, we work very iteratively through a process of build, test, learn, evolve, so a great product doesn't happen the first time, it happens the thousandth time.

“Whether we recognise it explicitly or not, using data to make decisions day-onday is what we do in the business. From the delivery of engineering, to sourcing for our supply chain, to working with our logistics partners, to how we're going to manufacture the product, we're constantly seeking data and trying to make sense of it to drive the business. My role is to try to make that as seamless as possible.”

How does that work in practice, we wondered? Phil continued, “An example is how through our R&D process we’re learning from the simulations and tests. We're currently at the early phases of looking at how we can bring all that information together not just within projects, but how do we share it around and accelerate innovation as a whole. That then gives us the potential to do some more advanced things with analytics to develop products.”

“I think we've got a lot of use cases in the business for advanced analytics and AI, and they’re particularly championed in our motorsport industry team where those applications are really powerful.”

Their challenge is how do they then take that capability and use cases and scale that capability out across the business. Well, the team at Fortescue Zero already has already achieved this with Elysia® Battery Intelligence, which is a software battery management system. By combining their best-in-class AI, electrochemistry and real-world battery systems experience and applying advanced analytics, Elysia®

has been elevated into its own customer-facing product.

This means Elysia® can bring Fortescue Zero’s expertise, having operated at the forefront of electrification for over a decade, to every battery on the planet.

Phil shared what this product delivers, “The outcome of Elysia® is really powerful –what it means for batteries is that we can improve the life, increase their performance, and enhance their safety."

In May 2024, Fortescue Zero announced a new partnership with JLR (Jaguar Land Rover) to use Elysia® on all their electric vehicles, starting with the new Land Rover fleet, so their customers will soon be able to experience the power of Elysia® for themselves.

With the team having such a large range of projects they’re working on, being able to seamlessly move data from source to presentation so the correct data is in front of the right people is key, Phil told us.

Their orchestration and ingestion provider is Kleene.

Ai and, as Phil’s team is trying to create trust that information they share is up to date and be transparent when it's not, they’re finding that being able to add flags and filters to indicate this is supporting them to create a culture of trust.

Phil noted, “ This allows people to have more confidence, which allows us to become data-driven, so this technology is really key to that. If you can't use the technology to demonstrate that transparency, how do you get that trust otherwise?”

DATA IN THE SPECIALIST WORLD OF MOTORSPORT MANUFACTURING

Phil has mentioned how fast-moving the business is, but some of their clients are particularly fast moving too. As we referenced a little earlier, Williams Advanced Engineering is now called Fortescue Zero, but the connection of the previous Williams name to motorsport will be present in many minds.

In the highly competitive world of motorsport and the specialist manufacture that goes into making a car perform, data and analytics clearly have a huge part to play.

Phil and his team have worked hard on joining the value chain end-to-end, from engineering to supply chain to logistics to manufacturing. And in low-volume, high-performance manufacturing, everything goes back to engineering.

Phil explained how this works in practice. “Because everything we do is under quite aggressive timelines, we're always working from how long we can give our engineers to do the best possible job, and then how long do we need for the supply chain and the logistics, and then how long do we need on manufacture to make sure we deliver what we need to on time, to cost, to quality.”

To make those decisions, data and analytics are vital and valuable for providing clarity –and for making more confident decisions more accurately. It’s enabled the business to go from having around three people on an individual project spending time working this out, to having data and analytics embedded and happening in the ordinary way of working.

Again, co-creation is important here. The data team don’t go in with a polished solution, they go to the business teams with a rough prototype and work together to refine it.

Phil pointed out, “Data and analytics isn't the job of the data and analytics team, it’s the job of the business. We’re there to accelerate and facilitate, and where needed we lead and guide, but we don't own the data, we don't own the decisions.”

Currently, there’s also work being done with Fortescue’s quality team on proof of concept around whether they can understand if quality issues are present earlier. Phil summarised this for us, “Can we use advanced analytics and predictive modelling to take huge datasets and spot a trend you can’t see as a human, and then how accurate are we predicting that? This will save us money, save us time, and help make sure our products are the best they can be.”

FROM RACING CARS TO AUTONOMOUS TRUCKS

It’s not all about motorsport for Phil and his team. Parent company Fortescue is a mining operation which include rail, energy and mining. And that means the Fortescue Zero data function has to consider this too.

With a remit that’s business wide, how possible it is for the data team to transfer knowledge from vehicles as varied as a racing car to a mining truck? This may seem like a theoretical question, but for Fortescue Zero, it’s not.

First off, let’s get into a bit of background on the motorsport side of thigs. Williams Advanced Engineering had previously designed, tested and manufactured batteries to power the first electric single seater championship for Formula E, and Fortescue Zero is also manufacturing the Gen 3 Formula E battery. The Gen 1 battery provided 99.8% reliability out of 440 starts during the first four Formula E season, delivering 200kW peak power.

A key component in battery energy conversion and charging is DC-to-DC technology. Phil explained, “Our deep knowledge of this started off in motorsport for a niche, high-speed charging application. We’re now taking that same technology and applying it at a much larger scale to how we charge and

make sure energy is available to power this huge heavyindustry mining equipment.”

In this case the equipment is an autonomous battery electric haulage truck which pulls a 300-tonne payload mine vehicle that Fortescue is developing with Liebherr. The truck moves iron ore from Fortescue’s mine site to the train, where it will continue its journey.

Phil shared how data can act as a bridge between these two seemingly different batteries, “I think when we're talking about data, it's not really the ones and zeros – the numbers, the storage – it's ‘What is the information that helps us understand the opportunities and the challenges that we have?’

“If you take that lens rather than a technical lens, your perspective changes so that you start to view the electric racing car batteries we develop and the batteries we’re developing for a 300-tonne electric mining truck in a very similar way.

“They're both extremely high-performance batteries for their application. How they use information to balance trade-offs and decisions based on power or mass is on a spectrum, and that's where the learnings come in.

“We can get those comparisons up and we can then start to understand how similar or how different they are. So, at that

level, that’s where that bridge is made because you can talk about those things in the same breath.”

Phil pointed out, “You can only do that when you look at the spectrum of the challenges and the opportunities on that parity. Otherwise, you’d think there’s no crossover here’.

While there’s no magic formula to applying data across sectors, having a data literate workforce such as the one at Fortescue helps to join things up.

USING DATA TO CUT CARBON

This joining together of different business areas is becoming even more important as Fortescue is on a journey towards achieve carbon zero in the not-too-distant future: by 2030.

Fortescue has an action plan for turning their science-based target into industrial delivery – and it’s already underway. The business is going to stop burning fossil fuels across all their operations, including their Australian iron ore

operation, without voluntary carbon offsets and without carbon capture and storage. They’re calling this Real Zero.

We wanted to know how this would work in practice. Phil shared, “Almost all of our group’s emissions come from our mining operations. So first, our focus is on using data to support development of carbon-zero products such as mining vehicles, dozers, graders and a battery electric locomotive to take iron ore from the mine site to port, plus hydrogen conversion on ships. This is where the most significant contributions are going to be felt.

“To give you an idea of impact, delivering our products will save 700 million litres of diesel per year.”

So, what role is data playing in this? “We’re very much tying in with our cross functional teams who are responsible for delivering these products to really be able to help them on this journey,” Phil answered.

“Data is really important in this, helping to make sure we have a common understanding

helps ensure we don't get caught in decision paralysis due to conflicting information and that we know enough to act.”

“A big part of the culture here is that we work on hard, important problems and we’re going to solve them very fast it. It’s maybe not your typical 9:00 to 5:00”

A VISION FOR THE FUTURE

While looking to what’s next, we wanted to get Phil’s take on what the future of mobility and energy storage might look like.

Phil was excited to reflect on this. “I think this is a really pivotal time for mobility and energy storage. So far, the focus has really been on hardware and infrastructure but those problems those things are now starting to be solved, but the challenge then becomes different.

“Having these energy storage and mobility systems based on fossil fuels compared to clean energy is fundamentally different because with fossil fuels, you can quite quickly increase and decrease capacity to match demand, whereas that’s much more difficult with clean or green energy.”

“The role of data analytics in this space is huge because you're now looking over an ecosystem in a very different way, and you're starting to see things connected which wouldn't traditionally be.”

“To give you an idea of impact, delivering our products will save 700 million litres of diesel per year.”

Phil suggested that in future, green energy generation, storage and distribution may not be discrete pieces of a puzzle but something to be considered together. And that key factors such as how to move energy around, ensure availability and be clever with energy storage are almost too big to comprehend without considering a more analyticsbased approach.

He illustrated this point with something very pertinent to Fortescue: mine sites. “If you imagine a mine site, they might have their own solar farms, wind turbines, and hydrogen power plants and their own storage. However, mine sites are 24-hour operations, meaning the autonomous trucks are going to be working all the time, so they’ll need to charge and go. Ensuring that we've got the energy available in this ecosystem in the right place at the right time is key to making sure these can continue to deliver.”

“I think that will also transition to general life as we move to a greener grid and infrastructure. For example, you're going to want to charge a car battery overnight so you can go to work in the morning, but there's not going to be any solar energy at night, and wind energy is responsive to weather conditions to a large part, so how do you make sure you can manage this in an effective way?

“You can't consider these things in isolation, and I think it will lead to some really exciting innovation over the next 20 years.

“In the mobility space, you might see a company shift to fundamentally different way of considering mobility. We have a really great opportunity using technology to do it in a responsible, sustainable effective way and I think it's going to be exciting.”

So, what tech does Phil envision will be the key enables for this change? “I think big data, data sharing, machine learning and AI are going to enable decarbonising in an effective way while maintaining quality of life. Without them, such things would not be a possibility.”

“And I think for that vision to work, how we share and democratise or monetise data is going to be a really interesting evolution. If businesses stay in the traditional format of today, I think you will be looking at really close collaboration between different industries and different functions.”

With Phil leading Fortescue Zero’s data team and the innovative products they’re helping to deliver, we can’t wait to see how their business continues to evolve. It’s one to watch.

Cameron Bergen Chief Executive Officer, mode40

DIGITAL TRANSFORMATION AND INDUSTRY 4.0 VISION

mode40's Approach to Digital Transformation: mode40 describes itself as a full-service digital transformation company. How would you define your approach to transformation, and what do you think sets mode40 apart in this highly competitive space?

mode40 was founded on the belief that businesses should have the ability to trace their data effortlessly, from the moment it’s created at its source to the point where it delivers the outcomes needed to operate more effectively. Simply put, we’re here to help businesses turn data into profit.

This idea was born, like many great ideas, over a beer and two decades of looking for organizations which could support the needs we had. Those two decades were lived on the other side of the table in a sector leading multi-national F&B company’s IT, engineering, production planning and OT teams. The struggle was every management consultant wanted to sell a PowerPoint that did not really say anything, every integrator could not get past the PLC and the software development companies had never been on the shop floor.

We decided to redesign this space to provide full-service support tailored to organizations' digital transformation needs. Our approach tracks data from its origin through

every relevant area of your organization, ensuring the insights it reveals are fully realized. Having the control through the entire data journey process ensures that what we identify as an opportunity can actually be realized.

Think of it like the NASA moon landing. While all the disciplines existed, in their silos of specialty they could not achieve success. The achievement wasn’t the work of one team or a single piece of technology— it was the result of countless disciplines working in perfect unison. The engineers built the rocket, the scientists calculated the trajectory, the technicians ensured every system functioned flawlessly, and mission control orchestrated it all. No single piece could have succeeded alone, but together, they achieved the impossible.

At mode40, we approach digital transformation with the same mindset. We combine the precision of engineering, the innovation of software development, and the practicality of shop-floor experience into a mission-focused effort. By controlling the entire data journey— much like NASA controlled every stage of the Apollo mission—we ensure the final outcome aligns with the vision.

KEY TO SUCCESSFUL INDUSTRY 4.0 INITIATIVES:

What are the foundational elements for a successful Industry 4.0 initiative? Where do companies often fall short, and how does mode40 address these gaps?

For decades the question was ‘What technology should I buy’ as we had such limitations in rigid uses of what technology could offer. That traditional approach for off the shelf solutions fail to take into account the intricacies of each plant floor. The foundational elements must include; Clear Objectives, Data-Driven Culture, Scalable an Interoperable Technology, Workforce Empowerment and Continuous Improvement.

Many organizations struggle to reach their goals due to common pitfalls: unclear objectives, siloed IT/OT operations, undervaluing the manufacturing engineer role, and an overreliance on technology as a solution. They often neglect the importance of system integration,

robust project management, and confronting the realities revealed by performance data once surfaced. Addressing these challenges is essential for driving meaningful transformation and achieving sustainable success long term.

This isn’t a “Next, Next, Finish” world anymore. The days of plugand-play technology installations are long gone—buried with outdated mindsets. It’s time to leave the excuses behind and embrace a more thoughtful, dynamic approach.

How do we address these gaps? Our Augment and Accelerate approach is about assessing where the strengths of your team are (from leadership through execution and project management) and augmenting them in the areas where you lack the strengths.

There are 3 questions which organizations are constantly asking themselves. “Why did we fail, I thought we knew what we needed”, “ know where we want to be but we don’t know how to get

there sustainably”, and “We have no idea where to start and what is possible”.

In acceleration we use a series of approaches and products which are off the shelf available but customizable to adapt to the organizations specific needs. Products should conform to the environments they are used in as opposed to forcing the environment to conform to the product.

One of our most popular products is our Corporate Playbook. This is a framework we developed which defines the technology strategy for companies over the next 3-5 years. It is built up from the real business cases and incremental steps each technology and change element that is required to realize the gains of the organizations investment. When organizations get tired of having meetings over years to keep talking about where to start and how to define what needs to get done, insert the 30 day framework to answers.

PEOPLE, PROCESSES, AND TECHNOLOGY ALIGNMENT:

You emphasize aligning people, processes, and technology for maximum profit. Can you share some effective strategies you’ve developed to maintain this alignment throughout a digital transformation project?

There is no secret sauce in engaging team members at all levels. As you trace a piece of data that may start with a PLC, move to an HMI, transferred to the SCADA system, augmented by the MES and end up on some executive dashboard, there are many players who have a stake in what is needed. Common belief is ‘it takes to much time or costs too much’ to engage people, reality is it costs too much not to. Fail to adopt is the biggest reason projects fail, the reason adoptions fail is the lack of awareness, desire and knowledge of what the objective and “What’s in it for me” are not communicated and understood from top to bottom of the companies people pyramid.

More uniquely at mode40 we engage clients using the same techniques to building complex software. Taking the strongest attributes of the Business Analyst, Lean Manufacturing and Engineering skill set to engage customers and properly define the outcomes that any approach needs to achieve. Once this is defined the alignment of the technology can be properly assessed and implemented.

ENGINEERING & TECHNOLOGY INTEGRATION

Optimizing Existing Assets:

Many companies constantly face pressure to adopt new technology, but Mode40 focuses on maximizing current assets first. What are some examples of this approach delivering measurable value?

I wish I had 2 days to work through this, but let me focus on a few of the areas where we see some of the biggest bang for the buck.

THROUGHPUT

We believe in maximizing the value of a company’s current assets before rushing to adopt new technology. Too often, organizations feel pressured to chase the latest trends, overlooking the untapped potential in their existing systems. By refining workflows, enhancing data utilization, integrating IT and OT systems, and extending the lifespan of equipment, we’ve helped clients achieve significant improvements in efficiency and profitability without the need for costly overhauls. Our approach not only delivers measurable results but also ensures a stronger foundation for future innovations.

But let’s talk specifics; Biggest bang for your buck areas of improvement?

Production Scheduling and sequencing, most commonly left up to an internal expert to apply a best approach to trying to blend operational challenges and sales demand. What can be realized given real world implementations? 92%

reduction in manual processes, 7% increase in facility throughput, compliance to orders grew more than 20% to a company record of >98%, 7 locations converted to centralized management.

Throughput is common choked by support process or micro-stoppages what would 21% improvement in line throughput mean to your organization

“Our team spends a lot of time trying to find their information”. What does a AI based implementation do? Reduce overall response time by 80%, improved front line training by 30%, elimination of more than 60 manual processes, deliver multilingual support, improving moral

One of our favorite example is to digitize the compliance data, compliance data has no value right? How about an improvement in 43% margin? Through a digitization process our analysis provided the sales team of large F&B the data to create a new value added product which was being classed as a secondary product. This new market allowed for substantial improvement in the valuation of the existing product stream.

ENGINEERING MEETS TECHNOLOGY:

mode40 merges engineering expertise with advanced technology. How does this blend improve outcomes for your clients in manufacturing and related sectors?

The trinity bridges the best of engineering, software development and project management and then

applying it into a rapid deployment framework. This approach reduces the common risks associated with traditional automation and consulting projects. Let’s examine a recent use case. A brand new, latest build by an OEM was designed to produce 100 units per minute. Installed, tested and validated to operate at 100 units per minute. Success! Result? Missed production targets more than 70% of the time. Integrators validated the specifications and machine performances. How can this be? The production process relied upon quick product changeovers, optimized CIP process and proper scheduling. OEM delivered on the specifications but the related production data which relied on an undefined set of processes turned into the old ‘garbage in garbage out’ scenario. So then what can be done? Take a 30 day approach to; Map the process dependencies, apply lean manufacturing tool kit to eliminate the waste, update the PLC data to map to the new processes, add additional data points to measure all stoppages and slow down events, implement SKU level tracking then integrate with the MES and retrain the team. Outcome: Elimination of micro-stops previously not measured as well as a reduction in overall CIP process time but most importantly getting to see a thumbs up from the team main operator having achieved his daily targets for the first time.

ACCELERATING TIME-TOVALUE:

You promise an accelerated time-tovalue on transformation projects. What processes or methodologies do you follow to ensure projects stay efficient while still delivering quality results?

Accelerating time-to-value is all about precision, agility, and seamless execution. We start with a deep discovery phase, mapping out bottlenecks and setting clear goals to avoid wasted time in execution. Our phased implementation strategy delivers measurable wins early, while iterative development ensures constant refinement and adaptability. With a team of broad experienced experts, we align technology, processes, and people for smooth integration, all while leveraging realtime feedback loops to pivot quickly when needed. By grounding every decision in data and focusing on actionable outcomes, we deliver results in weeks, not months, without compromising quality. Speed isn’t just about working faster—it’s about working smarter to drive immediate, impactful ROI.

WORKFORCE ENGAGEMENT AND CHANGE MANAGEMENT:

ENGAGING THE WORKFORCE IN TRANSFORMATION:

Transformation can be challenging for employees. How does mode40 foster workforce engagement and readiness, especially when implementing advanced technology tools?

We love this question. We cannot stress enough the importance of team member engagement. It is why we are demanding in our deployment approach. This approach brings a beginning to end approach on engagement of stakeholders. Engaging early and often with a documented list of the needs, concerns and risks. We execute this on two fronts. First, is a classroom setting where we can work through brainstorming exercising to help get ideas flowing and second is by engaging with people during the work where we can combine our own observations to build on and augment the teams’ contributions. This helps shape the end stage where this curated list of needs becomes a part of answering “Why” are we changing. When organizations allow the teams input to be acknowledge in this way then the adoption

of change, especially the adoption from the ‘Change Champions’ who are our first adopters and ambassadors, can have its most effect. Team member engagement cannot be compromised.

This approach brings a very practical and real human to human level of empowerment. It goes a long way over the obligatory pizza party, but pizza helps too.

CHANGE MANAGEMENT IN DIGITAL TRANSFORMATION:

What’s your perspective on effective change management in Industry 4.0 projects? Are there best practices mode40 applies to ensure smooth transitions and adoption?

At mode40, we see change management as the backbone of successful digital transformation, especially in Industry 4.0 projects. Technology can only take you so far; the true transformation happens when people and processes adapt seamlessly. Our approach focuses on aligning technology with

human behavior, creating a culture of adaptability, and implementing changes in a structured, inclusive manner. How we make the work? Lead with clear communication, stakeholder involvement from day one, phased implementation for gradual adoption but demand early wins and hands on training and support.

Sounds like an add for a course in project management, that’s because we don’t see anything revolutionary in what we do but more in the people who are doing it. Our team is comprised exclusively of experts each in their areas of discipline and with an average of more than a decade of experience the application of those experiences combined with skills delivers on the quality you would expect.

VALUE CREATION AND MEASURING SUCCESS

Unlocking Profit

in Existing

Processes: mode40 is focused on uncovering profit within current systems before recommending new investments. Can you walk us through how you identify these profit opportunities and some ways you help clients capitalize on them?

Data first, Decisions second. We begin by digging into the data— whether it comes from machines, people, or the overall process— and taking a holistic look at the decisions that impact performance. This approach allows us to map out gaps in data collection, analysis, and decision-making. By asking simple but powerful questions like, “What decisions are being made reactively that could instead be proactive?” we uncover significant opportunities for improvement.

Photo: The National Research Council of Canada’s advance manufacturing research facility in Winnipeg

Where are these opportunities typically buried? About 70% are likely hiding in spreadsheets, 20% are entrenched in tribal knowledge, and the remaining balance resides in processes that are tucked away in overlooked corners of your operations.

To press the point further, every one of our Fortune 500 clients— yes, 10 out of 10—has invested the equivalent of a small country’s GDP in their favorite ERP systems, only to find they still lack the capability to generate optimized scheduling for their facilities. Enter the infamous spreadsheet: a clear reminder that despite massive investments in technology, key opportunities for efficiency and optimization remain untapped. Those gridlines? They’re not just cells—they’re opportunities waiting to be realized.

MEASURING THE IMPACT OF TRANSFORMATION:

What metrics or KPIs do you prioritize to assess the success of digital transformation initiatives? How do you track both the immediate and long-term value created?

We focus on metrics that show real, measurable impact on the floor—things like production throughput, reducing downtime, improving OEE, and cutting costs per unit. Right out of the gate, we look for quick wins, like streamlining processes or cutting out manual tasks, to show immediate value. Long-term, it’s all about keeping costs down, getting the most out of the equipment, and driving better decisions with accurate

data. We keep a close eye on both the day-to-day and the big picture to make sure these changes stick and keep delivering results.

THE FUTURE OF DIGITAL TRANSFORMATION

Future

Trends in Industry

4.0: What trends in digital transformation and Industry 4.0 are you most excited about? How is mode40 preparing to stay at the forefront of these changes?

Here’s a little secret sauce: AI is here to change the game. After hearing that same promise for the past 25 years, I can finally admit—it’s happening. The lofty predictions of the past are becoming today’s reality.

That said, the fully automated "Jetsons factory" isn’t here yet.

No matter how impressive humanoid robots might be at mixing a perfect Manhattan, we’re still far from that level of automation. However, in three key areas, AI is already delivering on its promise. First, it empowers decision-making by putting highquality, actionable information at everyone’s fingertips, enabling real-time data-driven insights. Second, it simplifies integration with platforms like the Inductive Ignition Platform, which allow for scalable historization, augmentation, and optimization of data, creating seamless workflows. Third, it’s driving a convergence of technologies, breaking down barriers at an exponential rate. This fusion of metaverse simulation and operation is poised to redefine the manufacturing space in ways we’ve never seen.

Looking ahead, the manufacturing landscape is entering a datacentric frontier of unparalleled transformation and universal competition. As these technologies converge, they’ll push the boundaries of what’s possible, forcing a redefinition of the industry’s value proposition. It’s not just evolution—it’s revolution.

LONG-TERM VISION FOR mode 40:

Finally, where do you see mode40 in the next five years? What legacy would you like to create for the company in the field of digital transformation?

mode40’s vision is bold: delivering $1 billion annually in value to the manufacturing industry. To make that happen, we’ve teamed up with the National Research Council of Canada’s advanced manufacturing research facility in Winnipeg.

Think of it as the Stark Industries of manufacturing innovation— bringing together brilliant minds and cutting-edge tech in a stateof-the-art playground where we test, deploy, and fine-tune our breakthroughs.

And here’s the kicker: in 2025, we’re rolling out a new series of products aimed squarely at hitting that billion-dollar mark. Mark your calendars for March 2025—we’ve got announcements that will assist in setting the bar. This "defining year" is more than a milestone; it’s proof of our commitment to revolutionizing digital transformation. mode40 isn’t just setting the bar—we’re redefining how high it goes. Stay tuned. It’s going to be epic.

Franck Carassus

CSO & Founder, Opendatasoft

Franck Carassus is Co-Founder and Chief Sales Officer (CSO) at Opendatasoft. In his role he drives global sales with a focus on the U.S. Since 2011, he has supported 100+ organizations in data sharing across sectors including government, utilities, and telecoms. With 20+ years of experience, Franck has led sales teams and partnerships at HP, Symantec, Novell, and Dassault Systèmes.

Q&A

Driving data consumption at scale

1. WHY IS DATA SHARING NOW SO IMPORTANT TO ORGANIZATIONS?

We live in a digital world, surrounded by increasing volumes of data. To succeed, every organization now needs to be digital, using technology to be more agile, innovative, and customer-centric. Data is the lifeblood of digital businesses, which means that harnessing data sharing and exchange at scale is central to creating value through increased collaboration, innovation, better decisionmaking, and greater efficiency.

Businesses have always used data, but previously its use has been restricted to expert data analysts, who were responsible for creating specific reports and dashboards for senior management, either on a regular basis (such as quarterly sales reports), or in response to particular requests. This meant that data sharing was very much on a one-to-one basis, making it resourceintensive and difficult to scale. Now, being able to share data through a one-to-many approach is critical - and has not yet been fully considered by many organizations at a strategic or tactical level.

Essentially, in a world where everyone’s job and life revolves around information, data needs to be democratized and made available to all, whether internally within organizations, across ecosystems or with all audiences through open data.

2. DATA DEMOCRATIZATION SOUNDS LIKE AN OBVIOUS STEP, SO WHY AREN’T ALL ORGANIZATIONS DOING IT?

Providing access for everyone to the data they need is a simple concept - but can be very difficult to deliver in reality.

For a start, many organizations may not even have a record of all of their data, particularly if it is stored in different departments and multiple systems. Cataloging all of these data sources is time-consuming and involves everyone working together to break down departmental silos. And, of course, the volume and range of data is growing rapidly, with new sources and types continually being added.

Secondly, the company’s technology infrastructure is often not designed for

large-scale data sharing. It is designed to move data between particular systems, but doesn’t enable data to flow around the wider organization easily or make it available in formats that non-specialists can easily understand and work with.

Finally, most employees are not used to working with data on a day-to-day basis, meaning they don’t necessarily have the mindset to see the benefits of data democratization to their roles. They can feel that they don’t have the skills to harness data or trust that the data they access is accurate or reliable, particularly if it comes from other parts of the business.

3. WHEN IT COMES TO TECHNOLOGY, COMPANIES HAVE SPENT CONSIDERABLE SUMS ON SOLUTIONS - WHY HAVEN’T THEY SOLVED THE DATA SHARING PROBLEM?

As data volumes have increased, organizations have invested heavily in a whole range of tools, such as data lakes, data catalogs and data integration and business intelligence solutions.

However, while these allow data to be managed, they don’t deliver democratization, as they are simply not targeted at employees and businesses. Many tools are solely usable by experts, meaning they don’t support data sharing at scale. For example, data catalogs provide an inventory of a company’s data that is useful to technical administrators but difficult to understand by employees and managers. And, like an old-style library catalog, they just list the details of the data, without providing a direct link so that people can access and explore it.

What is missing is the “last mile” connectivity - a solution that puts the right data into the right people’s hands so they can confidently consume it through self-service. This centralized data marketplace has to connect to all data assets across the organization and make them available in the right formats for all audiences - such as through interactive dashboards for business users and APIs for technical specialists. Without a data marketplace, seamlessly sharing data at scale is impossible.

4. HOW DO YOU HELP TO MAKE DATA CONSUMPTION EASY?

At Opendatasoft we help organizations around the world in multiple sectors to build internal data marketplaces that enable them to share their data through self-service with their employees and other

stakeholders. This means that everyone has access to the data they need, through an intuitive experience that makes the whole process simple and straightforward, while keeping the data itself secure.

Our data marketplace solution scales the use of data, and enables organizations to turn it into a valuable business asset. As part of this, our solution helps companies to industrialize the creation of standalone data products to ensure that they can be quickly built, shared across the organization, and maintained on an ongoing basis.

We’ve spent over 13 years working with data sharingand before that myself and my co-founders worked on some of the first data portals in the world, so we understand both the benefits and the challenges around industrializing data sharing at scale.

5. BUILDING A DATA MARKETPLACE IS ONE STEP, BUT HOW DO YOU GET PEOPLE TO USE IT?

That’s a very good pointyou cannot make the mistake of simply building a data marketplace and assuming people will adopt it. The user experience is vital - if you don’t make it easy to use your data marketplace then people will ignore it, meaning you get no benefits from your investment in data sharing.

Everyone today is a sophisticated technology user, and they expect the same type of fluid experience provided by e-commerce marketplaces from the online business applications that they use.

All of this means adopting the same consumer focus for your data marketplace, centered around the user and their needs. To start, the interface has to be intuitive and inviting, without requiring extensive training, and it has to be easy for users to discover data through powerful search. Data should be available in different formats, such as maps and dashboards, to make it easily consumable. And, just like an e-commerce marketplace, users should have the chance to interact with data owners, asking them questions about data assets, and leaving feedback and ratings. All of this will help build trust in data sharing and encourage people to use your data marketplace on an ongoing basis.

6. CAN AI HELP?

Absolutely, AI can help across the board by acting as a copilot to help people get to the data they require in a much faster way. This begins with AI-powered semantic search that understands the intent of the query, rather than looking for specific, rigid keywords, and therefore provides much more accurate results, even if queries have been misspelled or are in a different language. AI-powered search can automatically suggest other, similar data assets, broadening access for users and giving them a more comprehensive view of the data that is available.

AI also makes it easier for users to visualize data in ways that best fit their needs. For example, in our solution, you can just ask a question when on a specific dataset and receive an automatically generated visualization that displays data in an intuitive, understandable way - such as a map, figure or graph. So, rather than being served raw data, if you asked for a comparison of sales across territories, you’d be provided with a graph or map clearly showing the information in a visual, understandable way.

7. DATA CAN BE SENSITIVE OR CONFIDENTIALHOW DOES PROTECTING IT AND MEETING REGULATIONS FIT WITH SHARING IT THROUGH A DATA MARKETPLACE?

Protecting data is vital. However, often concerns about regulatory compliance can hold back data sharing - these can be overcome with the right technology and processes in place. Essentially, it is about ensuring two things. First, you need to anonymize data to remove any personally identifiable information to safeguard privacy and meet regulations such as the GDPR and CCPA. Then, it is about controlling access, so that no-one sees data or even specific fields that they aren’t authorized to view. For example, accounts staff clearly need to be able to access the financial details of customers in order to do their jobs. Marketing teams could have access to the same data, but be blocked from viewing sensitive information that is not relevant to their work.

All of this requires robust access management and governance capabilities in your data marketplace, with administrators able to easily manage user access rights and permissions at a granular level. They need to be able to grant access to individual users or larger user groups in line with wider policies, safeguarding data and ensuring compliance.

8. YOU MENTIONED THE IMPORTANCE OF BUILDING A DATA CULTURE. HOW DO YOU ACHIEVE THAT?

A lot of people are scared when you mention data, and see it as something technical and beyond their understanding. In fact, we all happily use data in our daily lives - it's just that we don’t always think of it in those terms.

So, building a data culture is an education process, breaking down any preconceptions and overcoming their concerns. This involves training to a certain extent, but the best approach is to encourage people to experiment and build their confidence by accessing and using data themselves.

Easy to use data marketplaces are a key part of this, as they make it simple for users to find reliable data, create visualizations, and see how it helps them in their jobs.

For example, at the Town of Cary in North Carolina, the planning department now uses census data collected and made available via its data marketplace when creating reports and plans. Previously teams had to search for this data externally, meaning they now save time while guaranteeing accuracy. Data has a huge range of uses - one way to encourage innovation is to share examples of how people have benefited from data on your marketplace to drive new ideas.

9. WHAT SORT OF BENEFITS DO DATA MARKETPLACES DELIVER?

Data marketplaces turn data into business value by providing seamless access to information for all. In our experience this ROI breaks down into five areas:

• Better decision-making: Greater access to reliable, up-to-date information and insights delivers more informed, accurate decisions, at a tactical and strategic level.

• Greater efficiency: Having access to a centralized source of data helps to automate processes and removes the need for employees to manually look for and check data - or request it from data analysts. This increases efficiency and lets employees focus on their core roles.

• Innovation and new services: The availability of data is a spur to innovation and collaboration. Employees can use it to work in new ways and to create services that can be provided or sold to clients. For example, Internet of Things (IoT) sensor company Birdz by Veolia has used its data marketplace to automate the creation of new personalized monitoring dashboards for its clients.

• Cost reduction: In large organizations where data is not managed centrally there can be significant overlap between business units collecting similar data or buying the same datasets from external providers. Having a central source of truth reduces costs associated with creating or buying duplicate data.

10. GIVEN HOW MUCH DATA ORGANIZATIONS HAVE, CREATING A DATA MARKETPLACE MUST BE A TIME-CONSUMING PROCESS. HOW LONG DOES IT TAKE?

The good news is that by working with an experienced partner a data marketplace can be set up in a matter of weeks or months. Much of this time should be focused on understanding user needs and creating an intuitive experience that delivers an easy to use portal.

In terms of data, clearly adding every data asset you own to your data marketplace immediately is impossible. So start with those high value datasets that will be used most for specific use cases and will generate quick ROI. Often, this data is already being shared on a peer-to-peer basis, so requires less work to prepare and validate, and will drive usage and acceptance of your data marketplace. Build on this, and create a pipeline of new data to add to the marketplace, based on user requests and business needs, so that it grows over time. Adding data and visualizations is an ongoing journey, but the critical point is to get started and begin the process of transforming your data into value.

VIDEO GAME TECHNOLOGIES THAT CHANGED THE WORLD

Top 10 Video games have always been more than just entertainment; they have been breeding grounds for some of the most innovative technologies that have transformed multiple industries, from healthcare to entertainment and beyond.

Below, we dive into the Top 10 video game technologies that not only reshaped gaming but also influenced the broader technological landscape.

MOTIONSENSING TECHNOLOGY (WII REMOTE)

First introduced by Nintendo in 2006 with the Wii console, motion-sensing technology changed the way players interacted with games. The Wii Remote (Wiimote) featured an accelerometer and infrared sensor, allowing for motion-based gameplay. This technology paved the way for subsequent devices like the PlayStation Move and Microsoft Kinect, but its influence stretched far beyond gaming. Fitness trackers and smart home devices began incorporating motion sensors for interactive functions, and the concept of "gesture-based control" became a staple in virtual reality (VR) experiences.

MOTION CAPTURE (MOCAP)

MoCap technology has its roots in video game development, initially used for creating more realistic character animations. Popularized by games like LA Noire (2011) and The Last of Us (2013), MoCap captures human movement and translates it into digital character animations. While initially intended for games, the technology quickly made its way into film and television, helping to bring characters like Gollum in The Lord of the Rings and Caesar in Planet of the Apes to life. Today, it is foundational in digital entertainment, from gaming to film production and virtual influencers.

CLOUD

GAMING

Cloud gaming has revolutionised the way we access and play games, offering a streaming model where the game runs on remote servers and is streamed to players' devices. Google Stadia, NVIDIA GeForce Now, and Xbox Cloud Gaming have taken gaming beyond the need for expensive hardware. This has also impacted business software and remote work solutions, where cloud-based apps and tools have become increasingly ubiquitous, thanks to this gaming-driven infrastructure. By making high-performance games accessible from almost any device, cloud gaming is a blueprint for the future of entertainment and beyond.

ARTIFICIAL INTELLIGENCE

(AI) FOR NPC BEHAVIOR

AI has been an integral part of video game development for decades. Initially, Non-Player Characters (NPCs) had basic scripted movements, but as gaming technology advanced, AI allowed for more sophisticated behaviours. The dynamic AI systems in games like Halo and The Elder Scrolls series enable NPCs to react to player actions in unpredictable ways. These AI advances have since spilled over into industries like robotics, autonomous vehicles, and customer service, where adaptive AI is used for real-time decision-making and problem-solving.

VIRTUAL REALITY (VR) AND AUGMENTED REALITY (AR)

Born out of gaming, virtual reality (VR) and augmented reality (AR) are now at the forefront of multiple industries.

The immersive experience offered by VR headsets such as the Oculus Rift (now Meta Quest) and PlayStation VR began with video games but has since found applications in fields like healthcare (for pain management and surgery simulation), education, and training simulations. AR, popularised by games like Pokémon GO, has since transformed how we engage with digital content in the real world, becoming a key tool in retail, marketing, and design.

HAPTIC FEEDBACK

Haptic technology allows users to receive tactile feedback through touch, enhancing the gaming experience. From vibration motors in controllers to advanced haptic suits in VR, these technologies have evolved from their early use in video games.

The PlayStation 5’s DualSense controller features advanced haptic feedback and adaptive triggers, providing a more immersive tactile experience.

The technology is now being applied in surgical training, prosthetics, and consumer electronics like smartphones and wearables.

3D GRAPHICS AND

REAL-TIME RENDERING

The shift to 3D graphics in gaming was a technological leap that changed not only the gaming industry but also sectors like film production and architecture. Games like Quake (1996) and The Witcher 3 (2015) pushed boundaries in graphical fidelity. Real-time rendering technology is now integral to filmmaking (for visual effects), architecture (for interactive building models), and medical imaging (for 3D body scans). The Unreal Engine and Unity have made these advancements more accessible to creators, leading to a boom in interactive 3D media.

RAY TRACING TECHNOLOGY

Ray tracing simulates how light interacts with objects in a 3D environment, producing hyper-realistic lighting, shadows, and reflections. Initially used in CGI films, the technology has now entered the gaming world with graphics cards like NVIDIA's RTX series. Ray tracing is revolutionising gaming graphics by offering lifelike visuals, and it’s making its way into other sectors like virtual product design and augmented reality for more realistic digital environments.

BLOCKCHAIN AND NFTS IN GAMING

Blockchain technology has introduced a new frontier in digital ownership, particularly through NFTs (Non-Fungible Tokens), where in-game items like skins, weapons, and even land can be owned and traded securely on the blockchain. This innovation in gaming has sparked interest in industries like art and collectables, where NFTs are being used to authenticate and trade unique items. As gaming becomes more integrated with blockchain, we could see a future where digital ownership becomes standard in other industries as well.

ADAPTIVE CONTROLLERS FOR ACCESSIBILITY

Video games have led the way in inclusive technology with the development of adaptive controllers for players with disabilities. The Xbox Adaptive Controller, launched in 2018, offers customisable inputs to allow players with limited mobility to enjoy gaming on their terms. This technology has influenced the development of adaptive devices in fields like assistive technology for healthcare, including tools for mobility aids and voice-controlled interfaces for daily tasks.

These technological innovations, born from the world of video games, have fundamentally reshaped not only the gaming industry but have also impacted healthcare, entertainment, education, and even business software. The gaming industry’s relentless drive for innovation continues to propel technological advances that influence all aspects of modern life.

From Data to Resilience: Generative AI’s Role in the Future of Supply Chains

By Tom Perrone, SVP Global Professional Services at project44

In a world where disruptions are becoming more frequent and complex, supply chain resilience is essential for companies to safeguard their operations, meet customer expectations, and build sustainable, future-ready networks.

In 2024 alone, a series of unexpected events have significantly impacted the global supply chain. Early in the year, a powerful earthquake in Taiwan disrupted tech manufacturing and semiconductor production, affecting a range of industries. Soon after, the collapse of a major bridge in Baltimore strained transportation routes, delaying the distribution of automotive and other goods across the United States. Meanwhile, ongoing vessel attacks in the Red Sea region have disrupted key shipping lanes, slowing trade flows worldwide. Now, extreme weather, port strikes, and rising costs continue to threaten the stability of supply chain operations, underscoring the urgent need for resilient strategies.

Fortunately, recent technological advancements, especially in Generative AI, offer promising solutions for navigating these complexities. Generative AI has the potential to revolutionise supply chain management across various

organisations - from streamlining manual processes, effectively handling exceptions and risks, and unlocking invaluable insights.

It all begins with data

Until now, only organisations with big budgets, scaled infrastructures, and deep technical and security expertise could build the sophisticated foundation data models upon which Generative AI-driven supply chains must be built. For many enterprises, supply chain data remains fragmented, inaccessible, massive, and incomplete. These factors combined make it extremely difficult for supply chain leaders and their teams to make the decisions necessary to act quickly and confidently for true resiliency.

Yet, companies could gain a massive amount of strategic advantage by consolidating and standardising this insightful supply chain data to make critical business decisions. Since

every Generative AI journey starts with data, the datasets to standardise and enrich include mainframe computers for tracking inventory and orders, distributed computing & LANS to decentralise supply chain management systems, ERP systems to integrate all aspects of operations, and eCommerce data to view orders and shipments in real-time.

As a standalone activity, data standardisation can be a highly complex and costly activity, so supply chain leaders should look for a tech provider that can enable this data enrichment and connectivity layer via a GDPR-compliant platform for a ‘single source of truth’. With AI and ML being core components of this platform, anyone responsible for supply chain management would have access to, and the ability to withdraw, standardised data to make better decisions and meet real-time operational needs.

Leveraging data insights for strategic advantage

By connecting siloed information across the supply chain, organisations can then transform their global operations with advanced insights and analytics to improve overall performance. With a Generative AIenabled supply chain management platform, particularly one with predictive analytics capabilities, companies will be able to explore their enriched data to discover trends in performance, determine if issues are isolated or systemic, and take swift action to deliver unprecedented business value.

Predictive analytics offer the potential to identify patterns and uncover exceptions before they happen. It would, for example, be possible to determine whether a carrier is having a one-time issue or if it is likely to become a common or repeat cause of delayed shipments. At the same time, predictive analytics would make it possible for supply chain teams to evaluate historical shipping lane performance to better inform carrier assignment and overall supply chain planning. Another potential use case is analysing both computational and

statistical data to provide the most dependable arrival predictions for customers to improve their experience and increase satisfaction.

In a world over-flowing with data, the transformative potential of AI in supply chain management cannot be overstated. It offers a powerful means to convert raw data into actionable insights, crucial to effectively manage the unpredictable disruptions we now face every day. Historically, the advantages of Generative AI have been reserved for organisations with substantial resources. However, access to these benefits is being democratised by working with a supply chain technology partner, who can help transform your supply chain from a cost center to a revenue generator.

Powering supply chains of the future

Through solutions like predictive tracking and ETAs, as well as accurate, secure handling of big data to provide a single source of truth for transportation data, collaborating with supply chain technology experts empowers organisations of all sizes to harness the full potential of their supply chain - streamlining operations, enhancing decisionmaking, and achieving highvelocity.

Ultimately, digitalisation and generative AI empower supply chains to move beyond reactive responses, enabling them to be predictive and agile. This adaptability equips businesses to maintain resilience and thrive amidst ongoing global uncertainties.

STATS YOU NEED exec.index

By 2025 it is estimated that 85% OF CUSTOMER INTERACTIONS will be managed without human intervention, thanks to technologies like chatbots and virtual assistants.

(Source: Gartner)

The global telemedicine market is projected to reach

$185.6 BILLION by 2026, growing at a CAGR of 23.5%.

(Source: Fortune Business Insights)

FinTech investment globally reached $105 BILLION in 2020, with a 17% year-over-year growth.

(Source: KPMG)

E-commerce sales worldwide are projected to reach $6.54 TRILLION by 2022 accounting for 21.8% of global retail sales.

(Source: eMarketer)

The use of Artificial Intelligence (AI) in healthcare is expected to generate $150 BILLION in annual savings for the U.S. healthcare economy by 2026.

(Source: Accenture)

Digital payments are expected to reach a transaction value of $6.7 TRILLION by 2023.

(Source: Statista)

WEARABLE DEVICES

in healthcare are estimated to reach 105.4 million shipments globally by 2023.

(Source: International Data Corporation)

Blockchain spending in the financial sector is expected to exceed $16 BILLION annually by 2024.

(Source: International Data Corporation)

The global smart manufacturing market is expected to reach $479.01 BILLION by 2025, growing at a CAGR of 10.7%

(Source: Grand View Research)

The adoption of Internet of Things (IoT) technologies in manufacturing is predicted to reach $1.4 TRILLION in value by 2030.

(Source: McKinsey & Company)

Industrial automation is anticipated to lead to a 33% REDUCTION in manufacturing costs by 2025.

(Source: Capgemini)

67% OF MILLENNIALS prefer to shop online rather than in-store.

(Source: BigCommerce)

These statistics highlight the transformative impact of technology in various sectors, shaping the future of retail, healthcare, finance, and manufacturing.

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