02 Three Pillars of Analytics and their Need in Post-Covid Era

Students’ Corner

Three Pillars of Analytics and their Need in Post-Covid Era

“The last ten years of IT have been about changing the way people work. The next ten years of IT will be about transforming your business.”?Aaron Levie, CEO of Box. Businesses have been transforming themselves for a long time, but, Coronavirus pandemic has accelerated the adoption of digital transformation like nothing before.To thrive in our new virtual business world, companies have been forced to ramp up their digital strategy quickly. Those that were unable to pivot and reset their business plan on time did not survive.

In a post-covid era, businesses that progress digitally will engage more directly with customers, accelerate innovation, and hence demand a larger share of profit in their industries. Every business has the potential to become a digital one. Today's digitally changed businesses have an advantage, and only digital businesses will prosper in the future.

The goal always remained to create a data-driven organization. The times, however, have changed. Data must now guide businesses to survive in the unpredictable post-COVID-19 environment. The ability to quickly adapt and alter the business plan with personnel, goods, and processes based on actions, whether on the global market or within the company, is more apparent today. This means that advanced data analytics is essential for running realtime business processes. Beyond data-driven, the goal now is to use advanced data analytics to drive real-time business planning. Data and business analysts must focus on strengthening the three pillars of data analytics: speed, performance, and agility, to alleviate dissatisfaction and provide a better analytics solution and experience for the organization. The foundation for enabling these pillars is the entire data architecture backed by a sound data strategy. To handle increasing data volumes and accommodate more advanced and performant analytics models, reliable and fit-forpurpose infrastructure is required. Speed According to Gartner's 2019 CIO Agenda survey, firms that used AI increased from 4% to 14% between 2018 and 2019. Companies must look beyond the traditional use of CPU power to support their AI and Machine Learning applications using Graphics Processing Units as AI continues to grow. This will motivate them to design, train, and retrain their analytical models and run them more rapidly, resulting in better customer products and services. GPUs enable businesses to massively parallelize activities to assist the training of analytical solutions and/or inferencing, allowing them to finish numerous epochs in a quicker timescale and fine-tune the model. Furthermore, deploying GPUs in the cloud enables businesses to execute a variety of AI/ML workloads with the flexibility required for a cost-effective, scalable, and secure AI solution.

Whether a healthcare provider provides more accurate, faster diagnoses or a retailer provides more tailored customer experiences;more businesses are using AI. According to a 2018 Forrester survey of data and analytics decision-makers, 53% of businesses employ AI.

Performance Semi-structured and unstructured data are significantly more difficult to evaluate than structured data, yet they are far more common in businesses. The proliferation of IoT and social media content, according to IDC, will result in 80 percent of global data being unstructured by 2025. Organizations need to bring a wide range of data together in a 360° view that gives deeper, more accurate, and precise analytics insights to stay competitive. It's not always easy to tell what data is structured and what

Ms. Pragya Ram Nanwani

Student - MBA06 IIM Amritsar

is semi-structured or unstructured. Unstructured data is sometimes defined and processed in a structured manner (for example, log data in CSV format), whereas schemadefined data isn't always structured. These inconsistencies throw a monkey wrench into data analysis. Data scientists and analysts with extensive experience in building the best models and methodologies for working with unstructured data can use AI algorithms to help discern value from large amounts of unstructured data.Enterprises must develop a long-term Data Architecture that allows them to move beyond data-driven analytics to drive business intelligence through advanced analytics.

Agility A data model should bring all important data and related tables from many data sources together so that analysts can query them all at once and in connection to one another. Analysts' information and the value of their insights are limited if they do not do so. To account for all of the data that analysts will need, data engineers, data analysts, and business stakeholders must interact with one another to establish business requirements, intended data uses, and prospective (current) data access limits. Continuous communication necessitates having open and honest dialogues, asking the correct questions, and agreeing on the business needs and timetables that everyone is working toward. The evaluation of the business's operating environment should be at the center of the Data Strategy. Data Vault is one such example. Data vault helps enterprises efficiently grow their data quantities and respond to rapid business changes while maintaining a thorough data catalog and flexible data architecture. Because a history of the data is available, this is extremely valuable for compliance and auditing requirements. Firm executives receive insights into where change may be welcome, where it is needed, and where it is crucial if the business is to survive as the pace of transformation and volumes of data continue to climb by examining how an organization is genuinely meeting the three pillars of data analytics.