Deriving value from 'Data Products': Benefits of Data Products - White Paper

Despite recognizing the power of data, and the likelihood of the emergence of smart workflows and seamless communications between humans and machines in the near future, most organizations struggle to unlock its full potential. Organizations typically invest in programs that focus on meeting the needs of an end user, while also customizing data pipelines that can be reused tomorrow. However, most experts believe that organizations should focus their investments on data strategies that deliver short-term value and lay the footing to create future uses.

Treating data like a ‘product’

Some organizations have been gaining ground as a result, by treating ‘data’ like any other ‘consumer product’. Just as businesses roll out standard products that can be customized according to users’ needs, to maximize their revenues, a ‘data product’ is applied to various business challenges through a robust set of high-quality and easy-to-use data. For example, it delivers a complete view of customers, channels, employees, and more, amid the continuous rise of new and unpredictable data and data technologies. In other words, a data product is a reusable data asset that is created to provide a trusted dataset to achieve specific business outcomes.

These data products are created for both external and internal users, such as an application or dashboard or a metric or machine learning (ML) model embedded in workflows of users. In a nutshell, data products can produce massive returns because of their numerous applications. Sitting atop data lakes and data warehouses, data products assist teams in cutting down time to search for products, process them into formats, and create tailored data pipelines and datasets.

Deriving ‘value’ from data products

Data products are unique in the sense that they are focused on people, processes, and the business rather than technology. Essentially, data products involve ‘product thinking’ about data, implying the need to factor in the first principles of data platform as a product. In other words, to consider the different user personas, the user intent or the purpose of a data platform, and the product components that are accessed by users through various interfaces.

User Perosnas

Who are the users?

Data products allow businesses to extract insights from their vast pools of data, helping organizations make better predictions, and increase their revenues. Likewise, organizations can also tailor their data products to offer actionable insights to fulfil a specific business need.

User Intent

What user purpose does the data have?

Product Components

How does the data platform appear to users?

Key characteristics of a data product

Some of the key characteristics of data products, include:

• Its discoverability and reusability,

• Its data quality,

• Its security,

• Its observability to determine higher data reliability and quicker fixing of errors, and

• Its skills for agile decision-making.

DataOps has evolved in many ways to include features like testing, deployment, automation, and uninterrupted integrations. However, the major focus for DataOps should be to accelerate the growth of dependable data products.

Some organizations determine the effectiveness of their data products by increasing the use of their data, which ultimately leads to superior data-driven decisions. Meanwhile, other businesses have been aggressively involved in monetizing their data products.

Data products are everywhere

Data products are visible almost everywhere, such as the auto-correct feature on a phone or the spell check option on a computer. Data products can also be classified into different categories, such as data as insights (Google

Analytics), data-as-a-service (weather app on a smartphone), and data-enhanced products (converting any physical or virtual product into, for example, smart clothing that measures user data).

Traditional versus modern consumption of data

According to a McKinsey report, organizations have been focusing on two main traditional data consumption strategies – the grassroots approach and the bigbang strategy. In the first, use case teams collect the technologies and data that results in duplications and complex architectures, which are expensive to create,

A traditional and ineffective approach to data

Data

are fragmented

use, and sustain. The second strategy focuses on a centralized team to extract, filter, and aggregate data altogether. This reduces some replication of efforts but is askew to business use cases and therefore fails to meet the specific needs of its users.

Different technologies are employed for each use case, adding expense and complexity.

Data for each domain, such as the customer, is inefficiently reworked for every use case; quality, definitions, and formats wary.

A data product model focuses more on saving costs and time through a standardization approach. Consequently, use case teams use standardized products and connect

technologies based on users’ data consumption patterns to develop solutions. This reduces efforts, simplifies data architectures, and helps in quicker realization of value.

Data Product strategy

Flow of data

Systems of record Data platform Data products

Consumption archetypes Use cases

Digital applications

Digital banking app

Vendor

Core processing systems

Data warehouse

Customer

Branch

External data

Raw data lake

Product/ service

Advanced analytics

Investing portal

Predictive cross-selling model

Unstructured data

Operational data store

Employee/ agent

Reporting

External data sharing

Discovery sandbox

Predictive churn model

Financial report

Industry data ecosystem

Data exploration

Source: McKinsey

Apart from enabling speed and efficiency and providing all the data under one unit, data products can support different data consumers or systems like digital applications, advanced analytics, discovery sandboxes, reporting systems, and external data-sharing systems, which consume data. These business systems have their

unique ways to store, manage, and process data. And just as a Lego brick, a data product can be applied to several business applications with similar consumption models, according to the report. The consumption models showcase the most important ways in which data is consumed by users.

Benefits of data products

Data products generate better returns on investments despite some initial costs, supporting many use cases and outcomes.

Data products are beneficial for data consumers, as they provide:

• Quick insight,

• Trusted data,

• Improved data,

• Quick response time,

• High-quality data, and

• Easily discoverable and available data.

Future of data products

For organizations, data products offer:

• Agility,

• Reusability,

• Data trust,

• Data integrity,

• Business-focused and outcome-driven results, and

• Collaboration between business and IT.

The surge in data is forcing the need for larger and more diverse datasets to be created in the future. These datasets will facilitate users to easily identify patterns in datasets, while offering scope to continuously improve data products. Additionally, the rise of emerging technologies like artificial intelligence (AI), machine learning (ML), and deep learning, will open new paths for new and varied data products.

As Big Tech makes massive investments to improve such data products concepts further, only those who can make data more reusable are likely to gain market edge. Data products are significant in delivering new business use cases faster than before, in cutting down technology, maintenance, and development costs, and in minimizing data governance and risk liabilities.

About the Author

PRARTHNA TIGA • Senior Analyst - Product – iNAVA

Prarthna Tiga is part of the Business Operations, technology data intelligence team at SG Analytics. She has over 7 years of experience in market research and publishing. Prior to joining SG Analytics, she was part of the technology and media teams at GlobalData. Prarthna holds a Master’s degree in English Literature and a Bachelor’s degree in Commerce. She is keen about charitable services, and is an avid reader, traveller, and sports enthusiast and can be found playing basketball or badminton in her free time.

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