Transforming Data Management: The Power of Treating Data as a Product

In today's rapidly evolving digital landscape, the concept of treating data as a product is gaining significant traction. Harnessing data effectively for business operations, strategic planning, and AI has never been more critical for organizations as their data environments grow more complex. Data is no longer just a byproduct of business operations; it is an asset that can drive superior business outcomes when managed correctly. The traditional approach to data management often falls short in addressing the scale, distribution, diversity, and dynamic nature of modern data. Chief Data Officers (CDOs) and data teams are under immense pressure to maximize the utility of data assets while enhancing the productivity of data workers. 

By treating data as a product, organizations can simplify data consumption, management, and valuation, making it easier to derive actionable insights and drive innovation. In IDC’s recent worldwide Office of the CDO Survey, organizations that scored high in managing data as a product are positively correlated with providing ease of access to data, incorporating data in business decisions, supporting AI initiatives, improving data governance, and enabling digital business.

Positive correlations also existed in the measurement of financial KPIs. Organizations that scored high in managing data as a product saw improvements of 8.1% in profit and 7.4% in revenue. By comparison, those scoring low in data product management posted improvements of 2.6% in profit and 4.3% in revenue.

What is a data product? IDC’s interpretation

There is no standard definition of what constitutes a data product. Definitions range from "it's a table in a relational database, or an object in an object store" to "it's an analytical dashboard and all of the assets in the pipeline that deliver the data and analytics in the dashboard."

Rather than putting constraints on what exactly a data product is, IDC says that data products are defined by access, business value, and accountability. Access is making data products available, discoverable, and reusable. Value involves determining the business value derived from the data products, which drives the data valuation methodology. Accountability determines who is responsible for developing, maintaining, monetizing, and nurturing the data product throughout its life cycle. Effective data products tend to be a set of domain-specific data entities (think customer, product, location), and operational or analytical assets that can be used across multiple solutions and are accessible by multiple personas.

The two key classes of stakeholders in data productization are data product producers and data product consumers. A data product producer is not one person but typically a team of individuals who assume data product ownership, design, development, and management responsibilities. These teams may include data architects, engineers, stewards, and line of business (LOB) roles accountable for the data product.

Data product consumers are generally in the LOB, except in situations where new data products are assembled from existing data products. In these cases, many of the same data product producer roles are also data product consumers. Producers and consumers rely on governance and shared services, with roles such as CDOs, data architects, data and application engineers, and data and system administrators all supporting the creation, delivery, fulfillment, and life cycle management of data products.

Data product management versus data management: What’s the difference?

Managing data as a product is different from data management. Data management is focused on maintaining the consistency and integrity of data held within transactional, analytical, and object repositories, without any one specific business purpose other than perhaps supporting business operations. Managing data as a product requires a focus on the business value delivered by the product, which may itself be made up of several data and analytical assets. Data management tends to be centralized, whereas data products are distributed throughout the organization based on the business domain in which value is being delivered. Managing data as a product requires product management, data marketplace or data product hub technology, and fulfillment capabilities.

Transitioning from data to data-product management requires executive buy-in, along with a clear charter, defined scope, actionable plan, and metrics to measure success. It will also require dedicated resources. Plans will need to include change management within the office of the CDO and IT (as the data producers) and with the LOB (as the data consumers). New processes and procedures for product requests need to be put in place, in parallel with new technology, for data product creation, management, marketing, and fulfillment. Collaboration among data, business, and IT teams will also be a critical success factor for organizations embarking on this paradigm shift into managing data as a product.

Message from the Sponsor 

The shift to data products to achieve better business outcomes follows the mindset of what Agile has done for application teams. The goal is to enhance an organization's ability to deliver value faster. This is accomplished through leveraging domain expertise, empowering decentralized data teams, adopting a product-oriented mindset, and providing a marketplace for easy consumption. 

Data product owners and producers work closely with the business to prioritize and manage the products. Consumers can easily find them in the marketplace, where data products are organized in registries that provide essential context—examples, underlying data assets, limitations, and quality metrics—so consumers can quickly assess whether a data product meets their needs.

Curious to learn how a data catalog can help you harness the power of data products? Book a demo with us to learn more.