Our recently published article in Harvard Business Review, “A better way to put your data to work,” details how to establish a sustainable path to value. The key is to manage data just as you would a consumer product. Here, we present a visual summary of this approach.
Today’s predominant—and largely unsuccessful—approaches to data
Organizations typically employ either a grassroots or big-bang data strategy—neither of which enables them to make the most of their data investments.
In a grassroots approach, individual teams must piece together the data and technologies they need. This approach results in significant duplication of efforts and a tangle of bespoke technology architectures that are costly to build, manage, and maintain.
At organizations employing the big-bang strategy, a centralized team extracts, cleanses, and aggregates data en masse. This approach can eliminate some of the rework that occurs, but it’s often not aligned with business use cases and therefore fails to support end users’ specific needs. End users often struggle to confirm that the data provide the necessary level of governance and quality, which limits the time savings. Later work on new use cases that are aligned with business value often triggers a grassroots approach and its associated problems.
These strategies fail to lay the foundation for current and future use cases that will create value.
A better approach: Managing data like a product
We find that when companies instead manage data like a consumer product—be it digital or physical—they can realize near-term value from their data investments and pave the way for quickly getting more value tomorrow.
Data products provide all the data on one entity
Data products are wired to enable standard types of consumption
Data products enable more speed and efficiency
The benefits of this approach can be significant:
- New business use cases can be delivered as much as 90 percent faster.
- Total cost of ownership, including technology, development, and maintenance costs, can decline by 30 percent.
- The risk and data-governance burden can be reduced.
Getting started with data products
Success in product development requires an operating model that ensures dedicated management and funding, the establishment of standards and best practices, performance tracking, and quality assurance. Success with data products is no different.
- Dedicated management and funding. Each data product should have a product manager and a team consisting of data engineers, data architects, data modelers, data platform engineers, and site reliability engineers who are funded to build and continually improve their product and enable new use cases. These teams should sit within a data utility group inside business units. This organizational structure gives them ready access to the experts they need (including business subject-matter, operational, process, legal, and risk experts) to develop useful and compliant data products. In addition, it gives the teams access to user feedback, which helps them continue to improve products and identify new uses.
- Standards and best practices. We find organizations are most successful when they institute standards and best practices for building data products across the organization. This work is typically handled by a data center of excellence. Establishing standards and best practices includes defining how teams will document data provenance, audit data use, and measure data quality, as well as designing how the necessary technologies should fit together for each consumption archetype so they can be reused across all data products.
- Performance tracking. To confirm that their products meet end-user needs and are continually improving, data product teams should measure the value of their work. Relevant metrics may include the number of monthly users for a given product, the number of times a product is reused across the business, satisfaction scores from surveys of data users, and the return on investment of use cases enabled.
- Quality assurance. Because quality issues can erode end-user trust and retention, data product teams closely manage data definitions (for instance, whether the definition of customer data is limited to active customers or includes active and former customers), availability, and access controls that meet the right level of governance for each use case. To confirm data integrity, they work closely with data stewards who own data source systems.
For a deeper look at how leaders can manage data as they manage a product, read “A better way to put your data to work ,” on hbr.org.