Gable Blog | Benefits of Treating Data as a Product
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"The seeds of today become the harvest of tomorrow."
For a long time, we viewed data as a by-product. We did things online. And data occurred as a result.
As those online activities expanded, much more data was generated.
At some point, when we found ourselves treading water in a sea of data, we began to realize that not all data was the same. Some data was better, some was worse. And a subsequent realization was that better data allowed us to do many more things. So, it followed, why not treat data with intent—ensuring it maintains the highest possible quality throughout its lifecycle?
History is brimming with pivotal moments where a seemingly obvious insight rocked what was considered foundational knowledge at the time.
What does it mean to view data as a product?
Thinking of data as a product starts with the idea that data deserves to be treated with the same care, consideration, and strategic thinking as traditional products in an organization. Doing so involves embracing the following key characteristics:
Value: Ultimately, organizations treat data as a product in order to maximize the value derived from it. But this operational mindset also increases its applicability, be that to increase automation, improve decision-making, fuel machine learning algorithms, or other use cases.
User-centric design: Traditionally, products are designed with the end user in mind. Therefore, approaching data as a product should begin with an understanding of the specific needs of data consumers. For data teams in business organizations, consumers will typically include data scientists, analysts, and business decision-makers and stakeholders.
Lifecycle management: Products have distinct lifecycles. Data does as well, consisting of stages that include data creation, maintenance, and retirement. Data product management, then, ensures that data remains high-quality, up-to-date, and relevant throughout the data lifecycle.
Quality assurance: Viewing data as a product naturally results in data quality becoming paramount. Resources can then be justified to ensure data is high quality, with data integrity checks and process validations ensuring accuracy, consistency, and timeliness.
Accessible and discoverable: Products aren’t useful if they can’t be used. Treating data as a product means data catalogs, metadata, and proper access controls keep data discoverable and accessible by those who need to use it.
Documentation and training: To ensure usability, clear documentation should be established relating to the organization’s data. This document should also be accessible to all who need it, and contain relevant, up-to-date details such as data schema, sources, and quality.
Self-service and governance: End users should be empowered to access and use data without the help of data engineering or IT teams. Self-service tools make this both possible and secure, along with proper data governance that clearly defines ownership, stewardship, and data usage policies.
Iterative development and feedback loops: Much as software products benefit from iterative and agile development, data should be refined over time as consumers provide feedback and the needs of the organization evolve. Feedback itself should be fostered through an established, formal feedback mechanism, ensuring data constantly meets the needs of its users.
Scalability and data integration: Data volume within an organization will tend to tick up over time. Supporting data as a product means the corresponding infrastructure and tools needed to store, process, and analyze it must scale accordingly. Data and related data products need to be kept in alignment with business objectives and strategies as well, ensuring data initiatives, at any scale, continue to drive business value.
Security and compliance: Finally, productized data needs to be protected, especially when it contains sensitive or personal information. Security and compliance should include access controls and the proper levels of encryption. It should also be kept in compliance with all relevant regulations, such as the California Consumer Privacy Act ( CCPA) or the General Data Protection Regulation ( GDPR).
10 examples of how treating data as a product benefits organizations
Viewing (and, more importantly, treating) data as a product as opposed to a commodity is much more than a thought exercise. This modern approach to handling data produces many compelling benefits for businesses.
The following 10 examples demonstrate both the value and variety of ways these benefits manifest within organizations.
1. Enhanced personalization
As noted, treating data as a product involves both understanding and accounting for the specific needs of internal data consumers. But business end users can benefit as well, as better data within an organization can lead to products with higher usability, and which are much more attuned to what consumers specifically need.
Example: The evolution Netflix undertook to shift from a movie rental-by-mail service to a content streaming (and now producing) juggernaut would not have been possible had the company not realized the immense value of its data.
2. Better decision-making
An organization viewing data as a product can more easily integrate data into its decision-making processes. Often, the boon to decision-making takes the form of actionable insights and the organization’s ability to facilitate strategic initiatives. However, stakeholders and business leaders can also improve business decisions by leveraging high-quality data to increase the accuracy, potency, and value of organizational use cases.
3. Sustaining innovation and competitive advantages
Embracing data as a product fosters innovation, especially regarding product development and service offerings, which can lead to a competitive edge in the market.
4. Improved quality and integrity management
The data-as-a-product perspective also enables a consistent and overall focus on data quality and data integrity within an organization. This can be mission-critical for businesses that need to prioritize reliability and compliance.
5. Increased brand perception and loyalty
When data is productized and offered externally via an API, an organization can showcase its expertise, reliability, and value to specific communities.
6. Sustainable scalability
A robust data architecture forms the skeletal structure for data-forward organizations. It supports high-quality data while enabling the business to grow and adapt to market trends.
7. Regulatory compliance and governance
Successfully treating data as a product requires robust data governance practices, as this ensures the ethical use of data and regulatory compliance.
8. Enhanced collaboration and synergy
Quality data can foster collaboration throughout an organization as it promotes more cohesive data ecosystems.
9. Increased revenue generation and monetization opportunities
Viewing data as a product also allows organizations to leverage data assets as monetization opportunities.
10. Long-term sustainability
While many of the benefits of treating data as a product occur in the relatively short term, the subsequent lifecycle management it requires also ensures data sustainability and relevance in the long term.
Common reasons why organizations don’t see their data as a product
All the benefits discussed to this point beg the question—why doesn’t every company treat its data as a product?
Cultural factors
In modern organizations, cultural factors limiting progress are sometimes the hardest to control.
Operational factors
Alternatively, issues affecting how data is viewed within an organization may be hard-coded into the operation of the business itself.
Ensuring the value of data gets baked into an organization’s DNA
As is now abundantly clear, treating data as a product is paramount for any organization looking to succeed in an overwhelmingly data-dependent world.