"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.
Is the idea of treating data as a product revolutionary? No, not if you’ve been making a living in the data space for the past seven to ten years. Not that viewing and treating data as important isn’t important. (Hint: It is.) But for seasoned data practitioners, the idea of data as a product is more evolutionary than revolutionary.
However, for roles and experiences that run adjacent to data engineering, data science, and the like, the concept certainly may be revelatory. So, perhaps the key here is less about the significance of data as a product and more about the significant impact it’s increasingly having within business organizations. But this significance hinges on how widely accepted this newer view of data management is.
So, with that said, here’s everything you need to know.
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.
In addition to making products more relevant, this enhanced personalization can also enhance customer experiences and satisfaction.
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. This realization, despite its massive success, was not widely shared at the time.
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.
Example: Amazon relies exhaustively on its data to make informed decisions about inventory management, sales predictions, and the preferences of its customers. The way Amazon views and handles its data improves its inventory management and customer experience while fueling highly targeted marketing campaigns.
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. It is also foundational for facilitating machine learning models and algorithms that drive automation and innovation solutions.
Example: Spotify’s innovative year-in-review “Wrapped” evolved out of how the company analyzes the listening habits of its users. Empowered by its utilization of data, this feature has been exceptionally beneficial for Spotify, generating 425,000,000 tweets in the three days after its 2022 launch.
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 and often involves robust data management practices to ensure data is always pristine.
Example: Airbnb is such a company, as its user experience and brand reputation rests on a database of property listings, reviews, and user interactions that need to be up-to-date and accurate at all times. Due to the quality and integrity of Airbnb’s data, its users (both renters and hosts) know they can trust the company to provide accurate information and minimize instances of fraudulent listings.
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. Handled correctly, this can increase the value and perception of the organization. What’s more, engaging stakeholders with high-quality data products can also enhance loyalty and cement the organization’s reputation as a trusted data provider.
Example: With 28.7 million active members, Starbucks’ loyalty program isn’t just popular, it also contributes 55% of the company’s overall revenue. This is all based on the skill with which Starbucks handles and analyzes datasets of their customers' purchases.
This analysis empowers the coffee brand’s ability to offer personalized discounts and product recommendations at scale, promoting loyalty and repeat purchases.
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. Data architectures also function to keep data strategy aligned with critical organizational goals. Reciprocally, this alignment allows the data to evolve in step with the business.
Example: With the business model tied to the whipsaw demands of cloud storage, the Dropbox POV on data enabled it to closely analyze feedback and metrics from its users. The company then began to scale its cloud storage solutions, tailoring them to actual usage through this analysis. The success of this endeavor helped Dropbox expand its user base and introduce more business-specific solutions.
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. Implementation of security protocols and access controls are key components of safeguarding data and making sure it’s used ethically.
Example: HSBC Bank stakes its reputation on the quality of its data to help ensure compliance with increasingly influential global financial regulations. It also puts its data to work through the advanced analytics it uses to detect fraudulent activities.
8. Enhanced collaboration and synergy
Quality data can foster collaboration throughout an organization as it promotes more cohesive data ecosystems. But data scientists and engineers on data teams benefit in particular—the as-product POV increases the cohesion of development and management of data products.
Example: Before Slack was a household name, the product was a bit of a phoenix—born from the ashes of a failed video game project. From its first early iterations as an IRC, the Slack team has relied on the quality of its data to refine and improve the platform by studying team collaboration patterns of actual users.
9. Increased revenue generation and monetization opportunities
Viewing data as a product also allows organizations to leverage data assets as monetization opportunities. Common examples include providing analytics services or selling industry-specific insights. Productized data can also enable the creation of value-added services that go on to generate additional revenue streams.
Example: The General Electric Predix platform provides us with examples of both data leading to a monetization opportunity and a revenue-generating value-add service. Designed for the Industrial Internet of Things (IIoT), Predix collects data from GE-produced machinery and equipment.
By analyzing this data, Predix can estimate when a machine is likely to fail or require maintenance. This predictive maintenance service allows businesses to proactively schedule maintenance, reducing downtime and preventing problematic breakdowns. However, GE’s ability to sell the Predix service itself now functions as a revenue-generating, value-adding service.
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. Approaching data as a product, then, justifies further investments in data-related capabilities. In turn, these investments ensure continuous improvement and evolution within an organization.
Example: Patagonia is well-known as a retailer of premium outdoor clothing and gear. But as an activism brand, the company also focuses on how its products impact ecosystems and communities. One way it accomplishes this is by focusing on the data produced by its materials and supply chain, monitoring the impact its business operations have on the environment.
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? While the advantages are easy to appreciate from the outside, the realities of business operations are always complex.
Broadly speaking, even forward-thinking decision-makers and data leaders may simply face a combination of cultural and operational factors they are unable to resolve. Here are some of the most common:
Cultural factors
In modern organizations, cultural factors limiting progress are sometimes the hardest to control. Especially since we humans are generally lukewarm regarding change. Within a business, this can manifest as a calcified traditional culture, one that may think the best way to view and handle data is the “way things have always been.” Alternatively, traditional business cultures may view data as a byproduct of operations, not a valuable product unto itself.
Other culturally related problems may involve a simple lack of education, namely how beneficial viewing data as a product is, or that doing so is even a possibility. There may be a lack of expertise required to do so—or the fear that making data more accessible and user-friendly may encourage bad actors to abuse it. Or, organizational leadership may simply be prioritizing immediate gains over long-term initiatives, like data product management.
Operational factors
Alternatively, issues affecting how data is viewed within an organization may be hard-coded into the operation of the business itself. For instance, resource constraints may complicate the ability to procure sustained investments in the tools, personnel, and technology required to treat data as a product.
For organizations that operate within highly regulated industries, regulatory and compliance concerns, unfounded or not, may stifle attempts to view data differently. Concerns here may revolve particularly around the idea of making data more accessible and productized. These issues can also be compounded when data is siloed in different departments, making it difficult to foster a unified approach to data product management.
Organizational data that is poor, inconsistent, and outdated can be challenging to treat as a product. An organization may also struggle to embrace the concept due to a reliance on legacy IT systems, which can be challenging to integrate when modernizing their data infrastructure.
Finally, based on their maturity, some organizations may struggle to establish clear ROI for an initiative to radically alter how they treat their data. However, like the other issues noted here, this lack of clarity can be overcome with the right combination of strategy, resources, and commitment from leadership.
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. And data contracts are the best way to guarantee the quality of data before it even enters an organization.
For this reason, we’re offering a transformative approach to retaining, developing, and operationalizing data contracts. Make sure to join our product waitlist to be among the first to experience the benefits of Gable.ai.
