Data is a powerful new form of capital. It’s essential for organizations to survive and thrive in today’s fast-paced business environment. And it’s generated everywhere from humans, machines and Internet of Things (IoT) devices to edge systems and beyond.

Now, there’s a new opportunity to turn all that data into a competitive differentiator. How? By creating data products.

Simply put, they’re products that facilitate end-goals through the use of data.¹ They contain data packaged with everything someone needs to understand that data and use it to solve a new use case—even if that person works in a different team or outside the business altogether. Just like consumer products, data products are designed for specific purposes. Let’s take a closer look at what data products are, and how they differ from traditional approaches to using data.

Today, most companies approach enterprise data with a project mindset. Each time a business function has a problem that it wants to solve with data, the organization starts a project to acquire the data, cleanse and prepare it, then analyze it for that specific use case. And each time a new business problem or use case arises, it follows a similar process to acquire, prepare and analyze data for its specific need.

This project mindset may sound familiar. It’s how most data teams currently operate. But this approach has some significant drawbacks:

• It may lead to duplicated work.
• It’s relatively slow.
• The outputs from each project typically can’t be repurposed to solve other use cases.

Data products give companies a better way to address their data needs. They’re designed with the entire organizations’ data needs in mind, and they can be reused to support numerous use cases across multiple functions. Data products can be:

• Datasets—reusable datasets (eg for design, manufacturing, finance and operations), data streams, data feeds, or APIs that meet the needs of the whole enterprise, as well as each business function.
• Code—feature code and transformation snippets (small blocks of reusable code) or data models (which show the logical structure of how different data elements fit together).
• Analytics models—reusable machine learning models (eg for predictive maintenance).
• Dashboard reports—reusable dashboards (eg risk dashboards) and other visualizations.

You may not realize it, but you’re probably already familiar with some data products. In fact, many digital natives—such as Google, Uber and Netflix—have built their entire businesses around them. They compete on their ability to drive actionable insights from their data.

But data products aren’t just for digital natives. They offer huge potential for established companies, and they can even open up entirely new revenue streams. For example, a medical device manufacturer can start supplying medical-grade data services to healthcare providers to help drive better patient care. An oil and gas producer can achieve greatly enhanced efficiency in its plants. Or a media and entertainment company can serve personalized content to its customers.

To successfully implement data products, organizations must support the entire lifecycle—from conceiving and designing the product through to building it, rolling it out, supporting it, then retiring it when it’s no longer needed. This responsibility should sit with a new organizational function devoted to data product management. It’ll need people with a broad range of skills around data, business analysis, DevOps and more. Plus, the teams creating data products need knowledge of the relevant industry and domain.

Organizations should develop data products in a data platform that’s built in the cloud. Why? Because cloud enables scale, agility, and the opportunity to drive reinvention. It allows for data to be connected as a part of a larger continuum. And by tapping into the Cloud Continuum, organizations can productize their data—wherever it resides.

Investing in data products can really pay off. After all, data products empower organizations of all kinds to leverage data to achieve critical business outcomes. It’s too big an opportunity to miss.

¹ Definition from Data Jujitsu: The Art of Turning Data into Product, 2012, by Dhanurjay “DJ” Patil, former Chief Data Scientist at the United States Office of Science and Technology Policy