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June 7, 2023

How to Build Great Data Products

Rohit Choudhary

(Tina Ji/Shutterstock)

By now, it’s common knowledge that data is everywhere and being produced and consumed at an astonishing rate. But the more important thing to focus on—especially for enterprises that have invested in creating data environments—is what can be done with said data.

In today’s hyper-competitive market, every company wants to get the most from its data by putting it toward use cases that either generate revenue or build important business models. Enter, data products: Solutions, applications, or tools that help businesses make better decisions and streamline their processes through data.

Uber, for example, is a data product. Yes, it’s also a consumer product, but it relies on accurate data to make sound decisions. For instance, if people are leaving a big concert in droves, Uber needs to decide how many drivers to send to that particular area. Sending the right amount of drivers is contingent on having reliable data.

Zillow has created an innovative data product by taking publicly available data on residential housing sales, and using it to give home buyers and homeowners critical insights that had previously only been available to licensed realtors.

Other examples of data products might include: a software app that helps businesses visualize and analyze their sales data; a website that uses real-time traffic information to help drivers take optimal routes; or a mobile app that tracks fitness and health data to provide users with personalized exercise and nutrition recommendations.

Uber is a data product (guteksk7/Shutterstock)

Many companies are sitting on invaluable data that is unique to their organization, customers, and industry. There’s a huge opportunity to use this data to build new digital products that were previously unimaginable, or impossible to create. Now is the time to get on board: Without a good data product, you won’t be a successful company in the long run.

Here are a few key steps for building great data products.

Listen to What Your Data Is Telling You…

The first step to building a stellar data product is to identify a need in your market. What is your data telling you? And does it map back to something your users may really want to know? Think back to Zillow, who provided homeowners and buyers with beneficial information that previously wasn’t even available to them.

To identify this need, data practitioners and data business leaders must examine the intersection of their data investments with potential opportunities. The goal is to uncover whether the data available to you can deliver insights that are not available anywhere else in the market. Once this is determined, you need to rapidly build that information back into existing products to make them even better, or create innovative new ones.

…And Understand That More Isn’t Always Better

As we touched on, companies have a lot of data. So, while in theory it might be possible to build a data product that delivers a massive amount of information, is that really what users want and need? We live in an age of ultra short attention spans, and no one wants to have to parse through information to find what they need.

Keep things simple by zeroing in on what users want, and delivering it to them in a delightful, easy-to-digest way. The data products that provide the most value are those that are simple and easy to understand.


Make Accuracy and Reliability a Top Priority

As Donato Diorio said, “without a systematic way to start and keep data clean, bad data will happen.” When building data products, it goes without saying that we don’t want bad data to happen. Your data is only as valuable as the amount of trust your users have in it.

Not every data team can build their data stack from the ground up in an attempt to prevent bad data from the get-go, but aspiring to have reliable, accurate, clean data should still always be a top priority. Thankfully, data observability tools make this much easier by providing the continuous ability to ensure reliability, data pipeline efficiency, the elimination of data blind spots, and performance management capabilities.

Iterate, Iterate, Iterate, But Accurately

One awesome thing about building your own data products is that new data you generate may be applied to the product to make it better. Continually iterating based on new data and insights gives enterprises an edge by ensuring that their data products are always improving and adding more value for the user. And with agile delivery methods like DevOps, enterprises can continuously deliver new and improved products with no disruption to customers.

But again, it’s critical that you’re using accurate data here. If bad data is used to iterate on your data product, it could result in a less accurate product for customers. Eventually, this could even lead them to make poor decisions due to the inaccuracy of your product. Data quality is of paramount importance, especially when it comes to iteration.

Enterprises stand a lot to gain from building data products that provide unique value to their users. But it’s vital that the data upon which these products are built is of the highest caliber. Data observability tools make building data products easier and more accurate than ever. By following the steps above, and enlisting the help of a data observability platform, enterprises can stay competitive and further capitalize on their most valuable asset—their data.

About the author: Rohit Choudhary is the CEO and co-founder of Acceldata, a San Jose-based startup that has developed an end-to-end data observability cloud to help enterprises observe and optimize modern data systems and maximize return on data investment. Prior to Acceldata, Choudhary served as director of engineering at Hortonworks, where he led development of Dataplane Services, Ambari and Zeppelin among other products. While at Hortonworks, Rohit was inspired to start Acceldata after repeatedly witnessing his customers’ multi-million dollar data initiatives fail despite employing the latest data technologies and experienced teams of data experts.

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