What is a data product?

Data has now overtaken oil as the world’s most valuable resource, according to The Economist. And like oil, data needs to be processed to get the most out of it. Data products are what enable users to extract value from this resource. 

A data product, in general terms, is any tool or application that processes data and generates results. Businesses can use the results of such data analysis to obtain useful information like churn prediction and customer segmentation, and use these results to make smarter decisions. 

Data products have one primary objective: to manage, organize and make sense of the vast amount of data that organizations collect and generate. It’s the users’ job to put the insights to use that they gain from these data products, take actions and make better decisions based on these insights.

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What value does it provide?

The best data products can help businesses and organizations extract intelligence from their data in order to make predictions, optimize costs, and ultimately, generate more revenue. To collect this data, businesses and organizations might consult a large array of sources that might include data mining of consumers and users, business performance metrics, and other sources.

Data products are the most valuable when they fulfill a specific need within a business. For example, if a company wants to make an app that can identify a flower that you hold up to your phone camera, they would have to build a custom tool that works with a unique dataset of plant information. 

usinesses can easily use customizable data products to translate the wealth of data that’s already at their fingertips into actionable insights, specific to their company. For example, with data products tailored to its needs, a call center can analyze the competence level and speed of its workers, and a headhunting firm can track how many of its’ clients find jobs. 

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Examples of Data Products

Examples of the successful use of data products are everywhere: from auto-correct on your phone and spell-check on your computer to recommendation systems, the “you may also like” list that appears when you show interest in a particular book on an online retailer’s website. 

Data products can be sorted into three general categories: those that output data as a service, data-enhanced products, and data as insights. Examples of data as a service could be the weather app on your phone, or the real-time stock market ticker running across the bottom of your television screen. 

Data products can also enhance physical or virtual products, such as converting a car into a self-driving vehicle or smart clothing that can respond to touch or track data from its user. Data as insights refers to products that analyze the data “behind the scenes” in order to improve the performance or another aspect of a product. This could be something like Google Analytics, or, the best example we can think of, Sisense.

The Future of Data Products

What’s next for data products? There’s no doubt that AI and machine learning will figure prominently in the engine that drives data products into the future. In fact, 96.4% of companies are investing in AI and machine learning capabilities in 2019 compared to 68.9% in 2017. (Source: NewVantage Partners).  And because the right data product can substantially increase a company’s revenue and competitive advantage, that future is extremely lucrative.

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