What is a Data Product?


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There’s a world of difference between a data product and a dataset. A data product is useful and ready to help you solve a problem. A dataset is—well, it’s a lot to dig through. You can get to work right away with a data product, but a dataset will take a few days to understand.

Imagine you’re at Costco and see a new bicycle that you like. It’s affordable, has some decent features, and is a good replacement for the old rusty bike sitting in your garage. You imagine riding through your neighborhood, getting some exercise, and maybe losing some weight. Sounds like it could be a fun way to enjoy your afternoon!

You put the box in your cart, drive it home, and are ready to get riding. A few quick slashes with a box cutter reveals—surprise! It’s a box full of bike parts. Technically, there’s a bike in there, but it requires assembly. There’s a poorly-printed manual with poorly-translated assembly instructions. It looks like your afternoon ride is going to have to wait a few days.

You thought you were buying a bicycle, e.g. a data product, that was ready for a relaxing and fun afternoon. Instead, you got a project, e.g. a dataset, that’s going to take you a few days to figure out, assuming all the parts came in the box. At the end of this project, you’re going to hope you set up the brakes and gear shifters correctly. Maybe if you were a mechanic with a big set of tools you’d feel confident, but right now you’re realizing you bought an hours-long chore.

Data products play a crucial role in fueling innovation, growth, and business success. But what exactly are data products? They’re sophisticated tools crafted from algorithms and data-driven insights, delivering actionable results tailored to user needs. Think of them as the secret sauce that, when added to a company’s data strategy, can unleash new levels of innovation and operational efficiency. 

With countless success stories floating around, we can’t understate the importance of data products. Let’s start by defining what a data product is and explore how it has become a game-changer.

Definition of a data product

Data products are packages of data from one or more datasets specially curated to solve a business problem. They’re cost-effective, purpose-built, and easily consumable; they come in various formats, from PDFs and Excel sheets to plain text to API feeds. A good data product should be easy to use, with options like downloadable files. The ultimate goal? To address and solve challenges for end-users, regardless of how broad or specific those challenges might be.

The anatomy of a data product

Creating a data product involves several key components that collectively gather, process, analyze, and display data effectively. These include:

  • Data sources and collection methods
  • Techniques for processing and analyzing data
  • Approaches to displaying and presenting data
  • Embedded algorithms or AI components

Data sources and how teams collect data

Data products rely on diverse data sources to gather relevant information. Data teams collect and manage data from these sources for use in data products. Data product managers design and package them as easy-to-consume as possible. Enterprises use data collection methods, such as interviews, log files, surveys, analytics, observations, and experiments, to obtain data from different sources. Ensuring that the data teams collect is accurate, complete, and reliable is critical to the overall success of a data product.

Processing and analysis mechanisms

Once the team collects the data, it must be processed and analyzed. Data products use various processing and analysis methods to transform raw data into actionable insights. These methods include manual and automated processing.  and automated,  

Analysis plays a key role in converting raw data into useful information and ensures the data teams use in data products is high quality and reliable. With the right processing and analysis mechanisms in place, data products can deliver significant benefits, such as improved decision-making, new revenue streams, and enhanced user experiences.

Effective data analysts and data scientists can look at a dataset and determine what’s useful and valuable and what’s not. Equipped with programming languages like Python or R, they employ statistical methods to uncover underlying trends. They might use AI/ML tools like TensorFlow or Scikit-learn to detect patterns, identify outliers, forecast future trends, or even predict customer behavior. Additionally, by leveraging data visualization tools such as Tableau or Power BI, these professionals can translate complex datasets into intuitive graphics, aiding stakeholders in making informed decisions.

Presentation and visualization layers

An essential aspect of data products is their ability to make data accessible and understandable through presentation and visualization layers. These layers include the user interface, such as reports or dashboards, that allow decision-makers to interact with and understand the data. Visualizations and user interfaces enable users to effectively engage with the data and derive insights from it. Recommendation engines, predictive analytics tools, and smart assistants are all data products that use presentation and visualization layers.

Data visualizations in Power BI

Data visualizations in Power BI

Embedded algorithms or AI components

Some data products incorporate AI or machine learning algorithms to enhance their functionality. These algorithms recognize patterns in data, detect anomalies, and make predictions, thereby improving the accuracy and efficiency of data products. Using AI and machine learning algorithms in data products also uncovers insights that would otherwise be difficult to acquire, such as nuanced purchasing trends, hidden inefficiencies in supply chains, or the interplay of variables affecting customer loyalty and retention.

Consider Amazon’s recommendation engine: it uses AI to analyze user behavior, purchase histories, and browsing patterns. This machine-learning-driven data product suggests items, enhances shopping experiences, boosts sales, and uncovers previously hidden insights.

Types of data products

Data products can serve different purposes and audiences. Products are typically categorized as:

  • Internal
  • Consumer-facing
  • Embedded

The way a business builds the products will determine how they use them to solve their specific problems.

Internal data products

Internal data products, such as analytics dashboards for decision-makers, are designed for internal use. These products enable employees to analyze and interpret data for strategic objectives, helping enterprises optimize their decisions and processes. This leads to improved decision-making, streamlined processes, and increased profitability.

Consider Salesforce’s internal analytics dashboard. Salesforce provides decision-makers with an intuitive platform to monitor customer engagement, sales performance, and market trends. This internal data product identifies bottlenecks, forecasts sales, and strategizes market campaigns. By leveraging these insights, Salesforce continuously refines its approach, enhancing customer relationships and driving revenue growth.

Consumer-facing data products

Businesses aim consumer-facing data products at end-users to provide value directly to customers. One typical example of a consumer-facing data product is a recommendation engine on an e-commerce site, which uses algorithms to suggest products or services that may interest users. Implementing consumer-facing data products can increase customer engagement, customer satisfaction, and spur higher revenue.

Zillow’s home value “Zestimate” is a prime example. Zillow estimates property values by analyzing local real estate data and user inputs. The feature helps potential buyers and sellers make informed decisions, enhances user trust and engagement, and leads to more site visits and transactions.

Embedded data products

Embedded data products include AI components integrated within software or apps, enhancing their functionality and providing additional value to users. These products automate processes, improve precision, and offer insights that may be difficult to obtain via traditional methods. 

Predictive analytics tools and smart assistants are prime examples of embedded products. By incorporating AI and machine learning algorithms in data products, enterprises gain a competitive edge and stay ahead of the curve.

Key challenges in developing data products

Developing data products involves technical and non-technical challenges, such as joining disparate datasets and understanding what data consumers—humans and systems—need for a data product to be valuable. The most common challenges involve:

  • Data quality and reliability 
  • Scalability 
  • Security and privacy
  • Ensuring data relevancy

Ensuring data quality and reliability

One of the primary challenges in developing data products is ensuring data quality and reliability. To guarantee the accuracy and trustworthiness of the data they use in data products, enterprises must employ data validation processes, such as data cleansing, data scrubbing, and data profiling. These processes eliminate or rectify inaccurate, incomplete, or inconsistent data, thereby improving the quality and reliability of the data product. By implementing these data validation processes, enterprises ensure their data products are reliable and trustworthy.

Scalability concerns

Scalability is another challenge in developing data products, as enterprises must ensure their products are able to handle increasing data volumes and user demands. Enterprises are turning to cloud-based data fulfillment platforms like Revelate to achieve scalability. Revelate can quickly scale up or down to meet business needs. If your business experiences a sudden increase in data volume, for example, Revelate can automatically add additional computing resources to your account. 


Enterprises should also factor in the cost of scaling their data products, especially since many already use cloud and distributed computing within a cloud data warehouse for efficient cost management.

Security and privacy implications

Data products must also protect sensitive information. Proper protection involves ensuring data security and privacy by protecting data from unauthorized access and adhering to data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Failure to adhere to security and privacy regulations may result in data breaches, fines, and reputational damage.

Keeping the data product relevant and up-to-date

Maintaining the relevance of data products requires accurate, up-to-date data. It requires regularly monitoring data sources, properly collecting and storing data, and using automated processes to update the data product. By keeping the data product accurate and relevant, enterprises ensure it remains pertinent to the user.

Navigation apps like Waze constantly update traffic conditions using real-time user data. Regular monitoring ensures accurate route suggestions, with automated updates ensuring relevance. Continuous refreshment guarantees users receive timely directions, enhancing the app’s utility and reliability.

Benefits of implementing data products

Businesses are swiftly turning to manufacturing and implementing data products to leverage vast amounts of data. By doing so, they solve everyday business problems and gain competitive advantages. The benefits of implementing these data products include: 

  • Enhanced decision-making 
  • New revenue streams 
  • Improved user experiences 
  • Competitive advantages

Enhanced decision-making

Data products provide valuable insights that enable better decision-making within enterprises. By leveraging data products, businesses can optimize their decisions and processes for improved efficiency, better customer service, and increased profitability. Data products that support enhanced decision-making include analytics dashboards, reports, and other data-driven tools for analyzing and interpreting data.

For instance, Airbnb uses data products to optimize pricing suggestions for hosts. By analyzing local demand, events, and historical data, their dynamic pricing algorithm recommends rates to maximize bookings. Implemented on platforms akin to Databricks, this tool helps hosts make informed pricing decisions, increasing revenue and guest bookings.

Creation of new revenue streams

Data products generate new revenue opportunities by monetizing data assets. For example, enterprises may create and sell reusable data asset-based data products to customers, such as subscription-based services, one-time purchases, or advertising revenue. By leveraging data products and using data pipelines, businesses uncover novel opportunities, optimize processes, and generate new revenue streams, all while maintaining an efficient data warehouse.

In the realm of supply chain management, the integration of data products can foster strategic business partnerships. Consider a scenario where a manufacturer partners with its vendors and uses data products to predict future demand. By sharing these insights with their suppliers in real-time, both parties can synchronize their operations, resulting in optimized inventory levels, reduced waste, and streamlined production processes. 

Such partnerships not only solidify trust between businesses but also create a collaborative environment where shared data becomes a catalyst for mutual growth and profitability. By harnessing the power of data products, businesses can move beyond traditional transactional relationships and cultivate partnerships that drive collective innovation and efficiency.

Improved customer experience

Data products provide users with personalized recommendations or insights. An excellent example of this personalization is Spotify. Introducing “Discover Weekly” in 2015 transformed data into a tangible product. By analyzing listening habits, genres, and user-created playlists, Spotify curated a weekly list of song recommendations tailored to each user. 

Spotify didn’t just introduce an algorithm—it created a personalized data product to enhance user experience. The result? Over 40 million users engaged with Discover Weekly within its first year. With this innovation, Spotify retained subscribers and demonstrated the profound business impact of harnessing data as a marketable product.

Recommendation engines on ecommerce sites use algorithms to suggest products or services that may interest users. As a result, enterprises increasingly turn to these data-driven tools to enhance customer engagement and satisfaction. Similarly, predictive analytics tools anticipate future sales trends so businesses can make informed decisions and better serve their customers.

Competitive advantage in the market

Enterprises that gain insights into customer behavior, market trends, and other data-driven insights make informed decisions and stay ahead of the competition. Additionally, data products detect novel opportunities, optimize processes, and generate new revenue streams.

Starbucks leverages data products to analyze customer buying habits and store traffic. Its loyalty app tracks purchases, preferences, and visit frequency. These insights drive personalized promotions, menu optimizations, and store location strategies. By decoding customer behavior, Starbucks fine-tunes offerings, discovers growth avenues, and ensures a positive customer experience.

Finding a platform for data productization

Data products bridge the gap between data’s potential and tangible business growth. Using platforms like Revelate, data producers are able to apply fundamental product management principles to develop their offerings. They transform data into market-ready solutions that align with real-world challenges. Conversely, data consumers actively integrate these products into their operations, seeking insights to enhance decision-making. Their goal is not mere consumption but optimization, turning data into actionable strategies. 

As technology improves, it’s important to understand the relationship between producers and consumers. Companies that focus on innovative production and smart consumption are likely to be industry leaders.

The next logical step for enterprises is to identify the best tools and platforms to aid in their data product journey. To effectively develop and manage data products, it’s best to leverage data fulfillment platforms like Revelate that offer comprehensive solutions for data productization

Revelate addresses key challenges such as data quality, scalability, security, and relevance. The platform enables enterprises to manage data products effectively, ensuring they are built on reliable data and scale to handle growing user demands. Revelate helps enterprises harness the power of data products to drive their success and stay ahead of the competition.

Frequently Asked Questions

What is the difference between data and a data product?

Data is a collection of information while a data product is a technological product that leverages data to fulfill its objective. A data product is any platform or tool that uses data analysis to deliver results.

What is an example of product data?

Product data is information related to products or services such as a pair of shoes, concert ticket, rental car, haircut, or streamed TV show.

Why do you need data products?

Data products are essential for enterprises to effectively collect and analyze large volumes of data, helping them make better decisions.

By leveraging data products, enterprises can gain insights into customer behavior, market trends, and other important data points. These insights help them make more informed decisions and better understand their customers.

Why do data products work?

Data products work by collecting source data, processing it, and providing access to authorized data consumers through the use of data services and pipelines.

Data services and pipelines enable data consumers to access the data in a secure and efficient manner. They also provide the necessary tools to transform the data into useful insights. This process allows data consumers to make informed decisions and take action.

What challenges come with developing data products?

Developing data products presents challenges related to data quality, scalability, security, and relevance that businesses must address.

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