Data Products in Action: Examples and Insights

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In 2012, a father stormed into a Target store in Minnesota, furious that his teenage daughter was receiving advertisements for baby products. To his shock, the store’s data algorithms had accurately predicted her pregnancy before he knew. Welcome to the world of data products—tools and algorithms that transform raw data into valuable insights, often with uncanny precision. 

In the current data-driven era, Understanding data products and gleaning insights from their applications is the difference between thriving or lagging behind in the competitive market. Well-crafted data products guide decision-making and reshape industries, unlocking the future today by making sense of patterns and nuances that otherwise remain hidden.

Key features of effective data products

Data products are a collection of digital assets that are curated and consumable. They can include PDF files, Excel spreadsheets, plain text files, and rows from a source database. They often provide downloadable files, APIs, models, or other abstraction levels on top of the underlying data. They aim to solve problems for end users, even if the problem is loosely defined.

The simplest data products may contain a single CSV file or a small spreadsheet. The most complex data products may contain terabytes of data and be accessible only via a sophisticated, secure API. But it’s not the size of the data product that matters, it’s the usability and business value.

Data products that are truly effective possess certain key features: 

  • Interactivity (to promote exploration and engagement)
  • Usability/Consumability
  • Scalability
  • Data accuracy and reliability
  • Integration capabilities

Reusable data assets improve the effectiveness of products by making them more efficient and adaptable. They save time and resources by avoiding the need to recreate data from scratch. They also ensure that data products are consistent and reliable by providing a single source of truth.

Interactivity and usability

Interactivity and usability ensure data products deliver a smooth user experience where users can interact with the data in real-time and analyze it in a multitude of ways, ultimately leading to more informed decision-making.

Example: Created in March 2020, The New York Times’ Coronavirus Tracking Map is an interactive visualization that allows users to track the spread of COVID-19 around the world. The map shows the number of cases, deaths, and vaccinations in each country. Users can also filter the data by region, date, and other factors.


Scalability ensures data products can grow with the organization and handle increasing data volumes. With the growth of organizations and their data generation, data products must be highly scalable, facilitating quick and efficient processing of large datasets.

Not scaling data products can have serious consequences. It can lead to slow performance, instability, and security vulnerabilities. Failure to scale may disrupt operations, damage the organization’s reputation, and put the organization’s data at risk.

Example: Facebook must be able to handle a large volume of user activity, such as posts, comments, and likes. Facebook engineers have worked to make Facebook more scalable by using distributed architecture, caching techniques, data compression techniques, and cloud-based infrastructure. By implementing these practices, Facebook has been able to scale its data products to handle billions of user interactions per day.

Data accuracy and reliability

Users trust the insights data products provide based on data accuracy and reliability. To ensure that data is useful and understandable, it needs to be cleaned, organized, and structured. Inadequate data quality can result in inaccurate or deceptive outcomes, which can have serious ramifications for businesses relying on these insights.

Example: In 2016, the UK’s healthcare system used a data product to predict which patients were at risk of developing sepsis. The data product was inaccurate, resulting in many patients not being treated for sepsis. These inaccuracies led to patient deaths. This incident highlights the importance of ensuring data quality in data products, especially in healthcare, where data quality can literally be life or death.

Integration capabilities

Integration capabilities allow data products to work together with other tools and systems within an organization. They enable smooth data exchange between different applications and systems, so that businesses can use data products more effectively to provide valuable insights and drive success. Businesses in today’s interconnected digital landscape must prioritize integration capabilities, especially as organizations use many different tools and systems to achieve their goals.

Example: Amazon’s Echo smart speaker uses data from multiple sources, such as the user’s voice, music library, and shopping habits, to provide personalized recommendations. Echo would not have been possible without integration capabilities, which allowed Amazon to connect the speaker to its other systems and services. Its convenient integrations have made it a popular product, with over 100 million units sold worldwide.

Data product examples

Several existing data products are recognizable by their brand names, including:   

  • Recommendation engines: Amazon, Netflix, and TripAdvisor
  • Predictive analytics tools: FICO, Zillow, and LinkedIn
  • Data APIs: Google Maps, X (formerly Twitter), and LinkedIn
  • Real-time dashboards: IBM Watson IoT, Cisco Kinetic for Cities, and TradingView
  • Personal finance insights: Mint, Personal Capital, You Need A Budget (YNAB)
  • Health monitoring wearables: Fitbit, Apple Watch, Garmin

Each data product example showcases the diverse applications and benefits of data products across different industries and use cases.

Recommendation engines

Recommendation engines provide personalized suggestions based on user behavior and preferences. By employing algorithms to assess user behavior and inclinations, these engines generate tailored recommendations for products, services, or content that a user may find of interest.

Example: Tripadvisor uses collaborative filtering to recommend hotels, restaurants, and other travel destinations to its users. The data product analyzes user ratings and preferences to find other users who have similar tastes. It then recommends businesses these users have rated highly.

Predictive analytics tools

Predictive analytics tools help organizations:

  • Forecast future trends
  • Make data-driven decisions
  • Use data from various sources, such as a cloud data warehouse
  • Recognize trends
  • Forecast customer behavior
  • Maximize business processes

Example: Zillow uses predictive analytics to estimate home values. The company collects data on a variety of factors, such as the home’s location, size, and features. It then uses this data to develop models that can predict the home’s value. Other examples of predictive analytics tools include Salesforce’s Einstein AI and finance terminals like the Bloomberg Terminal.

Data APIs

Data APIs enable seamless data exchange between different applications and systems. By facilitating efficient data exchange, data APIs permit the development of robust data products that businesses can use across various platforms and applications.

Example: Google Maps is a data product example that uses an API to provide users with access to map information. The API allows developers to integrate Google Maps into their own applications. To do this, Google Maps first collects data on the world’s roads, businesses, and other features. It then stores this data in a database. The API allows developers to access this data and use it to create their own maps and applications. 

Data products that use data APIs may include denormalized tables or materialized views, dashboards and visualizations, machine learning models, and recommendation systems. However, these types of data products rely on the proper management of metadata and dataset instances.

Real-time dashboards

Real-time dashboards provide up-to-date insights and visualizations for easy data interpretation. They enable users to promptly and effortlessly access information, detect patterns, and make decisions in real-time.

Example: Cisco Kinetic for Cities is a real-time dashboard that helps cities to manage their infrastructure. The dashboard provides insights into a variety of data, such as traffic conditions, energy usage, and water levels. To do this, Cisco Kinetic for Cities collects data from a variety of sources, such as sensors, cameras, and traffic signals. It then aggregates this data and presents it in a way that is easy to understand. The dashboard identifies issues such as traffic congestion or water leaks. 

Real-time dashboards are indispensable for businesses that need to quickly interpret data. They often incorporate customer segmentation tools, predictive models, recommendation systems, fraud detection algorithms, and chatbots or virtual assistants, all of which help businesses make better decisions, improve customer experience, and increase profitability.

Personal finance insights

Personal finance insights help users manage their finances and make informed decisions. By offering data-driven guidance, these insights allow individuals to better understand their financial situation and make smart choices about their money.

Example: YNAB (You Need a Budget) is personal finance software that helps users track their income and expenses. It uses a zero-based budgeting system, which means that every dollar is assigned a purpose. As a result, the data product helps users stay on top of their finances and make informed financial decisions.

Here are some data product features that you can use to gain personal finance insights: 

  • Data-driven applications make decisions by feeding data into prediction-based algorithms 
  • Predictive models predict future events by identifying patterns in data
  • Dashboards and visualizations present data in an easy-to-understand way
  • Denormalized tables or materialized views optimize data querying by duplicating data from other tables to make it easier to access

Health monitoring wearables

Health monitoring wearables collect and analyze health data to provide personalized recommendations and insights. These devices, such as fitness trackers, smartwatches, and heart rate monitors, enable users to monitor their health metrics and make informed decisions about their well-being.

Example: Apple Watch uses a variety of sensors to collect health data, such as heart rate, steps taken, and sleep quality. Apple’s health app then analyzes the data to provide users with insights into their health and fitness. Users can also set goals and track their progress towards those goals.

By providing improved health outcomes and tailored recommendations, health monitoring wearables illustrate the potential impact of data products on personal health and wellness.

The creation process: From raw data to data product

The creation process of data products involves several key steps, including transforming raw data into a usable format, ensuring data quality, and designing an effective user interface.

How to transform raw data into a usable data product

Data productization refers to the process of transforming raw data into valuable, actionable insights or products that can be easily consumed, understood, and leveraged by businesses, organizations, or end-users. It involves the systematic collection, processing, analysis, and visualization of data to create products or services that generate value for customers or stakeholders.

Data productization often includes the following stages:

  1. Data collection: Gathering relevant data from various sources, such as sensors, social media, surveys, or databases.
  2. Data cleaning and preprocessing: Removing errors, inconsistencies, and redundancies in the collected data, as well as transforming it into a usable format.
  3. Data analysis: Applying statistical, machine learning, or AI techniques to discover patterns, trends, and relationships within the data.
  4. Data visualization: Presenting the results of the analysis in an easily understandable format, such as charts, graphs, or interactive dashboards.
  5. Data product creation: Developing a product or service that leverages the insights gained from the data analysis, such as a predictive model, recommendation engine, or data-driven decision support system.
  6. Deployment and integration: Implementing the data product within the intended system or environment, and ensuring it functions seamlessly with existing tools and processes.
  7. Continuous improvement: Regularly updating and refining the data product based on new data, feedback, or evolving requirements.

Businesses rely on various sources of raw data to create data products. Examples include:

  • The census
  • The stock market
  • The weather forecast
  • The internet

Revelate’s data fulfillment platform is a key player in the data productization space. The platform gathers data from various sources, processes it for usability, and transforms it into valuable data products users can share. Revelate users can then access the data by adding these data products to a public, private, or hybrid web store. These data products represent the refined versions of the original collected data, carefully prepared for data sharing purposes.

The importance of data quality, modeling, and user interface design

The creation of effective data products that provide valuable insights hinges upon three key factors.

  1. Data quality: Ensuring data is precise, dependable, and uniform
  2. Modeling: Examining intricate data sets and extracting valuable insights
  3. User interface design: Creating an intuitive and user-friendly interface for accessing and interacting with the data

Additionally, a well-crafted user interface makes data products more accessible and beneficial to customers, ultimately leading to more informed decisions and better business outcomes.

Data products are not a one-and-done proposition. They require ongoing maintenance and refinement to ensure that they remain viable in the long term. For every new data product version, its creators should gather customer feedback and iterate on that feedback. By following this process, data product owners can ensure that their products continue to meet the needs of their users and remain relevant in the marketplace.

Why data products reflect today’s innovative energy

Reflecting on the Minnesota Target incident, it’s clear that data products stand at the forefront of today’s technological evolution. These tools don’t just analyze; they reveal, often catching us off-guard with their precision. As we navigate this rapid tech progression, the ascendance of data products in multiple industries becomes undeniable. Their growing significance and adoption underscore the shift towards data-centric decision-making.

Revelate is a data marketplace that enables businesses to obtain useful data products. The Revelate platform aggregates data products from a variety of sources, including public data sets, private data sets, and data analytics tools. Businesses that use Revelate to find the data products they need make better decisions, improve their products and services, and increase their bottom line. Businesses seeking to gain a competitive edge should try Revelate today.

Frequently Asked Questions

What is an example of a data product?

Google Maps’ “faster route now available” notification, Salesforce’s Einstein AI, the Bloomberg Terminal, and Google Analytics all constitute a data product example. These range from decision-support to automated decision-making tools, and provide businesses with powerful predictive analytics capabilities.

What is data and a data product?

Data is a source of information businesses use to build products. A data product is a self-contained solution that delivers a trusted dataset, created for specific purposes and composed of data from multiple sources, which can be used to inform decisions or solutions.

What is a data asset vs a data product?

Businesses hold data assets internally and share them with a limited audience, whereas they create data products from one or more data assets and open them to a wider audience, which may not receive all the same data.

This wider audience may not have access to all the same data as the internal audience, but they can still benefit from the data products the business created from the data assets. Businesses can use data products to create insights, inform decisions, and drive innovation.

What is the value proposition of data products?

Data products provide valuable insights which inform decision-making and drive business strategies, creating a strong value proposition.

What are they key features of effective data products?

Effective data products have key features such as interactivity, scalability, reliable data accuracy, and integration capabilities to ensure an enjoyable user experience.

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