Data Products: Definition, Examples, Governance Guide

Trusted, governed information helps teams create reusable business assets with clear ownership, quality rules, and AI-ready enterprise practices at scale.

The amount of data created and owned across an organization is constantly growing. For enterprise-level businesses, this can mean untold volumes of information that need to be cataloged, sorted, cleaned, validated, and interpreted before becoming useful to an individual employee or the business at large. 

Even with a robust, end-to-end data quality platform, this is a daunting task for a data management department, and it is why businesses are turning to data products as an open-and-go solution. 

What are Data Products?

Data products are packaged data assets that help answer specific business questions. They also help individual departments ask and answer department-specific data questions with faster time-to-insight than ever before. 

These products are reusable, curated sets of information that have been packaged together in user-friendly ways to make it easy to see the data, understand the data, and take action on the data for a specific business use case. 

Data products could be compared to a ready-to-use sewing kit. Yes, a person can go out and choose a pattern to sew. There are lots of options, so it might take a while. Then they have to pick the type and color of the fabric, the type of needle they will use, the thread type and color, the modifications they might want to make to that pattern… the decisions are endless. They have to gather all these pieces, learn how to interpret the pattern, work hard to get the fabric to lay the right way, and then rip stitches out and try again when they get something wrong. 

Or… that person could buy a sewing kit with a built-in tutorial that provides everything they need and a step-by-step of how to use it. 

A data product is like a ready-to-use kit: it helps governance teams unify around a business concept rather than purely  organizing and cleansing a data catalog, and provides business analysts with an open-and-go product that they can start using to make decisions right away. 

These solutions also increase data literacy, by delivering data in packages that the end-user can read, understand, and use to make informed choices. 

What are the Advantages of Using Data Products? 

Don’t enterprise data teams handle all the data sorting and cleansing that a company might need? Yes. Kind of. Maybe not entirely. 

The volume of data assets that companies are creating in today’s world are increasing exponentially, and data analysts and data engineers can’t always keep up — especially when each department of a large business needs the same data to work differently for them and their business goals. The marketing team may need data presented in one way, and the finance teams and inventory teams need that same data in a different format to make entirely different decisions for their own departments. 

This is where these offerings really shine: they are created to be domain-specific and allow different teams within an organization to own and act on them. This way, marketing, compliance, and sales can all have custom solutions created for their specific use-case, rather than one data officer overseeing all of it and needing to create vastly different dashboards for different domains of the company… all ASAP, because business needs to happen today. 

In contrast, the same premade product can help teams achieve different goals, while all of those goals are supporting the overall corporate vision and long-range planning efforts.

Data Products Create Benefits for Internal Teams and Increased ROI 

Companies that use them see improved data access across teams and better automation capabilities. Teams that use these tools are also able to use data to better align with overall company-wide goals and objectives. 

Data engineers welcome the automated testing and quality standards that these packages bring to the table through data product contracts, where minimum quality standards are set in stone. In addition, data engineers get a stable reference point for their AI pipelines so they can build with confidence, and not guesswork.

Data analysts, employees, and even consumers benefit from fast, reliable data access that meets their specific needs — and they always know which data to use, without having to guess or wait on a centralized IT team to point them in the right direction.

IT teams and Chief Data Officers can avoid the bottlenecks that high data volumes create when their team isn’t large enough to manage all the requests or assets — and by offloading that burden, they can redirect capacity toward the AI readiness initiatives that actually move the business forward.

Team leads and department directors can be empowered with data products to make decisions based on accurate, quality data without the long lead times needed for pulling traditional reports. 

Implementing data products that are built for consumption across the whole organization streamlines their use and enables teams to use data in a way that makes sense for them.

Data Products Help AI Determine What Matters

Pure data doesn’t serve the end-user very well. But by packaging that data into data products that are layered with metadata, semantics, and dashboards, data teams can help their AI and machine learning systems to interpret the data and make more relevant decisions based on the enterprise goal. 

This delivers faster and better insights to stakeholders, enabling them to make decisions based on what matters and not just what AI thinks matters. 

Data Products vs Data as a Product

Data Products and Data as a Product are two related concepts:

What is data as a product? Data as a Product is an organizational mindset where businesses treat data as a valuable asset to be used in decision-making. 

What is a data product? Data products are physical, reusable packages of data that are created with data consumers in mind. 

Once an organization recognizes data as a product, they can start to shift to using data products to speed up their use of its data assets. 

Data Product vs Dataset vs Data Asset

If we continue to use the sewing kit analogy, a dataset is a collection of raw data that has the potential to be used for anything. It’s the materials still sitting at a crafting store. 

A data asset takes data a step further: it’s a generic sewing kit that comes with all the parts, but not with a specific project in mind. 

A data product, however, is that fully-outfitted sewing kit complete with everything you need to start sewing now. It is a ready-to-use product that a consumer can purchase and start using from minute one. 

That’s the beauty of data products: they are built with the end-consumer of that data in mind, and are then able to deliver a better, more targeted product.

Key Characteristics of Trusted Data Products

While data products themselves vary widely based on their target market and specific use-case, all well-designed data products will share these key characteristics: 

Ready-to-use data. This data has been cleansed, transformed, and is ready to use right out of the package. This saves internal teams time. 

Meets internal quality standards. The data product should have clearly-defined quality metrics, complete with outlined metadata, data lineage tracking, and internal testing frameworks. This ensures that you are getting what you pay for in a data product. 

Built for end users. Data products should be ready-to-comprehend for whoever the target audience is. If it’s not out-of-the-box usable, it’s not doing its job. 

Portable. Data products are packaged into a single unit, making it easy to share across an organization. If it’s not easily sharable, the time-savings will be lost. 

Reusable. These data products must be able to be reused across an organization and in different departments. One-time uses wouldn’t make data products worth buying. 

Unless a data product decreases the time-to-insight for your internal teams, it’s not worth investing in for your organization. 

Data Product Examples for Enterprise Teams

What is a data product in real life? There are lots of examples:

Recommended-for-you algorithms on shopping sites and streaming services are all data product-driven. Personalized content for you is based on data sets and user interfaces that are packaged as a data product. These data sets have been created from and tested via other lookalike profiles in order to key into what might be a hit for you. 

Common apps like maps and weather apps use APIs to pull data from other sources and package it in a user-friendly dashboard, helping you choose shorter destinations or make decisions about what jacket to wear. 

The financial sector uses data products frequently, using data sets to help score and analyze fraudulent charges or assess loan applicants based on risk factors via a trusted, proven data product. Even personal finance tools like Monarch or YNAB are data products that pull and sync data from disparate sources and collate it into a single, consumer-focused software app. 

All of these data products enable consumers to interact with large, unmanageable data sets in a way that is quick and comprehensible, and they add value to their overall lives or improve their ability to do business well. 

Why Governance Matters for Data Products

Governance matters for all data use, but especially for data products. Creating trust with users and consumers means that raw data needs to be transformed into reliable assets. These data assets need to balance security and user-friendliness. If data access doesn’t comply with privacy laws or isn’t validated, the data and, therefore, the data product will be unusable or create breaches of trust within an organization or with customers of the organization. 

Data governance is also important in data products in order to comply with industry-specific regulations. When purchasing and implementing a data product, a company needs the assurance that the product they are purchasing and all the Terms and Conditions of using that product aligns with not only their business objectives, but also with federal, state, and local laws. 

How to Build Governed Data Products at Scale

Building out governed data products at scale means that organizations must move from project-based goals to product-driven goals. Governance can’t just be a checkmark at the end of a build-out: data products must have governance embedded into their foundation as part of the product lifecycle. 

Data contracts need to clearly define the ownership of the data product, as well as set the quality standards. 

By building data governance into the product, producers can protect data by default. Access controls need to be both role-based and attribute-based, so that users can access only the data they need. 

Once a data product is built, teams need to continuously monitor the product to manage risk and avoid compliance violations. Automated data governance capabilities are essential to maintaining governance throughout the lifecycle of the data product. 

How Ataccama Helps Teams Manage Governed Data Products

Once an organization decides that data isn’t just a by-product of their operations, but an asset in and of itself, Ataccama can help them create a system for complete data pipeline monitoring and data product implementation.

With Ataccama ONE, AI-assisted real-time data management and AI readiness aren’t just a far-off dream. Using a unified data trust platform, Ataccama helps organizations break down data silos and ensure their teams have access to the high-quality data they need to make better decisions and leverage better business outcomes.