We’re living in the age of unprecedented data growth. We’ll soon be producing a staggering 181 zettabytes of data globally each year. But raw data itself doesn’t equal success. That’s where data products come in, transforming these massive volumes of information into actionable insights and tangible business value.
In this blog, we’ll dive into the foundations of data products – what they are, why they’re rapidly becoming a cornerstone of data strategies, and emerging use cases for data product-driven innovation. You’ll find out the key steps in creating and managing data products, their benefits, and real-world examples. Let’s dive into the world of data products.
What is a Data Product?
A data product is a curated, self-contained combination of data, metadata, semantics, and/or templates that is designed to deliver specific insights or functionalities that address business needs. Data products are intended to be shared and reused throughout an organization, taking the form of interactive dashboards, reports, machine learning models, APIs, and applications, to name a few examples.
Regardless of their format, Gartner lays out for key standards that data products must meet:
- Consumption-ready: data products must be trustworthy and reliable upon delivery, as well as findable and accessible by the right users.
- Up-to-date: data products should be regularly maintained, which may require engineering resources and defined SLAs.
- Authorized for use: data products must be appropriately governed via data access controls, data use agreements, and other guardrails as dictated by your organization.
- Quantifiable: data products must be measurable in IT and business terms in order to evaluate their effectiveness, and to perform cost-benefit analyses.
Data Products vs. Data as a Product
Given their similar names, data products can sometimes be confused with the concept of “data as a product.” While data products are self-contained, data as a product is a lens through which to treat data assets, asserting that they should be viewed, designed, and manipulated from the consumer’s point-of-view. This may involve creating data products, platforms, and other tools that make the user experience as seamless and effective as possible. Data as a product is also a core tenet of data mesh architectures.
Characteristics of Data Products
In addition to Gartner’s criteria that we mentioned earlier, data products should possess the following characteristics in order to effectively achieve business objectives:
1. Accessibility:
Data products cannot exist in a vacuum. They need to be discoverable and accessible for the users who can put them to work. Data product details should be well documented in order to make them easily findable, and published in a centralized location. Making the data product accessible also helps ensure that it will be reused, and in turn increase efficiency across the organization.
2. Observability:
While data products aim to streamline processes, they still absorb and can be affected by changes to the underlying data. For instance, data source modifications could disrupt automated processes built into a data product, leading to breakdowns in reporting or inaccurate results. You should ensure that your data products are reliably monitored and adaptable to these changes.
3. Security:
Data security standards apply to data products, just as they do to raw data. Since data products are a combination of assets, their security needs differ from each of those underlying assets. It’s important to apply access controls across data products when they are published in order to ensure that only authorized users are able to access, use, and share them.
4. Quality:
Data products are useless if they produce low quality or inaccurate analytics, so it’s essential to enforce quality control over the data that is fed into the products. With data monitoring and risk detection capabilities, you can ensure that the data fed into and extracted from data products is high quality and usable, while maintaining a holistic view of how data is being accessed by which users.
Common Types of Data Products
What are the most common ways data products are used in practice?
Data Visualization
Data visualization tools transform raw data into visual representations like charts, graphs, and maps, that make the information easier to understand and interpret. Popular visualization tools include Google Looker Studio, Tableau, Sisense, and IBM Watson.
The purpose of these tools is to speed up time-to-insights for data users. A chart depicting patient data is much easier for a healthcare worker to view and take immediate action on than a spreadsheet that requires them to comb through hundreds of rows.
Predictive Models
Predictive models are data products that analyze both current and historical data in order to formulate statistical models and generate data-driven predictions. These models can then be monitored and adjusted as newer, more relevant data is generated.
An example of predictive modeling is an investment firm that creates a data product to analyze past and present trends in order to forecast stock market movements. This model would allow financial advisors to give clients data-based recommendations about how to invest their money.
Search Engines
Most of us interact with search engines on a daily basis, but it can be difficult to determine the best or most accurate information from the sea of results. That’s why search engines create specific data products geared towards quality control processes.
Using machine learning and natural language processing, search engine data products examine trends in both queries and popular content to surface the most current, relevant, and appropriate results. This ensures that the search engine’s results are not stale, outdated, or incorrect – without having to manually scan and interpret vast numbers of web pages.
Artificial Intelligence Chatbots
The rising popularity of artificial intelligence is spurring the generation and rapid adoption of generative AI tools, large language models (LLMs), and AI chatbots. Organizations are also developing proprietary LLMs or adapting existing ones to meet their needs via RAG-based GenAI applications.
Each of these models is built and trained on massive volumes of data, in order to make query responses as accurate and reliable as possible. In this way, they are data products that aim to reduce search complexity and provide relevant and legitimate results to user questions. Where these chatbots have room for improvement is their adherence to the “quality” – not every query returns a reliable or truthful answer, and therefore must be vetted before using them for business decisions.
Putting Data Products to Work
Data products can serve an array of purposes and impact how organizations meet their goals. The previous section listed some of the most common types of data products, but it is by no means an exhaustive list. With the right planning and attention to the details that matter – availability, maintenance, governance, and quantifiability – you can put data products to work in a way that accelerates data sharing, innovation, and business results.
Industry analyst Sanjeev Mohan weighed in on the ways data products contribute to effective internal sharing via data marketplaces – read his blog here.