A framework to assess and enhance the maturity of data products

8 min readJul 31, 2023

Data products play a crucial role in today’s data-driven world, providing ready-to-consume assets derived from transformed and consolidated information, turning raw data into actionable insights. The prominence of data products continues to soar across sectors and organizations of all types, but not all of them result in business success. Reviewing successful and failing data products, patterns emerge as to what drives business impact.

In this article, we investigate those patterns and convert them into a practical framework that can be used to assess and enhance the maturity of data products.

What is a Data Product?

A data product is a set of prepared data or information (and hence specifically not raw data) that is ready to be consumed by a wide set of consumers. Data may come from diverse sources and in different formats and is then transformed and consolidated into a new asset that can be consumed by others. Among other things, key features of a data product include that it i s discoverable, addressable, and managed as a product. Also, each data asset must have use cases mapped against it — without a use case mapped against it, it cannot be a data asset.

In this process, three distinct sub-capabilities are required. First, Data Management capabilities are required to label the data, manage metadata, curate it, and possibly measure and remediate data quality, so that it can become a trusted asset. Second, Storage & Processing are naturally required to ingest the data, to store both the raw initial data as well as the enhanced, curated data, and to power the data pipelines, consolidation, and transformation. Third and final, through AI & Data Science capabilities additional insights can be layered on top of the data. This final step is optional — some data products may require a mere consolidation or preparation of data, whereas others might embed advanced analytics or machine learning models.

Why are data products so important?

Data products play a pivotal role in driving value for organizations, offering a myriad of benefits that extend beyond the mere consolidation and transformation of data. These value-driven impacts can be categorized into three primary categories:

Source: What’s the big deal about data products?

Use Case Enablement: At the heart of data assets lies the principle that they are meant to enable consuming business processes and activate the use cases within them. By providing readily available and curated data, these products empower users to derive actionable insights. Depending on the nature of the data product, it enables organizations to better understand customer needs, make informed decisions, drive revenues, drive operational efficiency, and mitigate risks.

Data Governance Streamlining: Data governance controls can be implemented within and around the data product, which given its highly reusable nature is a very strategic location to do so. With stringent controls and labeling mechanisms in place, data products ensure that content is accurately labeled, tightly controlled, and of high quality. By providing a trusted distribution point for datasets, data products minimize the need for multiple data checks while maximizing the impact of these data controls.

Landscape Rationalization: Many organizations have no clear and accurate picture of their systems and applications, and their exact data footprint, leading to data duplication across various locations and unnecessary expenditures on redundant tooling. As you activate more data products and drive consumption from them, invariably this provides opportunities for other datasets and processes to be removed, driving cost savings. By ensuring that data products are mutually exclusive and collectively exhaustive, organizations can avoid data fragmentation and achieve a cohesive and unified data strategy.

What is data product maturity? What are its dimensions?

The value derived from a data product is directly proportional to its level of maturity. Data product maturity can be defined as the extent to which a data product is strategically established and equipped with the necessary features to maximize the extraction of value from data. By attaining a higher level of maturity, a data product becomes a more robust and refined asset that facilitates seamless data extraction, empowers informed decision-making, and drives transformative outcomes. Let us take a closer look at these dimensions:

Awareness. The extent to which there is awareness among existing and potential users and what it can be used for. If people don’t know it exists, they can’t use it.

Usability. It is easy to find, access, and then use for anyone with a use for it and the right access rights. Access is self-service and immediate.

Interoperability. It can be coupled with other internal and external data, as well as AI, analytics, and visualization capabilities as needed. Data and insights are easily fed into use case applications.

Actionability. The data and derived insights are directly applicable for, and connected to, well-understood use cases with precisely articulated input needs.

Speed. It enables consumers to rapidly find, access, understand, and use it to make decisions and drive actions at speed. It is immediately ready for use.

Innovation. It drives innovation as consumers can experiment, relate it to other data, and apply data science to rapidly test value-driven hypotheses.

Trust. It is reliable, secure, and quality-controlled. It is available for consumers in a safe experimentation environment to discover if it can be of use.

Adoption. It is used across critical domains and processes and referenced in socialized success stories. There is evidence demonstrating user adoption and value creation.

Business Impact. There is a demonstrable, quantified impact that the data product has on the organization through specified use cases and impact statements.

Product Orientation. The asset is managed as a product in that it focuses on customers and their needs, taking an iterative lifecycle approach to drive continuous improvement and value.

These dimensions “multiply.” That is, maturity needs to be high (or of a minimum maturity) in every single dimension. If maturity is low in just a single dimension, this will depress the impact it can have on the enterprise.

Applying the framework

By assessing and improving these dimensions, organizations can unlock the full potential of their data products, enabling them to deliver actionable insights, drive innovation, and achieve significant business value. They form the basis of ZS’s data product maturity framework and provide a structured approach to evaluate the level of maturity and effectiveness of data products. By leveraging this framework, organizations can gain highly tactical insights into the strengths and areas for improvement within their data products.

To illustrate the practical application of the data product maturity framework, we will examine and assess maturity of three data products and understand how the framework identifies the current level of product maturity and provides a roadmap for optimizing each product’s potential.

Data Product 1: This product has low adoption despite users being aware of the product and the means to access it. This is due to the absence of clear product use cases and potential business impact. There is a need to identify clear business use cases, define features and the value add.

Data Product 2: The product has high-potential impact for its users, is actionable with ease of application and integration, however, this value is not well advertised to the audience. Recommendation here would be to enhance the data quality and market the product to the broader audience to create more visibility.

Data Product 3: This product ranks high on awareness and adoption with data and insights being very well connected to the use cases, resulting in high potential impact for the users. However, it is difficult for the users to access the platform and it is often under maintenance. There is an urgent need for infrastructure enhancements to ensure data can be accessed across different platforms and apps.

As you can tell, assessing the data products along the 10 maturity dimensions drives the identification of highly tactical opportunities for improvement. Once you know what is wrong with it, it’s straightforward to identify how it can be fixed.

After assessing your portfolio of data products based on the above-described maturity framework, you can start mapping the data products onto a 2x2 matrix, with the axes describing the potential impact for the enterprise and the size of the gap. Your initial focus should be on those products that can drive high value to the business yet are low on maturity, as the ROI on investment will typically be highest here.

Activation Enablers: How can this be enabled in organizations?

There are 4 key enablers that can make activation of data assets smoother for organizations:

Strategy & Governance . To have the desired impact of a data product it is essential it to be explicitly referenced as a part of Data Strategy. Organizations need to define why it the data product important, how is it going to be part of the data governance landscape, how is it a part of the architecture. Once defined, as a next step think about what’s the governance that comes with it.

Reference Architecture. It is recommended to have a common blueprint to drive a consistent implementation and drive synergies, for example by rationalization the technology stack. Instead of having every data product owner reinvent the wheel, a reference architecture can provide the respective product team with a “menu” of sorts, providing “ingredients” of data ingestion, storage, processing, governance, and science applications.

Interoperability Standards & Tooling. It is very critical to standardize the interoperability of data products so that it is consistently available and reliable, and that it is able to be accessed by all users. This includes providing the necessary tooling such as an API platform or basic ETL capabilities, and to incorporate these in the reference architecture, which in turn helps drive consumption.

Data Management Hub. Data Products should have a minimum amount of data governance and controls implemented. If they are designed in alignment with your reference architecture, data management capabilities could be deployed from a data management hub, for example driving automated cataloguing of the products and the underlying metadata.

Closing

Our Data Product Maturity Framework provides the key dimensions that determine the maturity of a data product. By assessing these key aspects, organizations can identify areas of improvement and develop targeted plans to prioritize and enhance their data products. Through our three examples, we have seen how analyzing product maturity can reveal opportunities for growth, whether it involves defining clear business use cases, enhancing data quality, or increasing user visibility. As the data landscape continues to evolve, focusing on data product maturity becomes essential for organizations aiming to harness the full potential of their data and drive meaningful insights and value.