Companies today are accelerating the movement of data to the cloud. As they do so, new opportunities are emerging to innovate the way they manage that data. Disruptive technologies such as data products are enabling organizations to uncover previously hidden insights within their data so their business can work smarter.
Tamr defines a data product as a consumption-ready set of high-quality, trustworthy, and accessible data that people and machines across an organization can use to solve business challenges. Using the perfect synergy of AI and human intelligence, data products reveal new insights that enable organizations to boost operational efficiency, power exceptional customer experiences, reveal untapped revenue opportunities, and safeguard their business from unforeseen risks.
In its Hype Cycle for Data Management 2023, Gartner defines a data product as “a curated and self-contained combination of data, metadata, semantics, and templates. It includes access and implementation logic certified for tackling specific business scenarios and reuse. A data product must be consumption-ready (trusted by consumers), kept up-to-date (by engineering teams), and approved for use (governed). Data products enable various data and analytics (D&A) use cases, such as data sharing, data monetization, domain analytics, and application integration.”
We agree with this definition.
Gartner goes on to caution companies, warning that “organizations that create one-off data products and don’t plan for management of the entire data product life-cycle usually struggle with data products not delivering value.”
We couldn’t agree more.
In our experience, when organizations fail to comprehend the business requirements and lack a well-defined strategy to oversee the lifecycle of data products, they severely hamper their ability to deliver the transformative impact their organization needs. That’s why it is critical for businesses to invest in a data product strategy and support it with an innovative data product platform.
Implementing a data product strategy brings structure to the ownership, processes, and technology needed to ensure an organization has the clean and trustworthy data needed for downstream consumption.
Knowing how and where to start is the hardest part of implementing a new strategy. But when it comes to getting started with data products, we believe there is a clear blueprint that CDOs and aspiring data leaders should follow.
1. Assess Your Data, Your Organization, and Your Technology
Start by determining if you have the right capabilities in place to implement a data product strategy. On the data side, it is important to understand:
Where does the data live?
Is it integrated across systems and departments?
Is the data accurate and complete?
How often is it updated?
Understanding the answers to these questions will help you determine not just the quality of your data, but also the budget you need, and the number and types of resources it will take to build a high-quality data product.
Next, review your team. You may also find that your current organization lacks the right skills to successfully implement a data product strategy. Identify resource gaps now, and make a plan to fill them.
We find that organizations with data product strategies often employ data product managers who design, build, and manage the cross-functional development of a data platform, or a suite of specific data tools to serve multiple internal and/or external data consumers.
It is also important to have the supporting technology in place to deliver on your data product strategy. Your technology should enable you to easily master your data, enrich it with external datasets, and integrate it across your systems and departments. Without these capabilities, you will struggle to deliver analytical insights or drive operational efficiencies. Tamr blends pre-built machine learning models with human feedback so that your data product delivers the best possible version of data.
2. Define Your Use Case
If you are exploring a data product strategy, it is likely that the leaders of your lines of business, e.g., marketing, R&D, or procurement, have a problem that is preventing them from reaching their respective objectives. Defining a use case is the best way to create a vision for how a data product can help them solve a business problem and reach their objective. It brings clarity to the work you are doing and makes it tangible for the lines of business leaders
To determine the requirements for your data product use case, you will need to ask questions such as:
Why do you need a data product?
What are you going to do with the data product?
Who is going to use it?
Where will they consume it?
What data does it need to include?
Your LOB (line of business) leaders may not know all the answers, but that is where you come in. Connect the dots, identify the data sets, and show them how a data product can help them.
Based on our experience, implementing new data product projects with either new or existing teams tends to be the most straightforward. Migrating from a pre-existing solution adds complexity and generally requires more effort and attention. Furthermore, involving additional parties like systems integrators and/or offshore resources can also add to the complexity of the projects.
3. Develop a MVDP: Minimum Viable Data Product
It is time to start implementing your strategy. Priority number one: developing an MVDP: a minimum viable data product.
Just like in agile product development, it is important to start small, release quickly, iterate, and prove value. Focusing on a specific use case allows you to be nimble. Each time you release a data product, deliver a few more capabilities and a little more value. Not only will it help drive the adoption of your data product but it will also help you secure additional funding, more resources, or better-supporting technology.
Along with this, make sure that you are supporting your lines of business partners so that they understand how they can use the data product in their everyday processes. Show them how they can access approved data products via their analytics tool. Get their feedback on the quality of the data. And use it to help make the next release of the data product even better.
Follow these steps and you’ll be on your way to implementing a successful data product strategy. But remember, like any new initiative, implementing a data product strategy is not without its challenges. Read this post to discover what challenges to watch out for and how to overcome them.
Note: Gartner does not endorse any vendor, product, or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of the Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
About the Author:
Anthony Deighton is the Data Products General Manager at Tamr. He brings extensive expertise in building and scaling enterprise software companies. With a twenty-year career, Deighton joined Tamr as its Chief Product Officer and has played a pivotal role in driving the company's success.
Before Tamr, he was Chief Marketing Officer at Celonis, establishing the company’s leadership in the Process Mining software category and creating demand generation programs resulting in exceptional 130% ARR growth.
He spent a decade at Qlik and was instrumental in transforming the organization from a relatively unknown Swedish software company into a renowned market leader. At Qlik, Anthony held key roles, including product leadership, product marketing, and, ultimately, Chief Technology Officer.