What is a Data Product Strategy?

The value of data has never been higher. And as AI takes hold, data’s value - and power - is only going to grow. Organizations that successfully harness the power of data will boost operational efficiency, deliver exceptional customer experiences, uncover hidden revenue opportunities, and avert risk. But those who don’t will experience disruptions in operations, lackluster customer experiences, the loss of competitive edge, and reputational harm. 

To succeed in today’s dynamic, AI-powered era, businesses must shift their focus away from traditional, master data management solutions. Instead, they must embrace data products. Why? Because a well-defined data product strategy is a game-changer, enabling businesses to take full advantage of their data and use it to gain a competitive edge. 

‍What is a data product strategy?

Simply put, a data product strategy brings structure to the ownership, processes, and technology needed to ensure your organization has clean, high-quality data. A data product strategy focuses on how the organization uses and consumes data. And when an organization has one in place, it signals a true commitment to treating data as a valuable asset.

Companies with successful data product strategies often employ the role of data product owner to manage their data products, similar to the way other organizations hire product owners to manage their software solutions. It’s a newer role, but one that is becoming more popular as data consumers are embracing the idea of data products. Their job? To focus on how users throughout the organization use and consume the data, similar to how product owners focus on the usage of their product. 

A data product strategy also recognizes the need to organize data products around key, logical entities, such as customers, suppliers, and patients. Having a well-organized structure for data products is critical, as most organizations will have hundreds of data products that deliver to their data consumers. 

Successful data product strategies also employ the right AI-powered data management capabilities such as a data product platform, AI/ML mastering, data quality capabilities, and data enrichment to ensure that the organization's data is clean and integrated.

‍Benefits of a data product strategy

Today, every business is a data business. And implementing a data product strategy through the design and use of data products accelerates your organization’s ability to realize greater value from your data. Said differently, if your organization wants to become truly data-driven, then you need a data product strategy. It’s that simple.  

When you have a data product strategy in place, your organization will realize a myriad of benefits including:  

• Better business intelligence:
  Deliver the best version of your data for use in BI, analytics, and dashboards.
• Exceptional customer experiences:
  Understand your customers, inside and out, so you can deliver the experiences they’ve come to expect.
• Optimized operations:
  Boost operational efficiency and drive greater ROI so your business runs better.
• Revenue gains:
  Uncover new revenue opportunities by revealing previously-hidden relationships in the data.
• Increased productivity:
  Improve the productivity of your data team so that business users can get answers to their questions faster. 
• Agile decision-making:
  Eliminate disputes over data because everyone is on the same page.
  

Moreover, investing in a data product strategy prevents your data from becoming stale. Today, much of the data entered into your enterprise systems is incorrect or incomplete. And when you leave that dirty data unattended, the situation becomes even worse. By implementing a data product strategy, you’re signaling that managing and cleaning data is part of everyone’s job, not just the responsibility of the data team. 

A final benefit of implementing a data product strategy is the elimination of data silos. If your business is like most businesses today, you have data silos. And these data silos reflect the structure of your organization and the systems you use to create data. By implementing a data product strategy, you can break down data silos by orienting your data products around consumption and use. Doing so makes it easier to use data products to integrate and enrich your data across these disparate systems, ensuring that it’s always clean, always up-to-date, and always ready for use in decision-making. 

‍How to build a data product strategy

Getting started with data products may seem like a daunting task. That’s why we’ve created a blueprint for CDOs and aspiring data leaders to follow. Doing so will help you avoid the pitfalls many organizations face when implementing a data product strategy. 

In short, we suggest that you:

1. Know your why
2. Assess your data, your organization, and your technology
3. Define your use case
4. Secure buy-in and budget
5. Develop a MVDP: minimum viable data product

To dig deeper into the step-by-step instructions for how to create and implement a data product strategy through the design and use of data products, download Getting Started with Data Products: A CDO’s Guide. In it, we dive into each of the five steps to help you get started. Follow them, and you’ll be on your way to implementing a successful data product strategy for your organization. 

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