How to Overcome 4 Common Obstacles in a Data Product Strategy Implementation

Data products hold the potential to deliver significant value to an organization. In fact, in its Hype Cycle for Data Management, 2023, Gartner cites value drivers including speed, scale, trust, monetization, and agility as just a few of the many benefits. If you’re looking to get started with data products, we suggest you start by reading this post. 

If you’re already implementing data products, then you know that challenges and obstacles are inevitable. But knowing how to navigate around roadblocks is key to a successful data product strategy implementation. Gartner highlights a number of common challenges in the aforementioned Hype Cycle. And we’re adding to their list by sharing four common obstacles we’ve seen organizations encounter, as well as ways to successfully overcome them.

Obstacle #1: Lack of Data Integration‍

Data silos are a pervasive problem, even at the most data-driven companies. But integrating data in order to gain a holistic view across the business is a challenge. Doing so requires organizations to employ data products to curate, standardize, match, and enrich records across disparate systems and silos.

‍‍To overcome a lack of data integration, organizations need an innovative AI-native master data management (MDM) platform and a data product strategy that enables them to tap into external data sources to establish data standards. Using unique IDs, companies can then verify firmographic details and normalize information across their systems, giving them a holistic view of key entities such as customers, suppliers, healthcare providers, etc.

Obstacle #2: Inability to Scale‍

Delivering clean, trustworthy data is one thing. Delivering it at scale as the volume of data accelerates is another. Doing so requires companies to move away from traditional, rules-based master data management solutions. 

Using the perfect synergy of AI and human intelligence, data products make it possible for organizations to deliver trustworthy data at scale, which is a game-changer when it comes to moving your business forward. Not only do data products enable businesses to transform data fast, but they also allow them to connect the dots across disparate data sets so they can compare, score, and master diverse data sets at scale. 

‍Obstacle #3: Insufficient Governance‍

Data governance is a must-have in today’s complex data ecosystem. And while source-based governance has been the focus for many organizations, many are finding it’s no longer enough.

Today, we’re beginning to see organizations shift their focus to consumption-based governance —and this shift makes sense. Because data products are a consumption-ready set of high-quality, trustworthy, and accessible data that people across an organization can use to solve business challenges, shifting governance to be consumption-oriented as well is a logical step.

Obstacle #4: Assuming You Need Customization‍

Many organizations assume that they need to customize data products. But they make this assumption before they even look at the data. And while every organization is unique, many perceived customizations for standard entities such as customers, suppliers, or patients are simply not required. 

To avoid the trap of over-customization, it’s important to run data through an uncustomized data product first. Review the results in the data product or an analytic tool. You may be surprised by the accuracy of the results. And while it’s possible that you may still need some level of customization, it’s likely that you don’t need as much as you think.

Overcoming these obstacles is challenging—but not impossible. It requires shifting your mindset, embracing agility, and selecting the right AI-native MDM solution to deliver data products that represent the best version of your enterprise data. To learn more about how AI-native MDM can support your data product strategy, please download our ebook, Golden Records 2.0: The AI-Native MDM Advantage.