5 Data Management Tool and Technology Trends To Watch in 2025

The market surrounding data management tools and technologies is quite mature. After all, the typical business has been using data extensively to help streamline its operations and decision-making for years, and many companies have long had data management tools in place.

But that doesn't mean that little is happening in data management technologies. On the contrary, as we head into a new year, plenty of change is afoot. And while no one (not even the best AI models) can predict the future with total accuracy, it does seem a safe bet that we'll see some key transformations within the data management tool ecosystem over the year to come.

To that end, here's my take on what to expect from data management tools and technologies in the year ahead.

1. Data tools will better align with diverse business needs

When businesses first began deploying data management tools, their main goal was often to centralize and bring order to the vast quantities of data they owned but were struggling to manage. They wanted to be able to track their data from a central hub, which is why data lakes and data warehouses entered the picture.

But now that many organizations have tamed that chaos, they're turning to data management tools for a different, more sophisticated purpose. They want to give each unit within the business access to the data it needs on the terms it needs.

This requires a more complex and decentralized approach to data management — one powered by tools like data meshes and data marts. Central data repositories won't go away, but they'll increasingly be accompanied by data tools and platforms that better align data with diverse business needs.

2. Increased focus on data transformation

Along similar lines, businesses with established data infrastructures are increasingly expecting their infrastructures to do more than just store data and make it available for analysis and reporting. They also want to be able to transform data — which means restructuring, cleaning, validating or otherwise processing it in ways that improve its quality and increase its value.

For this reason, expect to see data management tools offer more complex data transformation capabilities in 2025 and beyond. We're already seeing this from vendors like dbt, and this trend will also extend to others in the coming year.

3. A practical approach to data quality

"Data quality" has long been a buzzword. Most businesses with mature data management strategies understand the importance of ensuring that the data they use for analytics or to power AI apps and services, must be high in quality. You don't need a Ph.D. in data science to understand the "garbage in, garbage out" concept.

That said, traditional approaches to data quality have mainly focused on implementing governance policies, not automating data quality policies. Companies have established rules about which data quality standards they expect engineers to uphold, but they've left it to the engineers to figure out how to apply those standards.

However, I'm starting to see this changing as data management tools become more adept at enforcing data quality rules. This is due partly to the data transformation capabilities I mentioned above since improving data quality is often one goal of data transformation. But it also reflects a growing awareness that automating data management, including data quality assurance processes, is critical for getting the most from data management tools.

4. Data management tool consolidation

Traditionally, businesses have used different tools for each step of the data management process. They used one solution to warehouse data, another to prepare it, another to analyze it and so on. In other words, they took a "point" approach rather than a "platform" approach.

But we're now seeing a greater focus on consolidation. Businesses are placing increasing value on data management platforms that provide all the capabilities they need without requiring them to purchase and manage disparate tools.

That said, it's essential to remember that flexibility and modularity will always be important components of a modern approach to data management. Organizations may appreciate the simplicity of consolidated data management platforms, but they'll still expect to be able to deploy the tools of their choosing when necessary, and they will resist being locked into a single vendor's platform or ecosystem.

5. A multicloud-friendly approach to data management

Gone are the days when the typical business used just one cloud or other IT platform. Today, companies of significant size almost inevitably rely on multiple clouds, particularly because different units within the business might prefer different solutions or find more value in one cloud than another.

For this reason, data management tools will increasingly need to be friendly toward a multicloud approach. Solutions that only work with AWS or only with GCP, for example, will struggle to remain competitive in 2025 as businesses seek more flexibility.

Conclusion

In short, expect 2025 to be the year when data management tools and technologies evolve into "next generation" solutions hallmarked by advanced capabilities in areas like data transformation and data quality assurance. Expect as well that the typical tool or platform will be more adept at aligning with diverse business needs and at being cloud- and vendor-agnostic.

None of these trends are radically new; solutions that offer the features I've described above already exist. But in 2025, they'll start becoming the norm, not the exception.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Alexey Surgay