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Editor’s note: Your inbox is filled with articles promising the ‘next big thing’ in data management. To make cutting through the clutter easier for you, we bring top 4 data management trends that are all set to make the data strides in 2025.
Before we get into predictions and emerging trends, let’s address the fundamental shift that’s driving every trend in data management. The transformation of data from business byproduct to a strategic asset. Think of it like the oil boom of the early 1900s.
At first, data was seen as a byproduct of business operations - something to be managed and stored. Today? It's the new currency of business success. Here's how this transformation is playing out:
Three Fundamental shifts in Data Management
Shifts | Old | New |
---|---|---|
Question Shift | Where should we store all this data? | How can we use this data to create value? |
Investment shift | How can we minimize data cost? | How can we maximize return on data investments? |
Responsibility Shift | IT manages our data | Everyone is responsible for their data |
Now, why this matters is because these shifts have become the foundation of how organizations view data - which provides the context for every major trend we're seeing.
As we look forward, several exciting trends are set to redefine how your management your data. So, without any further ado let’s get into it.
The interplay between AI and data management is evolving into an interesting partnership that could redefine how CDOs approach their enterprise data strategies. As much as data management principles and architecture contribute to AI & GenAI (LLMs), data management also benefits from AI techniques. And it’s not only about tackling the long-standing data governance and quality challenge. What we see it is, as an opportunity for efficiency and insights.
The emergence of use cases like text-to-SQL creation, faster data dictionary creation, autonomous database management, vector storage, processing to support advanced use cases, augmented data management, integrated data quality checks, execution of embedded AI/ML models within the database and enhancement of metadata within data catalogues are all set to transform your data management practices. How?
Through better data discoverability and usability. This will help you to provide the right access to the right people for the right use cases, empowering them to make informed decisions based on the data at hand. It’s like having a smart assistant that knows exactly where everything is and can provide context at a moment’s notice. And who wouldn’t like that, right?
What started with Uber and then Netflix, followed by many fortune 500 companies as a better data integration initiative - now has become a data management trend. In fact, the need to establish a strong, integrated data platform that enables enterprises to maximize the advantages of increasingly diverse and voluminous data is now seen more than ever with in the cloud, accommodates massive data of all types, including audio, visual, and text, with no schema enforcement.
And the evolution of the data architectures - from data pools to data warehouses and data lakes, and now the data lakehouse is the testament to that. And what's particularly fascinating is how AI/ML initiatives are accelerating this adoption. The lakehouse's ability to handle both structured and unstructured data while maintaining ACID compliance makes it uniquely positioned for the AI era.
But what's driving this adoption? It’s the maturation of open table formats like Apache Hudi, Delta Lake, and Apache Iceberg has effectively bridged the gap between traditional data warehousing and modern data lake capabilities. But perhaps the most compelling trend is the shift in how enterprises view their data infrastructure investments.
The Total Cost of Ownership (TCO) narrative has become increasingly important, with organizations reporting 25-35% cost savings through storage optimization and reduced data movement. Add to this the ability to support real-time analytics – critical for industries from retail to manufacturing – and you have a clear picture of why 2025 is poised to be the year of the Lakehouse.
Hence for technical leaders evaluating their data strategy, the lakehouse architecture presents a rather convincing option for modernization. And the cherry on the top is its ability to scale seamlessly. What excites us about this trend is how quickly organizations are beginning to realize the transformative capabilities of data Lakehouse’s. As they embrace this architecture, we anticipate a shift toward more agile decision-making processes driven by real-time insights and advanced analytics.
This one in the list is no surprise – especially when we see majority of enterprises operating in multi-cloud settings. Subsequently organizations which are working with a combination of old and new technologies (especially for industries like Retail or CPG that actively use customer data from various touchpoints like social media, CRM platforms and other internal/external sources) are seeing themselves in a pickle.
To ‘balance the traditional data management and AI readiness’ scenario that CDOs are in, many companies have initiated their journey toward a more integrated data management approach, the next phase will involve significant advancements in data fabric capabilities. We can expect innovations such as self-healing metadata ecosystems and autonomous data discovery to become standard features.
So, what does the future of data fabric look like? It’s all about speed and intelligence. Organizations will benefit from faster analysis by leveraging in-memory processing to store frequently accessed data, significantly reducing latency compared to traditional disk I/O operations.
When it comes to Master data management, we have seen bicycle to a tesla worthy upgrade. Why we say this is because organizations that have long relied on various conventional methods to manage their master data, are now shifting from traditional Excel spreadsheets to more sophisticated Service-Oriented Architecture (SOA) systems. This means moving away from outdated overnight batch processes to systems that can match, merge, and maintain golden records in real time with remarkable accuracy.
What does this mean for your company? It means reduced time to uncover better data relationships, patterns, insights that would have taken human data stewards’ weeks to identify.
Adding to this exciting trend are low-code/no-code platforms like MDM 360, which are truly game-changers. They open the "MDM gates" for business users, allowing people without extensive technical backgrounds to actively participate in data management. This democratization makes the whole process quicker and more intuitive, enabling organizations to adapt easily to changing needs while ensuring that solutions work seamlessly across various platforms.
Plus, by integrating SOA principles into MDM, companies gain flexibility and re-usability of data services throughout the organization.
So looking ahead, the future of MDM looks bright. With projections for growth of the global MDM market showing a CAGR of 12.9% between 2023 and 2030, it’s clear that demand for effective data management solutions is on the rise. Because for the organizations managing complex data environments across multiple departments or global operations, modern MDM has become a must-have for keeping data quality and consistency in check. And hence it’s safe to say we have entered ‘golden’ period of MDM adoption.
Now that we’ve explored these trends, it’s clear that data management isn’t just about technology; it’s fundamentally about creating real business value. However, the truth is that transitioning to a more integrated and effective data management strategy can be quite complex. You might encounter challenges that could undermine the benefits you’re aiming for. It’s not always a straightforward journey, but don’t let that drag you down! (When in doubt – reach out to an expert like Polestar solutions).
All we have to say to you is the time for action is now. CDOs must prioritize enhancing their data readiness to harness these trends effectively. Embrace the future—it's time to make your data work for you!
About Author
Information Alchemist
Without data you are just another person , with an opinion.