Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Manish Sood, CEO, Founder and Chairman, Reltio
As the pace of innovation accelerates, data unification and management are more critical than ever for organizations aiming to thrive in an increasingly AI-driven economy.
Here are my thoughts on the industry’s direction for 2025:
1. Data strategies will center around security and sovereignty
In 2025, the dual pressures of rising cyber threats and stricter data sovereignty laws will force enterprises to rethink their data unification strategies, prioritizing secure, localized, and compliant data management solutions.
The frequency and sophistication of cyberattacks are increasing, targeting fragmented and poorly protected data systems. At the same time, governments worldwide are implementing increasingly strict data sovereignty regulations, requiring enterprises to store and process data within specific geographic boundaries.
In addressing these challenges, organizations will adopt data unification platforms that incorporate robust cybersecurity measures and localized data architectures. These solutions will provide encryption, granular access controls, and real-time threat detection while ensuring compliance with evolving data sovereignty requirements.
2. Say goodbye to application-specific SORs as the foundation for AI
The rise of generative AI (GenAI) is reshaping how enterprises think about their data. Organizations racing to implement AI-driven insights and automation are realizing that application-specific systems of record (SORs) are no longer sufficient. These isolated systems-built to serve narrow, use-case-specific needs-trap valuable data in silos, limiting its potential and creating inefficiencies that stifle innovation.
To harness the full power of GenAI, enterprises will increasingly shift toward unified data systems of record that provide a single, comprehensive view of their data across all applications and functions. These systems act as enterprise data hubs, integrating structured, unstructured internal data and external data into a contextualized, AI-ready foundation.
GenAI thrives on data enriched with context. Unified systems provide a holistic view of entities-customers, products, suppliers-ensuring GenAI models are trained and deployed with high-quality, relevant data. By consolidating data across applications, enterprises can unlock previously hidden insights, fostering cross-functional collaboration and enabling more accurate decision-making.
A unified SOR simplifies AI implementation, allowing enterprises to scale GenAI solutions without the complexities of managing fragmented data pipelines.
This shift will be driven by the need for better AI outcomes and by economic pressure. Enterprises cannot afford the inefficiencies of maintaining multiple disconnected systems of record in a world that demands agility and speed. Unified systems will become the standard for enterprises looking to leverage their data as a competitive advantage.
This transformation will mark the end of application-specific data silos and usher in a new era of connected intelligent enterprises.
3. Real-time, trusted data will keep enterprises competitive
Enterprises will increasingly demand real-time, trusted data to fuel AI applications, shifting expectations away from batch processes and static reports. Business-to-consumer (B2C) enterprises need real-time data to stay competitive because consumer behaviors can shift instantly.
Black Friday, for example, is no longer a single-day event-it is a testing ground for year-round agility. Retailers that rely on real-time data can dynamically adjust offers, promotions, and inventory based on customer demand and market conditions as they happen. Legacy systems and processes often deliver outdated insights, making it difficult for enterprises to respond to rapidly changing market dynamics, customer demands, or emerging risks.
The B2C industry will adopt real-time data unification as a baseline requirement, enabling organizations to make decisions at the speed of business. Technologies like event-driven architectures and real-time streaming data pipelines will become table stakes for enterprise data leaders. Embracing real-time data is not simply a technology upgrade but a shift in how they operate, strategize, and connect with customers.
4. Data unification solutions will be crucial in M&A synergies
As the merger and acquisition (M&A) landscape rebounds after years of stagnation, enterprises face the monumental challenge of unifying data from different systems, formats, and governance policies. Business leaders and investors often overlook the risks of siloed, fragmented data in M&A. Merged entities often inherit fragmented and siloed data ecosystems, which can delay operational integration and reduce the value of a transaction.
Data unification is a key enabler of the synergies that M&A activity seeks to realize over time.
Data unification will emerge as a critical enabler of post-merger success. Enterprises will turn to modern data management platforms to rapidly integrate and harmonize data across systems, creating a single source of truth. This capability will be essential for both operational efficiency and for delivering on growth promises, such as improved customer experiences, expanded market reach, and accelerated innovation.
Companies that invest in advanced data unification tools during the M&A process will gain a competitive edge, as they can execute faster integrations, uncover insights from combined datasets, and deliver tangible value to stakeholders more quickly than their competitors.
5. Expect more regulations around AI and data
Governments and regulatory bodies in the U.S. and worldwide are already stepping in to establish guardrails that ensure AI is ethical, transparent, and accountable. What will become even more evident in 2025 is that AI regulation is not a question of if but when … and how much. In the U.S., the patchwork of state-level laws and federal guidelines will further coalesce, and we may see more defined, technology-specific regulations emerge at the federal level. Europe, through frameworks like the EU’s AI Act, has already taken significant steps toward regulating AI development and deployment, setting a global standard for oversight. Other regions, including Asia, are following suit, with stricter compliance requirements on the horizon.
Compliance with AI regulations will require robust governance frameworks, and at the heart of those frameworks lies trusted, unified, and well-managed data. Without proper data governance-encompassing security, lineage, quality, and transparency-AI governance becomes nearly impossible to achieve. Enterprises will need to prioritize solutions that allow them to manage, unify, and monitor their data ecosystems in real-time, not only to fuel AI innovation but also to maintain compliance with emerging laws.
The regulatory environment will force enterprises to think strategically about their data, shifting from fragmented silos to a cohesive, governed foundation that meets both operational and compliance needs. Ultimately, the expansion of AI regulations will elevate the importance of data governance from a best practice to a business imperative, reshaping the landscape for AI-driven enterprises. Those who invest in this now will not only reduce risk but also unlock the full potential of AI while maintaining public trust.
6. Enterprise AI adoption will stall without unified data
In the coming months, many enterprises will continue struggling to scale AI due to fragmented, incomplete, and untrusted data. While AI solutions excel at extracting insights, their effectiveness depends entirely on the quality and comprehensiveness of the data they are fed.
Enterprises often operate with data locked in silos-across legacy systems, disconnected platforms, and scattered business units. This fragmentation results in inconsistent, stale, or conflicting data that undermines the performance of AI models. Without looking for ways to obtain a unified view of their data, organizations face significant barriers in deploying AI effectively:
- AI Models Underperform: Incomplete or outdated data leads to inaccurate predictions and diminishing trust in AI outputs and adoption.
- Operational Bottlenecks: Enterprises waste resources trying to stitch together data manually, slowing AI deployment and reducing ROI.
- Compliance Risks: Fragmented data creates gaps in governance.
Final Thought
The data unification and management industry stands at a pivotal inflection point. In 2025, leaders who embrace these trends will unlock the full potential of their data, drive AI adoption, and position their organizations for long-term success. Those who fail to act risk falling behind in an increasingly data-driven world.
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ABOUT THE AUTHOR
Manish Sood is the CEO, Founder and Chairman of Reltio, the first cloud-native, software-as-a-service (SaaS) data platform. An entrepreneur with a vision of the big-picture ways data can drive business and industry transformations, Manish founded Reltio in 2011 to help organizations accelerate the value of their data and deliver on business outcomes. Since its inception, Manish has led Reltio’s evolution from the concept stage to a high-growth company.
Manish previously led product strategy and management for the Master Data Management (MDM) platform at Informatica and Siperian. During his career, Manish has architected some of the largest and most widely used data management solutions used by most Fortune 500 companies today. Manish holds a bachelor’s degree in mechanical engineering from Andhra University College of Engineering.
