Just like 2023, 2024 was a dynamic year for enterprise data management, and 2025 is shaping up to bring even more change. Generative AI is becoming central to how enterprises manage and use their information, and the mushrooming influence of agentic AI could be even more demanding and disruptive going forward.
In this context, savvy enterprises have increased their focus on data quality, because they realize that AI is only as effective as the data it uses — like how a chef depends on good ingredients to make a great dish. This makes ensuring data accuracy and reliability a top priority, because it’s not just about collecting massive amounts of data, but about gathering and using the right data.
Over the past year, enterprises have reevaluated data management strategies to unlock AI’s full potential, and a range of IT vendors have played significant roles in supporting this shift. They have introduced new tools and frameworks to help organizations tackle data quality, change management, data governance and the complex interactions between data and enterprise systems.
Let’s look at the critical areas where organizations are looking to create more value from their enterprise data, and review what key vendors are doing to enable them.
The Need For Data Quality — And Stronger Connections With Enterprise Systems
When it comes to data management, clean and accessible information is the backbone. I’ve seen how addressing data quality and integration issues can turn messy, siloed systems into sources for actionable insights that drive better decisions. Many vendors like SAP, Databricks, Informatica and Cloudera help customers improve data quality with integrated solutions that create unified data views, improve collaboration, reduce errors and prepare data for advanced analytics.
In the past year, I have noticed a significant trend: the push for stronger connections between data and enterprise systems. Hybrid data platforms are enabling real-time data flow across ERP, SCM and other systems. For industries such as manufacturing, having a single view of production schedules, inventory and supply chain logistics makes a real difference because it allows for faster, smarter decision making and a lot less guesswork.
Take, for example, a manufacturing company that uses IoT sensors to monitor production equipment. By connecting these sensors to the ERP system, real-time data about machine performance can automatically update various aspects of the business, informing production schedules, maintenance requests, inventory forecasts and even financial projections. This flow of information across departments breaks down data silos and creates a current, unified view of operations.
Change Management Is About People And Processes
Not coincidentally, the effectiveness of these integrations always ties back to change management. No matter how advanced the technology, it’s only as good as how well people can use it and how much they can trust the data it provides. In other words, adopting new technologies isn’t just about the tools — it’s about the people and processes. I have consistently voiced how strongly I feel that change management is vital in making these transitions work. Without it, you’re likely to see resistance from teams, gaps in training or misalignment between IT and business goals, all of which can slow down projects or limit their impact.
The enterprises that get it right are the ones that focus on clear communication, phased rollouts and training that makes sense for their teams. The vendors that get it right provide thoughtful onboarding programs and tools to help make this so. It’s not just about implementing a tool, but ensuring that everyone is ready and equipped to use it effectively.
Data Governance Is A Critical Component For AI
The rapid rise of AI is also transforming how organizations approach data governance. As businesses increasingly rely on AI, the importance of ensuring data quality, security and compliance has never been greater. Automated governance tools are stepping in to fill this need, offering solutions that monitor AI systems and the data they use in real time.
At the same time, enterprise data management platforms are being held to higher standards for governance. They’re now expected to provide features that track data usage, enforce access controls and ensure compliance with evolving regulations. As AI adoption continues to accelerate, it’s clear that the way we govern and manage data will need to keep pace.
How Top Vendors Are Approaching Data Management
Let’s take a look at the most important vendors in enterprise data management, going category by category.
Large Cloud Service Providers
- AWS — The biggest CSP offers a good example of how cloud-based data management tools have become more accessible. Amazon SageMaker simplifies data management by bringing together data discovery, governance, collaboration and automation in one place, making it easier to access and process datasets for machine learning projects. Amazon DataZone simplifies data sharing, governance and analysis, making it easier for organizations to manage and utilize their data effectively. By lowering technical barriers, Amazon’s tools help companies innovate without needing large in-house AI teams to get more value from their data while maintaining control over governance and security.
- Microsoft — I recently published an article in Forbes about Microsoft Fabric, examining how it advances Microsoft’s approach to data management and AI integration. Fabric consolidates multiple Microsoft data services — Azure Synapse Analytics, Power BI, Azure Data Factory and Azure Machine Learning — into a single ecosystem. A key component is OneLake, a unified logical data lake designed to streamline data storage and accessibility. The platform’s deep integration with Azure AI services and Microsoft Copilot enhances automation and analytics capabilities, while its alignment with Microsoft Purview strengthens data governance and security.
- Google — Google’s unified view across cloud environments helps businesses reduce compliance risks and manual data management efforts while speeding up AI model deployment. Recent enhancements include updates to BigQuery to support faster, cost-effective analysis of large datasets. Dataplex simplifies data governance by automating discovery, organization and security, and Vertex AI works with Dataplex to track data from processing through AI model deployment, ensuring compliance and responsible AI use. Looker will soon be integrated with Dataplex; this should improve data visualization by bringing BI assets, data and AI resources together.
Other Major Platform Providers
- Oracle — The database and ERP giant offers a full spectrum of services supported by Oracle-managed data centers globally. Besides its enterprise applications and IaaS offerings, it supplies customers with AI-powered databases, analytics and development tools via PaaS, plus its Oracle Data Cloud helps turn large datasets into insights for businesses. The latest Oracle Database 23ai includes a number of data management enhancements, including AI Vector Search, which finds results based on meaning rather than exact keywords.
- IBM — The company is enhancing its data management capabilities by improving its data fabric and the watsonx.data platform. Integrating DataStax’s NoSQL and vector search features allows for better handling of both structured and unstructured data across hybrid cloud environments. The result is faster data processing, improved AI model retrieval and more efficient AI-driven workloads, which means quicker access to insights, better scalability for AI applications and stronger data security and compliance. This aligns with IBM’s larger data and AI strategy to create an integrated environment that simplifies data management while realizing the potential of AI technologies.
- SAP — With its Business Data Cloud, launched in Q1 2025, SAP is making waves in enterprise data management and positioning itself as a strong competitor to established offerings from Oracle and Salesforce, with a focus on integrating structured and unstructured data from SAP applications and external sources. Through a strategic partnership with Databricks, BDC enhances data accessibility and governance for SAP’s cloud-based ERP offerings. It also improves Joule, SAP’s AI assistant, simplifies data integration, reduces storage costs, provides AI-driven analytics and helps customers consolidate data from multiple sources, cutting down the time spent on data preparation.
- Salesforce — Data Cloud is a comprehensive platform that brings together different types of data such as customer interactions, transactions and external signals in one place. An important part of this platform is Agentforce, introduced in August 2024, which leverages AI to help create detailed customer profiles so sales, service and marketing teams can deliver more personalized and timely experiences. Data Cloud’s capabilities extend beyond traditional data management, incorporating unstructured data processing, including audio and video content, to provide deeper customer insights and enable more contextually aware AI agents.
- ServiceNow — The company’s Now Platform uses a unified architecture and single data model to simplify application development and integration. The introduction of RaptorDB, an in-house database, addresses scalability challenges often encountered in unified systems. ServiceNow strategically aims to eliminate “swivel-chair” operations by serving as the unified front-end for processes across multiple enterprise systems, aligning with the company’s goal of streamlining workflows across organizations. The company’s planned acquisition of Moveworks should enhance its AI capabilities, making it easier for employees to find and use data across different systems.
- Adobe — The company offers an integrated suite of tools to improve how businesses create and distribute content. In support of this, its Customer Data Platform consolidates customer information from various sources, allowing for more targeted marketing and customer service. This data management approach aims to improve efficiency and collaboration while maintaining strong data governance and compliance. The company continues to incorporate more AI capabilities, improve interoperability among its products and enhance reporting features to support enterprise marketing functions.
Data Specialists
- Cloudera — The Cloudera Data Platform combines data storage, processing and analysis tools in one place, helping organizations manage data across on-premises, cloud and hybrid environments and remote devices. CDP works with major cloud providers as well as on-site systems and is based on open source technologies. The company is particularly focused on enabling businesses to leverage their proprietary data to build contextualized AI models, while maintaining strict governance and compliance standards. Cloudera Data Engineering handles large-scale data processing for analytics and AI applications, using a cloud-native service with Apache Spark and Airflow for orchestration, monitoring and troubleshooting.
- Informatica — The company’s Intelligent Data Management Cloud platform leverages AI and machine learning to help organizations integrate, govern and extract value from their data assets across multi-cloud and hybrid environments. Informatica has recently enhanced its offerings by introducing user-friendly low-code/no-code options, which can reduce the technical barriers for data management and analysis, allowing a broader range of employees to work with data directly. Additionally, the company has implemented DataOps principles to create more agile data pipelines, which can result in faster data delivery and increased responsiveness to changing business needs.
- Databricks —The Databricks platform combines data warehouses and data lakes, enabling organizations to handle all data types and workloads in a single system. This unified approach enables data engineering, machine learning and business analytics from one platform. Through its partnership with SAP (discussed above), Databricks extends its capabilities to provide seamless data processing and AI-driven insights for SAP’s Business Data Cloud customers. If you want to know how well Databricks is doing, in December 2024 the company raised $10 billion in Series J funding from a set of top VCs, pushing its valuation to $62 billion.
- Snowflake — The Snowflake Data Cloud focuses on data management and analytics, offering a single platform where businesses can store, process and analyze all their data. The solution is known for its scalability, allowing businesses to use only what they need when they need it, which helps control costs. Designed with security and regulatory compliance in mind, it’s well-suited for handling sensitive information. Its Native Application Framework allows developers to build, deploy and distribute applications directly within the Data Cloud, enabling data access and computation without requiring customers to move or duplicate their data.
- Teradata — The company has expanded its VantageCloud platform to improve analytics for hybrid and multi-cloud environments. This platform simplifies data preparation, model deployment and monitoring, enabling faster AI development. Teradata’s partnership with Nvidia introduces an on-premises AI solution that integrates with familiar cloud-based AI tools such as AWS SageMaker and Google Vertex AI. Its open data ecosystem approach decreases data integration time, enabling faster insights from diverse sources. Teradata’s focus on responsible AI and ethical data management helps companies reduce compliance-related incidents, mitigating financial and reputational risks.
Looking Ahead: Generative AI Becomes Part Of Everyday Operations
As we move deeper into 2025, several trends are likely to reshape the enterprise data landscape. AI governance will take center stage as enterprises focus on frameworks to ensure AI technologies’ ethical and responsible use. This is about meeting compliance standards and building trust and transparency in AI systems’ operations. Privacy-enhancing technologies will also gain momentum as enterprises adapt to evolving data privacy regulations.
Edge computing will continue to grow with the expansion of IoT devices and 5G networks, pushing data processing closer to the source. This shift will require new real-time strategies to manage and analyze data while addressing security and latency challenges.
Data mesh architecture is another area to watch. Its decentralized approach offers flexibility and scalability, making it appealing for organizations struggling with siloed data. However, adopting this model requires significant cultural and operational changes to empower teams to manage their data effectively.
Finally, quantum computing could begin to impact data processing and analysis in specific areas such as cryptography and complex simulations. Forward-thinking organizations have started exploring its potential to prepare for future opportunities. Together, these trends will shape how enterprises approach data management, AI integration and decision making; all of them will require adaptability and proactive planning for organizations to achieve success in an increasingly complex landscape.
Navigating The Data-Driven Future
Looking back over the past year, it’s clear that effective data management remains central to business operations, but much of the real progress came from getting the basics right — like data quality, change management, governance and integration. The tools are out there, but they work only if teams are ready to use them.
As we move forward, I suggest keeping an eye on the emerging trends described above. These developments bring new ways to handle data but also come with challenges. For example, edge computing may improve real-time processing, but it also adds complexity in managing distributed data. AI governance is another area to prioritize, not just for compliance but to make sure teams can trust the systems they’re working with.
I recommend focusing on building strong foundations. Start with clean, reliable data and align your processes and teams before diving into new technologies. The tools and innovations will keep evolving, but the organizations that get the basics right — data consistency, organizational alignment and thoughtful change management — will be better prepared to adapt and make smarter decisions in an increasingly complex landscape.
Moor Insights & Strategy provides or has provided paid services to technology companies, like all tech industry research and analyst firms. These services include research, analysis, advising, consulting, benchmarking, acquisition matchmaking and video and speaking sponsorships. Of the companies mentioned in this article, Moor Insights & Strategy currently has (or has had) a paid business relationship with Adobe, AWS, Cloudera, Google, IBM, Microsoft, Oracle, Salesforce, SAP, ServiceNow and Teradata.
