Where Can Data Management Partners Create the Most Value in 2026?

Insights from Our Data Experts for Global Data Leaders

Last year, we commissioned Forrester Consulting to survey over 300 global data leaders to learn how they were handling their data management challenges. We asked them about their current data best practices, the barriers impeding their progress toward data management goals, and the benefits they’ve seen from improving data management.

We also asked these leaders an open-ended question: 

"What additional services or support would you like a data management partner to provide when managing your master data?"

We received responses from leaders in finance, sales, procurement, compliance, IT, and several other functional areas in addition to data and analytics. Most of these leaders were C-level, vice presidents (in charge of one or several large departments), or directors (managing a team of managers and high-level contributors), representing companies in more than 20 different industries. (All survey responses were anonymous, but respondents had to provide some details about their role.)

As we go forward in 2026 — and reflect on the major technological, economic, and geopolitical pressures that companies had to wrestle with this past year — we thought the time was right to take a fresh look at this question. We selected a dozen of the respondents’ most interesting and thought-provoking answers, and we organized them into four categories according to their general theme:

• AI and Machine Learning Technologies
• Data Quality, Integrity, and Governance
• Integration and Interoperability
• Training, Support, and Collaboration

We then asked a group of our leading data, technology, and AI experts to provide analysis and commentary from a forward-looking point of view — considering how business needs and goals may have shifted in 2025 and where companies will need to focus their resources to stay competitive in 2026.

For better context, and to help our data experts refine their commentary, each of the survey respondents’ answers to the open-ended question is accompanied by the respondent’s industry, department, and job level.

Let’s explore the additional services 12 data leaders want from their data management partner and the guidance our experts would offer in response.

AI and Machine Learning Technologies

Mastering Data Management – Survey Response
"A data management partner should help us incorporate AI and machine learning capabilities to streamline the identification of trends in data, enhancing predictive analysis and supporting data quality management."
Industry: Telecommunications	Level: Director	Function: Corporate Compliance

This respondent’s request isn’t surprising. AI is quickly becoming a game-changer for enterprise data management. AI tools are making it easier to spot trends and predict what’s coming next, far more efficiently than humans can. By analyzing massive datasets and uncovering patterns, AI helps businesses make smarter decisions faster and improve customer experiences at lower cost. 

“These capabilities are being built right into data management platforms,” says Prachi Juneja, Vice President, Analytics Advisory Services. “That means real-time monitoring to catch issues like fraud or system glitches before they spiral, and predictive analytics that can forecast customer behavior or market shifts so companies can plan ahead instead of playing catch-up.”

Mastering Data Management – Survey Response
"Our data management partner should provide advanced AI-driven tools capable of detecting, identifying, and automatically correcting errors across the data lifecycle."
Industry: Advertising and/or Marketing	Level: Vice President	Function: Information Technology

This is a valid ask; advanced AI-driven tools to detect and correct possible data anomalies are critical to maintaining high-quality data across the lifecycle. Depending upon the type of data, your AI tool should be able to identify extraneous data, poorly formatted data, and invalid data. 

“AI tools should be applied at different points within your data supply chain to ensure consistency,” says Sean Carolan, Vice President, Data Advisory Services, “and they should be applied in real-time to identify data problems as soon as they can be detected.” 

In 2026 and beyond, a data management partner should be providing these tools to automatically detect and correct errors. By automating the entire data lifecycle, from initial ingestion to consumption, they reduce manual effort, speed up processing, and help ensure data integrity. 

Prachi Juneja says: “AI-driven tools can improve data quality while allowing data professionals to focus on higher-value work instead of grappling with manual tasks like data cleansing and validation. The reduced manual effort can also yield significant cost savings by helping minimize the risks of bad data and data breaches. 

“Also worth mentioning,” she continues, “is that AI can automate monitoring for regulatory compliance, monitor data usage for security threats, and provide on-demand audit documentation, which helps enterprises avoid getting hit with fines and penalties.” 

Mastering Data Management – Survey Response
"We seek a data management partner to help us achieve AI-driven insights, accuracy, automation, and real-time synchronization."
Industry: Electronics	Level: Director	Function: Logistics

Real-time synchronization can really help drive first-mover advantage in a business environment requiring fast, accurate decisions. A data management partner can help companies adopt modern data architectures capable of processing data as it arrives, rather than in overnight batches, which makes insights and actions immediately available. 

Data management service providers help clients achieve seamless, two-way synchronization across various applications and platforms. They use technologies like APIs and AI-based integration tools to eliminate delays and create a unified view of information across the organization. 

“In logistics, this can be valuable for improving coordination by keeping inventories, shipment status, and delivery timelines updated across all the organization’s systems,” says Prachi Juneja. 

Sean Carolan added: “Leveraging AI Insights to help with accuracy and automation should be part of a complete approach to data management in 2026. We can use AI-driven insights via machine learning and model creation to help ensure that we have clean, accurate data. This allows additional AI models to create actionable insights to help ensure that decisions are based on accurate information.”

Data Quality, Integrity, and Governance

Mastering Data Management – Survey Response
"When it comes to master data, I'd want a partner who can proactively spot and fix data quality issues, like duplicated or missing info, and keep a close eye on our data health through regular checks."
Industry: Business or Professional Services	Level: Director	Function: Data and Analytics

It’s very important — and will only become more so — to proactively spot and fix master data quality issues like duplicates or missing information through regular checks. A proactive approach ensures data reliability, which leads to better decision-making and greater trust in your data assets. 

“Data management partners can help institute proactive data quality management processes to help prevent the proliferation of bad data that can lead to mistakes, failed campaigns, and incorrect analysis,” says Prachi Juneja. “Bad data can have significant financial implications. Studies show that a large portion of revenue can be affected by data quality issues. Proactive measures mitigate this financial risk.”   

Mastering Data Management – Survey Response
"A data management partner could assess our organization’s data governance practices for maturity, which could help find any gaps or areas where data policies and compliance need improvement."
Industry: Transportation	Level: Vice President	Function: Operations

This is a common operational concern, especially within the transportation industry. Data governance maturity directly impacts compliance, operational efficiency, and decision-making quality. Gaps in policies or practices can lead to risks around regulatory requirements, safety standards, and data accuracy — issues that can impact everything from fleet management to route optimization. 

Engaging a data management partner to perform this assessment would provide an objective view and actionable recommendations, helping the organization strengthen its governance framework.

Larry Cox, Senior Director, Solutions Architecture, comments: “In 2026, as transportation networks generate massive data volumes and global regulations tighten, robust governance will be critical for maintaining trust and enabling advanced analytics. Improved compliance and governance practices will also support initiatives like AI adoption and predictive modeling, which rely on clean, well-managed data. 

“Ultimately, these capabilities will position the organization to respond quickly to market changes, reduce risk exposure, and realize greater value from its data assets — keeping operations efficient and competitive in a fast-changing industry.”

Mastering Data Management – Survey Response
"A data management partner should prioritize innovation and continuous improvement by proactively introducing new methodologies and technologies to enhance data management processes."
Industry: Construction	Level:  Vice President	Function: Information Technology

This viewpoint makes a lot of sense in the construction industry. Innovation and continuous improvement are critical in data management for this industry, especially as data volumes, complexity, and regulatory requirements grow. A partner that proactively introduces new methodologies — such as AI-driven data quality tools and real-time integration technologies — helps construction organizations stay ahead of the curve and avoid costly delays. 

Lucas Black, Senior Vice President, Sales & Marketing Data Solutions, observes: “In 2026, with economic uncertainty and growing pressure to keep projects on budget and on schedule, businesses will need agile data ecosystems that reduce costs while helping uncover insights for growth. 

“Additional services such as predictive analytics, automated compliance monitoring, and enriched third-party data can help anticipate supply chain issues, manage risk, and even improve client relationships.” 

Integration and Interoperability

Mastering Data Management – Survey Response
"The data partner should provide support for smooth data transfer between legacy and modern systems."
Industry: Insurance	Level: Director	Function: Data and Analytics

This is an important point because seamless data transfer between legacy and modern systems is critical for maintaining business continuity and avoiding costly disruptions. In the insurance industry, many organizations still rely on legacy platforms for core operations, and without proper migration support, they risk data loss, inconsistencies, and extended downtime that can impact everything from claims processing to compliance. 

A data partner with strong integration expertise can ensure smooth transitions, which helps preserve data integrity and minimize operational risk.

“In 2026, modernization will accelerate as insurers adopt cloud-based systems, AI-driven analytics, and advanced automation,” says Larry Cox. “Efficient data migration will enable organizations to leverage these technologies without being held back by outdated platforms. Additionally, smooth transfers reduce compliance risks and help maintain historical data for regulatory and strategic purposes. 

“Ultimately, smooth migration positions insurers to innovate faster, scale efficiently, and be more competitive.”

Mastering Data Management – Survey Response
"The data partner should ensure seamless data synchronization between multiple platforms, including ERP and CRM."
Industry: Consumer Services	Level: Vice President	Function: Supply Chain

This concern is critical in this respondent’s industry because seamless data synchronization across platforms like ERP and CRM is key to keeping operations efficient and customer satisfaction high. Without a robust data management system with master data management (MDM) at its core, organizations risk fragmented records, inconsistent information, and unnecessary delays. 

A data partner that prioritizes MDM ensures a single source of truth, enabling accurate, real-time information across all integrated systems.

Larry Cox comments: “In 2026, as more companies move to cloud-based solutions and deploy advanced analytics, the role of MDM will become even more vital. Centralized governance and synchronization will support automation, AI-driven insights, and predictive modeling by ensuring clean, consistent data flows between ERP, CRM, and other enterprise platforms. 

“This capability reduces compliance risks, enhances customer experience, and drives operational agility.” 

Mastering Data Management – Survey Response
"I would like a data management partner to provide real-time data validation, advanced audience segmentation, comprehensive campaign tracking, and seamless CRM integration to enhance marketing insights and decision-making."
Industry: Advertising and/or Marketing	Level: C-level Executive	Function: Marketing/Advertising

Real-time data checks, smart audience segmentation, campaign tracking, and smooth CRM integration are the backbone of effective marketing today. Real-time validation keeps your data clean from the start, so you’re not wasting budget or missing chances to personalize. Advanced segmentation helps you zero in on the right audiences, boosting engagement and ROI. 

Tracking campaigns across channels gives you a clear picture of what’s working, so you can adjust on the fly. And when everything connects through your CRM, you get a complete view of customer interactions, making it easier to build loyalty and long-term value.

“These capabilities will matter even more as competition heats up, budgets tighten, and AI-driven personalization becomes the norm,” says Lucas Black. “Businesses will need accurate, integrated data to stay efficient and deliver measurable results. 

“Tools like predictive modeling, identity resolution, and privacy-friendly enrichment will help marketers anticipate customer needs while staying compliant with expanding regulations,” he continues. “All this illustrates why strong data practices aren’t just a nice-to-have; they’re direct enablers of smarter, faster, and more impactful marketing.”

Training, Support, and Collaboration

Mastering Data Management – Survey Response
"I want a data management partner to provide thorough training and support, educating employees on best practices to ensure they have the skills and resources to manage data efficiently."
Industry: Healthcare	Level: C-level Executive	Function: Information Technology

In the healthcare industry, proper education on data entry, validation, and maintenance is critical for maintaining high-quality data, which directly impacts patient safety and operational efficiency. 

According to Shannon Cunningham, Senior Director, Client Learning & Adoption: “In healthcare, inaccurate or incomplete data can have life-or-death consequences. Well-trained staff can use data management tools effectively, reducing bottlenecks and manual errors and leading to better decisions across clinical functions. 

“On the administrative side,” she continues, “faster workflows and better reporting can help streamline billing and claims processing, improve regulatory compliance, and support financial planning and forecasting.”

Lucas Black also comments: “In a healthcare context, training and best practices on data management can literally influence a patient’s well-being — never mind the risk to the healthcare provider’s business and reputation. The additional training and resourcing for efficient data management will pay off in the form of better patient outcomes, workforce efficiency, and cost management. And the burgeoning adoption of AI makes data even more of a risk or reward. 

“The degree of attention to data management and categorization can either unlock amazing insights from AI, or propagate bad information throughout AI-driven workflows,” he continues. “I’ll also note that projects with a lot of data and no real governance may not yield actionable insights. Good data governance also ensures compliance with various regulations and privacy considerations.”

Mastering Data Management – Survey Response
"Our data partner needs to have a global perspective and help us navigate the complexities of operating in multiple countries, each with its own set of regulations."
Industry: Telecommunications	Level: C-level Executive	Function: Corporate Compliance

A data partner that combines global standards with local adaptability and regulatory awareness would be a big asset for an international telecom company. Global organizations face increasing complexity in managing data across diverse markets. A partner with a consistent and reliable taxonomy ensures comparability and integration, while local expertise addresses nuances such as postal code formats, regional industry classifications, and language-specific data structures. 

This balance — local-first, global-always — is critical for accuracy and operational efficiency. Michele Mech, Vice President, Data & Analytics Enablement, explains: “Additional services like global taxonomy alignment (e.g. NACE Rev. 2.1, the latest EU standard) and mapping to local codes, combined with regulatory monitoring, help your team maintain compliance with evolving laws such as GDPR (General Data Protection Regulation) in Europe and CCPA (the California Consumer Privacy Act) in the United States. 

“More than ever, businesses this year will need scalable data solutions that deliver both global consistency and local precision to support them through economic uncertainty and regulatory tightening.”

Mastering Data Management – Survey Response
"I need further support in automating tasks like data entry, validation, approval, and updates to reduce manual effort, ensure consistency, and enhance operational productivity."
Industry: Financial Services	Level: Vice President	Function: Corporate Compliance

This is a real concern in financial services, where manual processes like data entry, approvals, and updates often slow things down and drive up costs. Automating these steps, especially validating data right at the point of entry, can prevent costly errors and reduce the risk of regulatory violations. 

A trusted data partner that helps automate at the source or centralize validation through a strong data management system makes your data more accurate, consistent, and audit-ready, while reducing operational strain.

Larry Cox says: “In 2026, automation will be a major lever for cost control and efficiency as firms face tighter margins, growing regulatory demands, and increasingly complex data environments. Building automation into front-end systems or using centralized MDM catches issues early, reduces rework, and speeds up reporting and decision-making. 

“Clean, reliable data is also essential for firms trying to adopt AI tools to accelerate risk modeling, fraud detection, and personalized financial services,” he continues. “It’s almost too risky, in a tightly regulated industry like financial services, to undertake these projects without the data automation expertise of a data management partner.”

Survey Highlights: Why Use a Data Management Partner?

When we asked our survey audience to tell us the top benefits they thought they would realize from using a data management partner, the top three answers were:

• Increased efficiency and agility for faster decision making and reporting – 50%
• Increased cash flow – 48%
• Better selection of suppliers/avoidance of supply disruption – 48%

Survey respondents also cited other benefits, such as faster time to business value, faster time to market, and better visibility into relationships with business partners. These advantages closely align with survey respondents’ top business priorities for the year ahead:

• Business efficiency – 55%
• Regulatory compliance – 54%
• Revenue/customer growth – 51%

The Wrap-Up

The new year is bringing businesses an unsettling mix of economic uncertainty, tighter budgets, and rising pressure to deliver yet more efficiency and more measurable results. At the same time, data volumes continue growing and AI adoption is moving from experimental to essential, powering everything from predictive analytics to customer experiences. 

Data decision-makers are at the nexus of all this churn and swirl; their efforts to turn fragmented, inconsistent data into accurate, trusted business assets will make the difference between companies that struggle and companies that thrive.