Profisee

AI-ready data is data that has been cleansed, labeled, validated and transformed into a high-quality, structured format suitable for immediate and effective training or deployment by an AI tool. Data preparation for AI depends on core data attributes such as quality, integration and governance, which determine how reliable and useful the information will be.

Last week, I hosted a webinar with my colleague Malcolm Hawker, Profisee’s Chief Data Officer, to walk through the recently published 2026 Gartner Magic Quadrant for Master Data Management Solutions. Malcolm and I came to the discussion from two very different vantage points. He spent years at Gartner and authored the previous Magic Quadrant in 2021.

As companies work to adopt generative AI, quite a lot of hype has built up around context in data and analytics as data practitioners work to build a semantic layer to make data useable by AI. Context in data is important, but truth in data is just as important; it takes both to create trusted data. Master data management is the answer to how companies can create data that reflects multiple versions of the truth to build the knowledge graphs AI needs to produce trustworthy outputs.

AI-ready data for manufacturing provides a unified foundation that enables AI to process clean information and deliver measurable business results. Poor data quality is a leading cause of AI project failures in manufacturing, underscoring the importance of ongoing monitoring and effective data management. Profisee MDM provides unified data stewardship, flexible integration and AI-assisted workflows to build AI-ready data at scale.

I’m excited to announce Profisee 2026 R1, a release that pushes master data management into where the enterprise is heading: AI-driven workflows, Lakehouse-native integration and users who expect data to be available inside the tools they already use. Most MDM platforms still operate as the destination for stewardship and usage. But in today’s data environment, the destination is wherever the user already works. A chat interface, a Lakehouse, a connected application or, increasingly, AI agents.

A unified customer view (also known as a customer 360 profile) is a complete picture of your organization’s relationship with a customer across the enterprise, capturing all touchpoints and systems rather than a single deduplicated record. Achieving and using a unified customer view can increase customer engagement, improve customer relationships and provide revenue growth opportunities.

A single source of truth is a reliable and complete data record that encompasses domain inputs from across the enterprise. An SSoT can improve resource management, enable better decision making, and result in more accurate analysis and forecasting. A flexible, multi-domain MDM platform like Profisee enables SSoT initiatives without the complicated set-up of multi-tool solutions.

Data merging combines data records from two or more source data sets to produce a single master copy. Data merging can help the business build a customer 360 view, standardize product listings, improve data analysis and more. Profisee MDM has data automation, workflows and governance tools to ease data merging and the larger master data management processes. Companies that compete in today’s data-first marketplace must build unified views of their products, customers, suppliers, locations and more.

In an AI-first world, success depends less on how much data you have and more on how trustworthy and governed that data is. Financial institutions don’t need to rip and replace legacy systems; a layered, modular approach lets them modernize incrementally and deliver value along the way. Master data management sits at the core of a modern financial data architecture, ensuring trusted records flow consistently across analytics, AI and operational systems.

High quality data is data that aligns with the organization’s defined data standards and governance policies and can be reliably used for reporting, analytics and regulatory purposes. Obtaining high-quality data is an ongoing, iterative process that requires clear goals and analysis. Companies with quality data management programs manage compliance and fraud risks while gaining a more complete customer view suited to increase share of wallet.