Data Governance
Clear ownership, shared definitions and rules that hold, for better decisions and a data foundation both people and AI can build on.
Most organizations want to be data-driven, but it often stops at the ambition. Reports show different numbers, no one knows who owns which data and a lot of time goes into fixing errors after the fact. With data governance, you decide who owns the data, what it means and which rules apply. That means fewer debates about which number is right, faster decisions and a stable foundation for AI, e-commerce and reporting.
What is Data Governance?
Define, own and classify your information
We help you implement data ownership principles and create a common glossary that defines and explains your most business-critical information.
Business value:
Clarity, accountability and a common, unambiguous truth for the entire organization.
Build trust and traceability
By documenting the origin of the data and establishing quality processes, your information becomes traceable and reliable. It becomes clear to everyone where the information comes from and how it has been handled, which builds trust.
Business value:
Increased trust in data throughout the organization, leading to safer and faster business decisions.
Fostering a responsible data culture
We help you communicate clear roles, responsibilities and processes for data management. By making high-quality data accessible and understandable to the right people, a culture in which data is treated as a strategic asset is fostered.
Business value:
A culture of data awareness that reduces operational risks and maximizes the value of your information.
Quality must be built at the source
A common mistake is thinking that data quality is something you can “clean up” later. Our experience, and our philosophy, is the opposite: true and sustainable data quality can only be achieved at the source, within the business processes where the data is created.
Trying to fix issues further down the chain is often both inefficient and costly. For every corrected data point, there is a real risk that a new one with the same issue will be created a week later.
Clear and pragmatic principles
That’s why our work focuses on establishing clear and pragmatic principles for how data is handled across your organisation.
This includes defining explicit data ownership, ensuring that someone is responsible for the accuracy of specific information. It includes creating a shared glossary so everyone agrees on what terms like “active customer” actually mean.
Most importantly, it involves taking control of the processes that generate your data. By ensuring quality at the moment information is created, you build an asset you can trust — and confidently act on.
Building a culture of data-driven work
But Rome wasn’t built in a day; it was built one house at a time. We don’t believe in designing a perfect, theoretical blueprint for tackling these challenges.
Instead, we believe success comes from pragmatically addressing real problems in the business, and using those learnings to build a culture of data-driven work — along with an understanding that how we handle data shapes what we can ultimately achieve with it.
Resultatet är en organisation som vet vem som äger datan, vad den betyder och hur bra den är. Internt betyder det färre diskussioner och snabbare beslut. Utåt betyder det korrekt information i varje kanal och en grund som Master Data Management, PIM och AI kan bygga vidare på.
Dive deeper into Data Governance
Explore our webinars, guides and podcast episodes on how organizations take control of ownership, definitions and data quality.
Have we been using AI the wrong way? Data Experience and AI in processes
What is MDM? And why do you need it?
The importance of Data Discovery in MDM projects
Data Governance as a Mediator
How Data Governance acts as a mediator in conflicts around product information and processes
From Data Governance to AI
Introduktion till Data Governance: Effektivisera er datahantering
Getting Started with Data Catalog: A Step-by-Step Guide to Effective Data Management
Improve data quality and compliance with Data Governance and Catalog
Maximize the value of your data with Data Governance and Catalog
Can every task have its own user interface?
Utbenat: Data-driven
Utbenat: Master data
Utbenat: Context
Utbenat: Data Quality
Utbenat: Data Trust
Why Fiwe is your partner for Data Governance
Succeeding with Data Governance requires a pragmatic partner who can balance structure with business value.
A tactic for business value, not bureaucracy
Our goal is never to create heavy or bureaucratic administration. We focus on implementing a Data Governance framework that is tailored to your reality and delivers fast, measurable impact in the areas where it matters most.
We put process first, tools second
There are many tools for Data Governance, but they create no value on their own. Our expertise lies in first helping you define your processes, roles, and ownership models. After that, we support you in selecting and implementing the technical tools that best support your strategy.
A partnership that builds a sustainable data culture
Our ultimate goal is to make good data practices a natural part of your organisation. We act as a long-term partner to train your teams, reinforce new ways of working, and build a sustainable culture where everyone understands and takes responsibility for their role in maintaining data quality.
Frequently asked questions about Data Governance
Answers to the questions we hear most often from organizations that want to take control of the ownership and governance of their data.
Why is data governance a prerequisite for AI?
AI uses data exactly as it is. If terms are unclear, ownership is missing or sensitive data is exposed, AI amplifies the problems instead of solving them. Data governance provides clear definitions, access rights and traceability, so AI can be used in business-critical processes. Watch the webinar Data catalog as a foundation for AI.
Who should own data governance in the organization?
Responsibility should sit with the business, not only with IT. A steering group with representatives from the largest data domains sets the direction, while data owners and data stewards are responsible for their areas. IT supports with systems and technology. When the business owns the question, governance stays relevant and gets followed.
How do we keep data governance from becoming bureaucracy?
Start with a concrete business problem rather than a comprehensive framework. Introduce only the roles and rules needed to solve it, and build them into the processes where data is created. Once it works, take on the next area. That way, governance feels like help in everyday work rather than extra administration.
Do we need a tool for data governance?
Not to get started. Ownership, definitions and ways of working can be established with simple means. As data volumes and the number of systems grow, a tool like Informatica Cloud Data Governance and Catalog becomes essential to keep order, automate quality checks and scale the work. We help you decide when and what.Informatica Cloud Data Governance and Catalog däremot avgörande för att hålla ordning, automatisera kvalitetskontroller och skala arbetet. Vi hjälper er välja när och vad.
What is a data catalog?
A data catalog is a searchable inventory of an organization's data: which sources exist, what the fields mean, who owns them and how data moves between systems. It makes it easy to find and trust the right data, and keeps documentation up to date. Watch our webinar Get started with a data catalog.
What is data classification?
Data classification means labeling data by how sensitive and business-critical it is, such as public, internal, confidential or personal data. The classification controls who can see and change the data and how it must be protected. It is the foundation for both information security and regulatory compliance.
What is the difference between data governance and data quality?
Data quality measures how accurate, complete and consistent data is. Data governance decides who is responsible for that quality, which requirements apply and what happens when data falls short. Without governance, quality work becomes a clean-up that has to be repeated over and over.
How does data governance help with GDPR and other regulations?
Regulations like GDPR and upcoming requirements for digital product passports require you to know what data you have, where it lives and who is responsible for it. Data governance provides that overview through classification, ownership and documented data flows. That makes compliance easier to achieve and to demonstrate during an audit.
What is a business glossary?
A business glossary is a shared list of the organization's most important terms. It describes what customer, active customer or net sales mean, for example, and who owns each definition. When everyone works from the same definitions, departments stop showing different numbers for the same thing. Listen to Utbenat: Context.
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