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AI and analytics rely on trusted data foundations. We implement industry-specific data governance frameworks that ensure quality, security, and traceability while balancing control with agility. Our data governance consulting services help CIOs and CDOs improve data quality, ownership, and compliance across the organization.
Artificial Intelligence only delivers value when the underlying data is trustworthy. Our data governance consulting services help organizations establish data quality management, ownership, and compliance structures that enable reliable analytics and responsible AI.
We at BE-terna operate technology neutral. You do not need every platform on the market to build effective data governance. You need the right methodology, framework and change management that fits your organization to turn a tool into a program.
We work with market-leading platforms such as Microsoft Purview, Microsoft Fabric, Qlik Talend Cloud, Qlik Data Products and Collibra.
Governance fails when accountability is unclear. We design the roles, decision rights and domain structure needed to make governance work across business, IT and compliance.
Typical outcomes:
A data catalog and business glossary give teams one place to find, understand and trust business-critical data assets, definitions and KPIs.
Trusted decisions need trustworthy data. We design data quality management programs that combine profiling, validation, observability and clear remediation workflows.
When auditors, regulators or leadership ask where a number came from, lineage gives your teams a clear answer. We implement traceability across source systems, pipelines, data platforms and reports.
Governance and security meet in data classification, access control and evidence. We help you protect sensitive data while keeping business access practical.
AI governance connects trusted data, responsible use, risk classification and human oversight. We help you create the controls needed for Copilot, agentic AI, machine learning and generative AI use cases.
Most organizations know, that data governance matters. The challenge is turning it into measurable business value.
Create shared KPI definitions, approved data sources and quality rules so leadership can make decisions based on trusted information.
Use lineage, ownership and evidence to answer audit and compliance questions with less manual effort.
Move from measuring issues to resolving them with clear owners, root cause analysis and remediation workflows.
Give AI, analytics and automation initiatives governed data sources, documented controls and transparent risk handling.
Make ownership visible across business domains so data quality, access and definitions are not left only to IT.
Reuse standards, glossaries, quality rules and controls across domains instead of rebuilding governance for every project.
You receive a governance maturity baseline, priority use case list, target operating model, tool benchmark and roadmap. The assessment should combine stakeholder interviews, artifact review and practical prioritization.
Your Data Governance Office becomes operational with named owners, stewards, decision rights, a governance council, first critical data elements and an agreed domain structure.
Your selected platform is configured to support the governance methodology. This may include catalog setup, glossary workflows, data quality rules, lineage and integration with analytics or data platforms.
The program expands across domains. Progress is tracked through measurable indicators such as critical data elements with owners, certified KPIs, rule coverage, issue resolution time, catalog adoption and audit evidence retrieval time.
BE-terna combines governance consulting with hands-on implementation. We help organizations move from strategy and framework design to tool configuration, adoption and continuous improvement across all our business solutions.
Millions of transactions in the ticketing and access system, but limited options for fast business analysis: That was the challenge facing Dachstein Salzkammergut cable cars. Together with BE-terna, the company built a centralized analytics platform based on Qlik Cloud that brings together sales, access, and ride data from SKIDATA, enriches it with business context, and makes it available for management, controlling, and operations. Today, users can analyze more than 50 million data records in seconds, with updates every 15 minutes, near real time reporting, and mobile access on the mountain. The project went live exactly on schedule.
Data governance is the framework of policies, processes, roles, responsibilities and technologies that ensures your data is accurate, consistent, secure, timely and used effectively across the organization. It is not a technology. It is a program for organizational development. In practice the program covers six capabilities: ownership and operating model, catalog and glossary, data quality, lineage, access and classification, and AI governance. Tools alone are not governance.
You are ready when the cost of ungoverned data has become visible, even if it is not yet quantified. Typical signals: the same KPI appears in multiple reports with different numbers, an audit or regulator has asked a question you could not answer quickly, an AI pilot has stalled because training data could not be trusted, or a new leader has inherited a data landscape nobody fully understands. Our assessment can easily show you the use cases where data governance brings the most success for your organization. The assessment also recommends whether you need a fast track pilot, a domain specific program or a full enterprise governance rollout.
Program first, always. A catalog without owners fills up with outdated assets. A quality tool without stewards generates alerts nobody acts on. We see organizations that bought the tool more than two years ago and still do not have governance. We also see organizations with no tool that have meaningful governance because ownership, definitions and decision rights were established first. Technology matters, but it is only an accelerator, not the foundation.
The common failure modes are predictable. Programs launch as IT initiatives without business ownership, so the rest of the organization never adopts them. Tools get implemented without decision rights, so nobody knows who approves a glossary term or a quality rule. Policies live in SharePoint but nobody enforces them. We prevent these failures by sequencing the work deliberately (operating model before technology), by making change management an explicit part of delivery, and by insisting that every critical data asset has a named owner before the catalog goes live. If these three things are wrong, no tool will save the program.
They are distinct disciplines that reinforce each other. Governance defines ownership, policies and definitions for your data, including critical data elements, reference data and hierarchies (customer, product, supplier, cost center, account). Master data management creates and maintains a single consistent version of core entities. Governance provides the foundation MDM needs to succeed. When customers need MDM, we work with platforms like Microsoft MDS, Profisee, Stibo Systems and SAP MDG, and we align the two programs so they reinforce rather than duplicate each other.
We help you establish AI governance controls and evidence aligned with EU AI Act expectations: a model and use case inventory, risk tier classification, training data documentation and lineage, human oversight workflows, monitoring and change processes. Final compliance depends on use case, risk class and legal interpretation, but the governance posture we put in place is defensible to regulators, customers and internal auditors. Whether you run Copilot, agentic AI or classi c machine learning, your AI programs become auditable and aligned with your risk appetite.
We focus on regulated and data intensive industries across DACH and CEE. In financial services and insurance we support the data governance controls and evidence needed for DORA, BCBS 239, Solvency II and MaRisk. In energy and utilities we support NIS2 and market communication requirements. In manufacturing we support CSRD, supply chain due diligence (LkSG) and product data integrity. In telecommunications we support NIS2 and data retention requirements. The governance capabilities are the same across industries. The regulations, data models and priority use cases are not.
No. We are technology neutral. We implement the catalog and governance platform you already have or choose, and we bring the methodology that makes any of them work. We have deep expertise in Microsoft Purview, Microsoft Fabric, Qlik Talend Cloud, Qlik Data Catalog and Collibra, and we also work with customer chosen combinations of fabric, mesh or hybrid architectures. If you have not chosen yet, the assessment includes a tool benchmark tailored to your stack, budget and regulatory needs.
The assessment produces a roadmap your leadership can act on. First business value typically appears once one priority KPI, domain or report has named owners, approved definitions, quality rules and a visible issue workflow. Broader maturity across domains is built step by step. For smaller scopes, a focused diagnostic or pilot is available. The program delivers value in waves, not only at the end, and scope can be re-sequenced as business priorities shift.
BE-terna combines governance design with hands on implementation, platform expertise and industry delivery expertise across DACH and CEE. We work alongside our Telefónica Tech governance framework, and we stay with the program through technology configuration, change management and evolution. The result is a governance program that runs, with measurable adoption and audit evidence, not a framework document on a shelf.
Most customers begin with a governance diagnostic: we identify the priority domain or use case, assess maturity, confirm owners and produce a 90-day roadmap. From there we scale into a full assessment or directly into a domain pilot. Talk to our governance experts to scope yours.
