In many companies, “data” is everywhere, yet no one can say with complete certainty which reports are accurate, who is responsible for KPI definitions, or why the same indicator has three different values across three departments. This is not a technology problem—it is a problem of governance, quality, and accountability. The scale can be surprising: Gartner estimates that poor data quality costs organizations an average of $12.9 million per year, yet 59% of organizations do not measure data quality at all. Added to this is the “decision cost” – when strategy, margin, inventory, or sales targets are calculated based on inconsistent definitions. In practice, you don’t need a revolution or a multi-year program—you need a sequence of small, well-designed steps that can significantly improve reporting predictability and data confidence in 8–16 weeks. Below are five steps to organize data and accountability in your company, which can be implemented iteratively within the Microsoft Fabric ecosystem.

Start with business decisions

A data-driven organization is not built on creating an “ideal model,” but on specifying which decisions should be made faster and more accurately with data. Select a few areas where data errors have a real cost: product margin, product availability, campaign effectiveness, employee turnover, and production timeliness. Then describe these decisions in business language: who makes the decision, how often, based on what indicators, and what happens if the indicator is wrong. This step stabilizes expectations and prevents the data platform from growing without providing “moments of truth” for management. A good pilot is, for example, “One version of the truth about margins and discounts” in sales or “One definition of OTD/OTIF” in the supply chain – measurable and easy to defend. Only when you have your priorities in place do you choose the integration and modeling method in Fabric (Lakehouse/Warehouse, Data Factory, Power BI).

The effect of this step:

• list of priority decisions + business owners,
• 10–20 KPIs with definitions and sources,
• backlog “what must be true in the data” for the KPI to be reliable.

Make a quick inventory of data and a flow map

Data chaos rarely results from a lack of data – more often it stems from a lack of knowledge about where the data comes from, how it is transformed, and where it ends up in reports. All you need here is an agile “data inventory sprint”: identify source systems, critical tables/entities (customer, product, order, invoice), places of manual operations (Excel, exports), and key transformation points. It is worth adding minimal quality metrics (completeness, uniqueness, definition consistency) – since many organizations do not measure quality, simply starting to measure it usually reveals where errors are really “escaping.” Business example: in a manufacturing company, it often turns out that “line productivity” and ‘downtime’ are calculated from two different registers, and in B2B sales, an “active customer” has different criteria in CRM and ERP. In Microsoft Fabric, such inventory can be easily converted into structured layers of data (landing → curated → semantic), instead of mixing raw with processed data. Most importantly, you finish the sprint with a map of “what stands on what,” not with another presentation.

The minimum that should be created:

• source catalog + critical data entities,
• list of “risk areas” (manual files, duplicates, missing keys),
• the first 5–10 quality rules for critical data.

Organize responsibility: data owners, stewards, and simple RACI

Data does not “belong to IT” and does not “belong to analysts” – data belongs to business processes, and technology is only a means of maintaining and sharing it. A key step without revolution is assigning roles: Data Owner (responsible for definition and use in the business), Data Steward (ensures quality and consistency in practice), and Custodian/IT (maintains and secures the technical aspects). Without this, you will always have the same conflicts: “the report is lying” vs. “the system returns this” vs. “it’s a matter of definition.” A simple RACI matrix for 10-20 key KPIs and entities is enough – on one side, “who approves the definition,” on the other, “who fixes it when the quality rule fails.” Example: in retail, the owner of the “net sales” definition is finance/controlling, and the steward can be a domain analyst; IT provides consistent sources and integrations. This clarity radically shortens discussions and improves response time to data incidents.

What should be implemented immediately:

• business glossary for priority KPIs,
• RACI for domain data (sales, finance, logistics, HR),
• “escalation path” for data errors (SLA/priorities).

Introduce “Minimum Viable Governance”: quality, safety, and labels

Governance does not have to mean a heavy committee and hundreds of pages of policies. In the “no revolution” approach, you focus on the minimum required to protect the company and make reporting realistic: quality rules for critical data, role-based access control, and classification of sensitive data. Here, Fabric supports the “guardrails” approach well: integration with Microsoft Purview enables, among other things, the use of sensitivity labels and oversight of data flow (from source to report) without manually “keeping an eye on Excel.” A practical example: in a service company, it is enough to mark customer and salary data, restrict export outside of controlled paths, and at the same time enable departments to quickly self-serve non-critical data. This approach usually unlocks collaboration: the business gets access, but on terms that minimize risk and limit “shadow BI.” Most importantly, governance becomes part of the process rather than a hindrance.

Practical elements of MVP governance:

• set of DQ rules (e.g., completeness of tax identification numbers, dates, currencies, keys),
• data classification + confidentiality labels,
• roles and access groups associated with domains and reports.

Build a single source of truth and data distribution: OneLake + layers + semantics

Without a single, logical place for data, companies always revert to silos, duplicates, and “someone else’s file.” In Microsoft Fabric, OneLake acts as a stabilizer—a unified, logical data lake designed as a “single place for analytical data” and supporting a “single copy of data, multiple analytical engines” approach. In practice, this means that you can build your solution in stages: first, critical domains and a single reporting scenario, then additional sources and data products, without having to rebuild from scratch. The level of semantics (in Power BI/semantic model) is very important: even the best lakehouse will not ensure consistency if measures and definitions are duplicated across teams. Business example: in a capital group where companies have different ERPs, you can standardize the reporting layer (same KPI definitions), leaving system differences in the integration layer. As a result, the organization does not feel a revolution – it feels that the reports are starting to “make sense.”

How to implement this without causing organizational shock:

• pilot (1 domain + 1 management dashboard + 1 set of quality rules),
• iterations every 2–4 weeks: new source / new KPI / new data product,
• success metrics: report preparation time, number of “definition disputes,” number of data incidents.

Summary: A data-driven organization means order and accountability—technology only speeds things up.

The shortest path to becoming data-driven without revolution is to start with decisions, take a quick inventory, assign owners, implement minimum governance, and standardize the source of truth and semantics. Statistics show that companies pay real money for poor data quality and measurement—and this is a problem that can be solved in stages. In the Microsoft Fabric ecosystem, these steps naturally form a coherent path: OneLake as the foundation and Purview as the “safety belt” for data and accountability.