How do you build a data strategy around the decisions your business needs to make?
The short answer
Build a data strategy by naming the decisions the business needs to make, defining the measures those decisions require, and tracing them to reliable sources. Assign ownership, access rules, and freshness requirements. Then choose a manageable first project with a clear user, a measurable outcome, and a plan for ongoing support.
Which business decision should the strategy start with?
Start with a decision someone already has to make. Which locations need attention? Where is demand changing? Which operational issues should the team address this week? A specific question gives the business and technology teams something concrete to work through together.
For each question, name the person making the decision, how often they make it, and what they do with the answer. If nobody can describe the action that follows, spend more time on the question before designing a dashboard.
Consider an illustrative operations review. Managers want to identify where performance has changed and decide which issues deserve investigation. Today, preparing for that review might require gathering several reports and reconciling their definitions.
The first improvement could be a trusted view of a few agreed measures, with enough detail to investigate exceptions. That gives the project a purpose, a user group, and a way to evaluate whether it helps.
These examples are planning scenarios, not descriptions of a particular customer's processes.
How do you turn a business question into data requirements?
Work backward from the action. Define the measure, the comparison, and the level of detail needed. Then identify the sources and the transformations required to produce the answer.
| Planning question | What the team needs to agree on | Why it affects delivery |
|---|---|---|
| What decision will this support? | The action and accountable decision-maker | Gives the project a business purpose |
| What does the measure mean? | Calculation, exclusions, units, and reporting period | Prevents competing versions of the same metric |
| Where does the information come from? | Authoritative source and how records connect | Exposes integration and reconciliation work |
| How current must the answer be? | An acceptable delay tied to the decision | Guides refresh and processing requirements |
| Who should see the result? | Access by role, location, or business responsibility | Shapes security and sharing |
| What happens when the data is wrong? | An owner and correction process | Makes the result supportable after launch |
Write these decisions down before treating a prototype as authoritative. A chart can look finished while its most important definition remains unsettled.
Suppose one report includes canceled orders and another excludes them. Combining the reports won't resolve the disagreement. The responsible business owners need to agree which definition applies to the decision and preserve the distinction where both are useful.
Who should own data quality and definitions?
Assign ownership to people who can explain the information and resolve disagreements about its meaning. Technical teams can manage connections and processing, while business owners remain responsible for the definitions used to make decisions.
Microsoft's data governance guidance brings business owners, stewards, and technical teams into the planning process. That division of responsibility matters because an inventory of data doesn't explain how the business intends to use it. Plan data governance with Microsoft Purview
For a first project, keep ownership practical. Name who approves a metric definition, who investigates a source discrepancy, and who fixes a failed refresh. Make the escalation path visible to the people using the report.
Test a few known problems early: duplicate records, missing identifiers, inconsistent categories, and records arriving after the reporting cutoff. Decide whether the system should correct, flag, or exclude each type of problem, and who can authorize that treatment.
Avoid silently removing inconvenient records just to make the dashboard look clean. Users need to understand meaningful gaps and exceptions when interpreting the result.
Do you need a new platform before you can start?
Evaluate the existing environment against the first project's requirements. Some problems can be addressed through clearer definitions and improvements to an existing reporting process. Others require additional integration, storage, or analysis capabilities.
If Microsoft Fabric is under consideration, connect its capabilities to those requirements. Microsoft describes semantic models as the layer that defines relationships, measures, and business terminology for analysis. A model can express agreed definitions, but the business still has to agree on them. Power BI semantic models in Microsoft Fabric
Freshness is another architecture decision. Microsoft documents different trade-offs for imported data and queries directed at source systems. Choose based on when the business needs an answer, the source system's capabilities, and the operating cost. Data storage options in Microsoft Fabric
For a weekly planning meeting, a dependable scheduled refresh might meet the need. A workflow responding to changing operational events could need a different design. The question is how much delay the decision can tolerate.
A strategy should also explain what stays in place. Identify which systems remain authoritative and how information will flow between them. A platform decision is easier to evaluate when everyone understands the work it must support.
What makes a manageable first data project?
Choose a decision with a committed owner, accessible source information, and a way to verify the result. Limit the initial scope enough that the team can resolve definitions and test the complete workflow.
For example, a project could support one recurring operations meeting with a defined set of measures. Agree on the source systems, the reporting period, the people who can access the view, and how the totals will be checked before use.
Include representative exceptions in the test. Compare the proposed output with source records and explain any differences. Keep a record of the reconciliation so users can understand why they should trust the new view.
Measure more than whether the dashboard loads. Track preparation effort, reconciliation work, unresolved data issues, and whether the intended users can take the next action without rebuilding the analysis in a separate spreadsheet.
Define a review date and the evidence needed to expand the project. Also assign responsibility for refresh failures, access changes, and evolving business definitions. A useful report needs an operating owner after the initial delivery team moves on.
How does this prepare your business for AI?
Clear definitions and trusted sources make it easier to evaluate AI answers about the business. If several measures use similar names but calculate different things, a conversational interface adds another place for that ambiguity to appear.
Microsoft's guidance for Fabric data agents specifically calls out duplicate or overlapping measures as a source of ambiguity. It recommends consolidating or clearly differentiating them when preparing semantic models for AI. Semantic model practices for data agents
Start with questions whose answers the team can independently verify. Ask which source and definition produced the result, whether the user should see it, and whether the reporting period matches the question.
That doesn't require completing every data initiative before experimenting with AI. It does require choosing a scope where the information, permissions, and expected answers are understood well enough to test.
What should you bring to a Data Strategy Day?
Bring the decisions you want to improve, examples of the reports people use today, and a list of the systems involved. Include someone who owns the business outcome, someone who understands the sources, and someone responsible for the technology environment.
Be specific about where the work stalls. Perhaps reports arrive too late, definitions differ between teams, or a useful question takes too much manual reconciliation. Those details help establish the first priority.
ArchitectNow's Data Strategy Day starts with business decisions and works through analytics priorities, platform design, governance, and a high-level roadmap. The aim is to identify where to start and the groundwork needed to make that project useful.
Discuss a Data Strategy Day with ArchitectNow and bring the business question your team needs to answer more reliably.
Common questions
Do we need to consolidate all our data before the first project?
Start by identifying the information required for the selected decision. Validate that scope before committing to a broader consolidation effort.
Who decides which report is correct?
The responsible business owner should approve the definition and reconciliation rules, working with the people who understand the source systems. Record the decision so later reports can apply it consistently.
Should every dashboard show real-time data?
Set the freshness requirement from the decision. A weekly planning report and an operational alert can justify very different update patterns.