Use retail data in Power BI

Why retail data is often difficult to use

Retail data is spread across stores, schedules, personnel planning, weekly reports and operational systems. This makes it difficult to quickly see which stores are performing well, where personnel planning deviates or how leave and occupancy are developing.

Manual exports go a long way, but the process is prone to errors. Especially with multiple stores or regions, you do not want to collect and combine files every week.

RetailSolutions data to Power BI

DigiData supports RetailSolutions and retrieves store data, store groups, employees, contracts, competencies, leave balances and weekly data. That data becomes available as an OData feed, so that Power BI can load the tables directly.

This allows you to create dashboards for store performance, planned versus actual hours, leave trends and benchmarks between stores. Because the data arrives in a structured manner, you can create filters per store group, region or period.

Which insights are valuable?

Deviations are particularly interesting for operational management. Which stores structurally need more hours than planned? Where do leave balances run up? Which store groups perform better based on turnover, hours or occupancy? By making retail data centrally available, you can answer these types of questions more quickly.

Retail data becomes even stronger when you combine it with finance or POS data. This creates a picture of turnover, planning and staff deployment in the same report.

Analyze retail data with ChatGPT, Claude and Gemini

In addition to Power BI, you can use CSV exports for AI analysis. For example, let ChatGPT, Claude or Gemini summarize a weekly report, name unusual stores or formulate questions for a regional manager. Always use controlled exports and your own data policy.

A practical retail data model

Start with stores as the central dimension. Add store groups, regions, and time periods so you can compare performance without having to manually filter each time. Employees, contracts, leave balances and competencies form the personnel layer. Weekly data and planning form the operational layer.

In Power BI you can create KPIs such as planned hours, actual hours, deviation per store, leave balance, occupancy per period and performance per region. If you add POS or financial data later, you can link staff deployment to turnover or margin.

For whom is retail data in Power BI valuable?

For regional managers, it provides insight into stores that need extra attention. For HR it helps to monitor leave, contracts and occupancy. For operations, it shows where planning structurally deviates from realization. For management, it makes trends visible across multiple stores and periods.

The advantage of DigiData is that these groups do not have to work with different exports. Everyone can report on the same synchronized tables.

Data quality in retail reporting

Retail reports are sensitive to name differences, missing periods and stores that are grouped differently. Therefore, ensure that store groups, periods and employee links are used consistently. DigiData helps by retrieving the source data from RetailSolutions in a structured manner, but good reporting definitions remain important.

Which retail tables do you use first?

Start with stores, store groups, employees, contracts and weekly reports. This combination provides a quick overview of planning versus realization. You can then add leave balances, competencies and any finance or POS data.

For Power BI it is useful to use stores as a dimension. Region, store group and branch then become filters that allow you to view the same KPIs at multiple levels. Employees form a second dimension, especially for occupancy, contract hours, leave and planning.

Weekly reports are often the fact table. Here you can see planned hours, actual hours, deviations and operational signals per period. If you link that table to stores and employees, you can see where structural deviations arise.

Combine retail data with turnover

Retail planning gains more value when you can link personnel deployment to turnover or margin. A store with many hours can perform well when turnover grows, but requires attention when effort increases and results lag behind.

DigiData can make RetailSolutions available in addition to other sources. For example, combine retail planning with finance data or POS data when that source is available. This creates dashboards for turnover per hour worked, staff pressure per region and planning efficiency per store group.

Trend information is especially valuable for management. A single week says little; several periods show whether a store is structurally different. Power BI makes that trend visible as soon as the data is consistently synchronized.

Checkpoints before you send on retail data

Check whether all stores are linked to the correct store group. Also check that employees remain correctly linked when they change locations. Small source errors can cause major differences in dashboards.

In addition, determine which period is leading. Retail teams often work in weeks, while finance sometimes manages in months. If weekly and monthly reports are mixed up, discussions arise about which figures are correct.

Finally, use clear definitions for planned hours, actual hours, leave balance and occupancy. DigiData supplies the tables; your Power BI model determines how these terms are calculated and presented.

From weekly report to management cycle

Retail teams often work in weeks. As a result, a dashboard is only useful if it fits in with the fixed consultation cycle. For example, create a Monday view for last week, a current week view for planning and a monthly view for management. Each view uses the same source data, but answers a different question.

For regional managers, prioritization is especially important. Don't just show all stores, but mark stores with different hours, increasing leave balances or structural understaffing. Then the dashboard becomes an action list instead of a collection of tables.

For management, trend is more important than detail. Show which regions are improving, which store groups deviate and whether staff deployment changes with turnover or planning. Grouping weeks into periods creates a calmer management picture.

Privacy and access

Retail data may contain personnel information. Consider contract hours, leave, competencies and planning. Determine in advance who can see this data and at what level of detail. An HR team needs different information than a regional manager or board member.

Work with summaries where possible. For management, the total per store, region or period is often enough. Employee details are only needed when someone is responsible for planning or HR follow-up. By consciously setting up roles, you prevent dashboards from becoming unnecessarily privacy-sensitive.

Also record how long exports are kept. Power BI uses the feed for reporting, but CSV exports for AI analysis or ad-hoc monitoring should be handled according to your own policies. DigiData enables controlled exports; your organization determines which data can be shared.

First dashboard: simple but useful

A good first retail dashboard will include stores, periods, planned hours, actual hours, variance, leave balance, and a few filters for region or store group. Only add more detail later. If the basics are wrong, additional graphs will only make the problem harder to find.

Check the first version together with someone from operations and someone from HR. Operations sees whether the planning is logical; HR sees whether contract hours and leave are interpreted correctly. You can then add finance or POS data for broader control.

Use the first month mainly to build trust. If store totals, region filters and week transitions are correct, teams will dare to use the dashboard more often. You can then manage for exceptions instead of manually checking each export again.

Summary

Using retail data in Power BI requires a reliable connection and structured tables. DigiData makes RetailSolutions data available via OData and CSV, so you can build dashboards and AI analyzes without manual exports. View the full RetailSolutions link.