Centralizing data for SMEs: step-by-step plan

The problem: data spread across dozens of systems

As an SME you can easily use five to ten different software packages. An accounting program like Twinfield or Exact Online, a CRM like Simplicate, project management in ClickUp or Bouw7, time registration, invoicing, and perhaps industry-specific tools. All those systems contain valuable data, but they don't talk to each other.

The result: if you want an overview of your business performance, you have to log into five systems, create exports, combine spreadsheets and hope you don't make any mistakes. That takes time, is frustrating and the figures are outdated by the time you're done.

What does 'centralize data' mean?

Centralizing data means collecting all relevant company data in one place. Not by putting everything in one system (that is unrealistic), but by using an intermediate layer that retrieves data from all your systems and prepares it for reporting.

Imagine: your accounting from Twinfield, your project hours from Bouw7 and your CRM data from Simplicate all come together in one place. From there you can build Power BI dashboards that combine everything. Calculate project returns by linking hours to invoices. Determining customer value by combining CRM data with turnover figures.

How do you approach that?

1. Start small

You don't have to connect everything right away. Start with your accounting data, which is the most important source for most companies. As soon as it is available in Power BI, you will immediately notice the difference: current turnover figures, outstanding invoices, cash flow overviews, without having to export manually.

2. Add resources step by step

After your accounting is running, connect the next source. This could be your CRM, your project management tool or your time registration. With each source you add, your reports become more valuable.

3. Combine data in Power BI

The real advantage is in combining. Link hours data from Bouw7 to invoice data from Exact Online to calculate project returns. Combine member data from Virtuagym with billing data for churn analysis. CRM data from Simplicate in addition to your accounting for a complete customer view.

Where does DigiData come into the picture?

DigiData was built for exactly this problem. You connect your software packages via the dashboard, and DigiData automatically retrieves the data daily. Everything becomes available as OData feed that you can load into Power BI or Excel. You don't need to have any API knowledge, write scripts or do exports.

DigiData currently supports Exact Online, Twinfield, Bouw7, ClickUp, Simplicate, Robaws, Virtuagym, RetailSolutions and RDW. New integrations are added regularly. View the full overview of all integrations.

Which order works in practice?

A practical sequence starts with the reporting that already occurs monthly. For many SMEs, this is a finance dashboard: turnover, costs, outstanding items and cash flow. Then start with Twinfield or Exact Online, because those sources contain the amounts used by management. Then you add operational sources that explain why numbers change.

For example, a construction company might start with accounting and then add Bouw7 or Robaws. Then you not only see the achieved results, but also hours, materials, planning and project status. A service provider often starts with Simplicate, because CRM, hours, projects and invoicing come together there. Retail teams are more likely to add RetailSolutions to combine planning, leave and store performance.

Make it clear for each source which question that source should answer. A source without a concrete question quickly becomes a data collection without an owner. Good questions include: which projects generate margins, which customers are growing, which stores deviate from planning, or which invoices remain outstanding? That question determines which tables you need and how you build the Power BI model.

Checklist for your first central data layer

First determine which definitions are leading. Agree on what turnover, margin, written hours, billable hours, outstanding amount and period filters mean. If those definitions remain unclear, just move the spreadsheet problem to Power BI.

Then check the source data. Are customer names consistent, are project numbers filled, are periods used in the same way and are administrations properly separated? DigiData helps by collecting data in a structured way, but the company definitions remain yours.

Then create a small data model that you can validate against the source. Compare totals in Power BI with the source system, document discrepancies and only then add additional sources. This way you prevent an ambitious data platform from starting with twenty tables that no one is sure are correct.

What does centralization deliver in concrete terms?

The biggest gain is repeatability. A report that works this month should work again next month without any new export work. This shifts the time from collecting files to interpreting figures.

The second gain is trust. When finance, operations and management use the same synchronized tables, there are fewer discussions about which spreadsheet is current. You can make decisions based on the same source data and investigate deviations in a targeted manner.

The third benefit is flexibility. Once the central layer is in place, you can use the same data for Power BI, Excel, CSV export and controlled AI analysis. A manager can view a dashboard, a controller can check a CSV and an analyst can use the same dataset to summarize patterns with ChatGPT, Claude or Gemini.

Governance: who owns which data?

Centralization only works if it is clear who is responsible for the source data. Finance usually owns general ledger, invoices and outstanding items. Operations or project management owns planning, hours and project status. Sales or account management monitors CRM definitions such as customer segment, opportunity stage and contact person.

Determine per source who can request changes, who checks totals and who determines which fields are used in reports. This prevents Power BI from becoming a collection point for tables that no one takes responsibility for anymore. A central data layer is not a substitute for ownership; it actually makes visible where ownership is lacking.

Also work with a small word list. Define turnover, margin, active customer, ongoing project, billable hour and outstanding amount. Place those definitions next to the dashboard or in documentation. This way, management, finance and operations can use the same report without always discussing the meaning of the figures.

How do you prevent a new spreadsheet problem?

The pitfall is that a data platform ends up with the same chaos as the old spreadsheets, but with nicer graphs. You can prevent this by consciously starting small. First, publish a basic dashboard that is monitored monthly. Only then add additional sources, detail tables and AI exports.

Make changes visible. If a field is calculated differently, note that in the reporting documentation. If an integration is expanded with new tables, first test whether existing dashboards continue to work the same. Each extension requires a short validation: is the total correct, is the period correct, is the filter logic correct and does a colleague understand why the table exists?

Use DigiData as a stable source layer. The synchronization retrieves data; Power BI creates the control information. By separating those roles, your model remains maintainable and you prevent reports from becoming dependent on separate files, hidden transformations or personal working methods.

The costs

At DigiData you pay per integration, per month. No start-up costs, no long-term contracts. You can try it for free for 14 days and cancel at any time. For most SMEs, the time saved on manual exports is enough to recoup the investment. Contact us for more information.