Power BI Integration: How Company Data Is Turned into Analytics
Company data is rarely stored in one place. Financial information is often kept in accounting or ERP systems, while sales data is stored in CRM platforms. Plans and additional calculations may be maintained in Excel files or other business systems. To use this data for business analytics, it first needs to be collected, combined and prepared.
Power BI integration makes it possible to automatically collect data from different company systems and use it for unified financial and business analytics. Depending on the systems in use, data can be retrieved from databases, via APIs, from files, or through other methods. The collected data is then prepared and combined according to the company’s specific analytics needs.
Across PLY Business projects, we have implemented integrations with more than 20 different data systems, including accounting, ERP, CRM, and other business data sources. These include Rivilė, Finvalda, Microsoft Dynamics 365 Business Central, TSM, Butenta, Directo, Euroskaita, Robolabs, Agnum, Labbis, Pragma, Optimum, Pay Premium, Bauwise, Terra Logistics and others.
However, our experience has shown one important thing: while the technical process of retrieving data from different systems may vary, the resulting analytics is primarily shaped not by the accounting software being used, but by the business questions the company wants to answer.
How Does Data Get from an Accounting or ERP System into Power BI?
There is no single integration method that works for every system.
Depending on the software and its technical capabilities, data can be retrieved:
- directly from a database;
- via an API;
- from automatically generated files;
- from other structured data sources.
Before building an integration, it is therefore important to understand not only which system the company uses, but also what data it contains, how that data can be accessed, and what information is actually needed for analytics.
The technical extraction of data is usually only one part of the process. Another important step is turning raw data into information that is meaningful to the business and suitable for analysis.
When Is It Worth Automating Analytics with Power BI?
In many companies, financial and business analytics starts with Excel. Data is periodically exported from accounting, ERP, or other systems, supplemented with additional information, used for calculations, and turned into reports for management or finance teams.
When the number of data sources, analytical dimensions, and reports is limited, this process may be perfectly adequate. The need for Power BI usually arises not because Excel itself is a problem, but when preparing analytics becomes an increasingly complex and repetitive process.
As a business grows, the number of data sources, reports, and calculations may increase. The same steps then have to be repeated during every reporting period: collecting new data, preparing it, combining different tables, updating calculations, and checking the final results. Manual tasks take time and make the analytics process more difficult to manage. They also make it harder to ensure that the same calculation logic is applied consistently every time.
The value of Power BI integration becomes particularly apparent when:
- the data required for analytics is spread across several accounting, ERP, CRM, or other systems;
- the same manual steps are repeatedly required to prepare reports;
- data needs to be transformed, classified, or combined rather than simply collected;
- multiple interconnected Excel files and formulas are used;
- more complex calculations are required, such as profitability by customer, product, or business unit, margins, cost allocation, or budget-versus-actual analysis;
- the same KPIs are calculated differently across reports or teams;
- management needs regular access to up-to-date results and wants to understand not only what changed, but also why.
In a Power BI analytics solution, more than just data refreshes can be automated. Once the analytical model has been created, it defines how data is extracted, cleaned, transformed and combined. It also defines the logic used to calculate the company’s key metrics.
Each time the data is refreshed, the predefined data preparation and calculation logic is applied automatically. This eliminates the need to repeatedly perform the same data processing steps, copy formulas, or manually recalculate metrics. It also reduces the risk of analytics being affected by an incorrectly copied formula, an omitted data source, or another manual error.
The result is not only a more convenient way to analyze data, but also a reliable and consistent analytics process, allowing teams to spend less time preparing reports and more time analyzing results, understanding the causes of variances, and making decisions.
How Does Power BI Interactivity Make Data Analysis Easier?
Automated data collection and preparation solves an important part of the analytics process, but the value of Power BI goes beyond that. Well-designed interactive reports allow users to do more than see the final result. They can filter data, analyze different dimensions, and investigate the causes behind changes.
Importantly, this level of interactivity does not come simply from choosing Power BI as the technology. When designing an analytics solution, it is necessary to consider how users will analyze information, what answers they will be looking for, and how they can move from a high-level result to the underlying causes. The data model, calculation logic, analytical dimensions, and interactions between reports are then designed accordingly.
For example, suppose a company’s profit has decreased. The metric itself shows the change, but it does not explain what caused it. Interactive analysis can help identify the reasons behind the decline. Did sales volumes fall? Did the sales structure change? Did margins decrease? Users can then explore which customers, product groups, or business segments had the greatest impact.
This makes it possible to move systematically from a high-level result to increasingly detailed information and understand more quickly not only what changed, but what caused the change.
The ability to move easily from an overall result to its underlying causes is one of the key benefits of interactive business analytics. Management or finance teams do not need to prepare a separate report for every new question – many answers can be found by exploring the same continuously updated data model.
To make practical use of interactive analytics, it is important not only to build the reports but also to help the team learn how to use them. After the initial implementation of a PLY Business solution, we train users to work with the Power BI reports we have created. We show them how to use interactive features, filter and drill into data, and investigate the causes behind the results.
This enables users to work with analytics independently and find answers to relevant business questions more quickly.
Does Analytics Differ When Using Rivilė, Finvalda, TSM, or Another Accounting System?
The technical side of the integration may differ, but business analytics needs are often similar.
One company may use Rivilė, another Finvalda or TSM, yet all of them may want to answer very similar business questions:
- which business areas are the most profitable;
- how revenue, costs, and margins are changing;
- which customers or products generate the most profit;
- what the current accounts receivable and accounts payable situation is;
- how cash flows are changing;
- how actual results compare with the budget;
- how other important business metrics are changing.
The data required for these calculations may be stored differently across different systems. During integration, the data is prepared and transformed into a data model suitable for analytics.
That is why we design PLY Business solutions around the company’s specific business model, organizational structure, and management needs rather than around the standard reports available in a particular accounting system.
Can Power BI Combine Data from Multiple Systems?
Power BI makes it possible to combine data from different company systems within a single analytical model and analyze it together.
For example, a company may have:
- financial data in an accounting or ERP system;
- customer and sales information in a CRM;
- additional plans or calculations in Excel;
- inventory, manufacturing, e-commerce, or other data in separate systems.
Data from these sources can be connected within a common analytical model, allowing information from one system to be enriched with data from others.
For example, a CRM system may show the activities of a sales manager and the sales process, while the accounting system provides actual revenue, cost of goods sold, and expenses. By connecting these data sources, a company can analyze not only sales volume or activity, but also actual financial performance and profitability.
Combining data from multiple systems has another practical benefit. Comparing information across different sources often reveals inconsistencies, missing levels of detail, or differences in how data is recorded. This not only helps create more comprehensive analytics but can also highlight areas where data entry, classification, or other internal processes could be improved.
How Should a Company Prepare for Power BI Integration?
Before starting a Power BI implementation, a company does not need to have a complete list of all desired reports, metrics, or analytical dimensions prepared in advance. Nor does all of the data need to be perfectly structured and ready for analysis.
At the beginning of a project, our priority is to understand the company’s operations, business model, and systems. It is useful to discuss how the company currently analyzes its data, which reports are prepared manually, where the analytics process consumes the most time, and what information is most frequently needed for management decisions.
The client does not need to design the future Power BI analytics solution themselves. Drawing on our experience in financial and business analytics projects, we prepare the initial set of reports and adapt their structure, calculations, and analytical dimensions to the company’s business model and available data.
During the initial implementation, we create a set of financial and business analytics reports tailored to the company. Depending on the company’s operations and available data, this typically includes:
- Sales analytics
- Cost and gross profit analytics
- Operating expense breakdown
- Other and financial activities analysis
- Profitability overview
- Balance sheet report
- Cash flow report
- Inventory and turnover analysis
- Turnover ratios report
- Detailed debt analysis (including receivables, payables, and prepayments)
- Debt control reports
You can see what these reports look like in practice in our interactive Power BI demo report.
This is not a standard reporting package applied in the same way to every company. The analytics structure is tailored to the specific business – its areas of activity, organizational structure, product and customer segmentation, chart of accounts, and other characteristics relevant to analysis.
A company may already have specific requirements at the beginning of the project. For example, it may want to monitor company-specific KPIs, analyze particular business processes, or use existing management reports. These requirements can also be included in the analytics solution.
Our experience shows that analytics needs rarely end with the initial implementation. Once users start working with Power BI reports and analyzing their data in greater depth, new questions and requirements naturally emerge. Sometimes an additional metric or calculation is needed, sometimes a new analytical dimension, and in other cases an entirely new custom report is created to analyze a specific business process.
A PLY Business analytics solution can be continuously expanded and improved as the company’s needs evolve.
What Problems Commonly Emerge During Data Integration?
In our experience, many of the questions that arise during analytics projects are not related to Power BI itself or to the technical connection to a system, but rather to data quality and business logic.
For example:
- different systems may use different codes for the same customer or product;
- categories or classifications required for analysis may be missing;
- information required for analysis may be incomplete, or some data may not be assigned to the appropriate categories;
- some information may be maintained manually;
- the data required to calculate a desired metric may not be collected at all.
As a result, implementing analytics sometimes reveals not only what a company can analyze, but also where its data collection and management processes could be improved. Even a well-designed Power BI analytics solution cannot fully compensate for poor-quality or incorrectly entered source data. Therefore, data quality issues identified during integration become an important part of the analytics project.
How Often Is Power BI Data Refreshed?
The frequency of data refreshes depends on the company’s specific needs. For financial and management analytics, it is usually not necessary for every new transaction to appear in Power BI immediately after it is recorded in the accounting system.
What matters more is that data refreshes are automated, reliable, and performed at a frequency appropriate for making timely management decisions.
We therefore determine the refresh frequency based on how often the company analyzes particular metrics and makes decisions based on them. In practice, we most commonly refresh data automatically once a day, although more frequent refreshes can be configured when needed.
Power BI Integration Is Only the First Step
The outcome of a successful analytics project is not simply connecting the data.
Integration creates the foundation. The real value emerges when data from different systems is transformed into clear, consistent analytics tailored to the specific needs of the business.
When developing PLY Business analytics solutions, we consider the entire process – from understanding business needs and data sources to integration, analytical model development, and the creation of interactive Power BI reports.
The ultimate goal, therefore, is not to have more data or more reports.
The goal is to have information that helps you understand your business better and make data-driven management decisions.
Want to See How This Could Work for Your Business?
If your data is currently spread across different systems or you spend significant time preparing reports manually, we can assess how it could be connected and used for Power BI analytics.
