The automatic reporting system that downloads data from Excel, API, CRM, ERP and databases, arranges them and presents them in the Power BI dashboards and management reports.
The model customer is a service or operating company that uses many tools: CRM, ERP, accounting program, Excel sheets, forms, design system and CSV files. The data was available but distributed, each department had its own reports and its own KPI definitions.
The biggest problem was not the lack of data, but the lack of a consistent process of processing them. The data was found in reports from different locations, in different formats and with different frequencies, so reporting depended on people who knew the files, formulas and order of operations.
In this model scenario, reporting is no longer a manual submission of files. Data from Excel, API, CRM, ERP and databases are sent to a controlled pipeline, validated and presented in Power BI dashboards.
Indicative values. Actual results depend on the number of reports, data quality, integration sources and the current reporting process.
The model client used CRM, ERP, accounting software, Excel spreadsheets, forms, a design system, and CSV files. Data was available but scattered, and each department had its own reports and its own KPI definitions.
The biggest problem was not the lack of data, but the lack of a coherent data processing process. Data entered the reports from different places, in different formats and with different frequencies.
Data must be downloaded from several systems and saved in files. Each export requires time and control to ensure that it contains the correct scope.
The same report is circulating in several files and emails. It is difficult to determine which version is current and on the basis of which a decision is made.
Formulas, ranges, and references can be easily damaged when copying data or changing the file structure.
The report is only up-to-date after manual data download, cleaning and recalculation of sheets.
Sales, finance, and operations may calculate the same metric differently, making the discussion about definitions rather than decisions.
Missing values, duplicates, incompatible dictionaries and incorrect data types are only detected during report submission.
Managers see data over time, so operational and financial decisions can be made based on outdated information.
It is not known when the data was downloaded, who corrected it, and why the result differs from the previous version.
The process worked, but was time-consuming and error-prone. Any change to data sources, column names, or file structure could have broken the report.
We design a process in which data is automatically retrieved from sources, validated, transformed, saved in an ordered structure and made available in Power BI dashboards or a reporting application.
Click or hover over a stage to see what is happening to the data at that point in the process.
Files from departments, exports and sheets are downloaded according to a set template.
Import files from folders, shared drives or forms. The system can handle repeatable templates and validate data structure.
Automatic data download from CRM, ERP, sales tools, marketing systems, design platforms and internal applications.
Leads, pipeline, offers, winning opportunities, salespeople's activity and sources of customer acquisition.
Invoices, costs, revenues, payments, receivables, liabilities and contractor data.
Operational data from internal applications, production systems, data warehouses or dedicated panels.
Reports, surveys, inputs from customers, employees or partners B2B.
The calculator shows an approximate value of time saved by reducing manual preparation of reports. This is not a guaranteed financial result.
Example: 12 reports per month, 6 hours manually, 0.5 hour after automation, 2 people and PLN 100/h gives approximately 132 hours and PLN 13,200 of approximate time value per month.
files, sheets, forms
sales and finance systems
import, validation, transformation
relationships, KPIs, history
Power BI or web application
email, Teams, Slack
management board, managers, departments
logs, permissions, backup
We analyze existing reports, data sources, preparation frequency, recipients and business decisions made based on data.
We establish one definition of indicators: revenue, margin, cost, lead, conversion, profitability, cash flow and other KPIs important for the company.
We connect data sources: Excel, API, CRM, ERP, databases, Google Sheets and section sheets.
We create data structure, relations, transformations, validation rules and refresh history.
We are building the first version of the dashboard for the most important audiences and most important decisions.
We compare the results with previous reports, identify differences and improve definitions.
We run a refresh schedule, train users and configure permissions.
We are adding more dashboards, alerts, predictions and automatic management reports.
| Area | Before implementation | After implementation | How to measure |
|---|---|---|---|
| Preparing a report | Manually merging files | Automatic data pipeline | Report preparation time |
| Data quality | Errors in files and formulas | Validation and alerts | Number of errors in reports |
| Data freshness | Weekly or monthly report | Data refreshed on a schedule | Update frequency |
| KPIs | Different definitions between departments | One data model | Number of discrepancies |
| Access | Report sent by e-mail | Online dashboard | Number of active users |
| Decyzje | Delayed data | Current metrics | Time from event to report |
Dashboard should not expose all data to all users. We design roles, access by departments, refresh logs, test and production environments, and control of sensitive data.
This case study presents an anonymized, model implementation scenario based on the typical needs of companies reporting data from Excel, Power BI, API, CRM and ERP. It does not contain customer names, financial data or information identifying a specific organization. All indicators and calculations are indicative and should be confirmed during pre-implementation analysis.
At the first meeting, we will check which reports take the most time and which data should be automated first.
No. Excel can still be used where it is a convenient input or analytical tool. The purpose of automation is to limit manual copying, merge and correct data.
Yes. Dashboard can combine data with CRM, ERP, databases, Excel, CSV, Google Sheets and API. It is crucial to build a valid data model and one definition of indicators.
Then you need a validation layer. The system can detect missing fields, duplicates, invalid date formats, inconsistent customer names or different identifiers.
Yes. You can set a schedule for refreshing data, for example every hour, every day or once a week. Frequency depends on business needs, API limits, data volume and infrastructure.
Yes. You can implement user roles, access by departments, row-level security and separate views for management, sales, finance and operations.
This depends on the number of data sources, the quality of current reports and the number of dashboards. The safest way to start is with MVP covering the most important KPI and one or two data sources.
Yes. Before implementation, it is worth counting how much time it takes to prepare reports, how many people are involved in the process and how many times a month reports are created.
Yes. We can audit reports, design KPIs, integrate data sources, prepare data model, implement Power BI or dashboard web and train users.
Yes. The system can send email alerts, Teams or Slack when sales fall, costs exceed the budget, margin is too low or data has not been refreshed.
Yes. Views can be responsive or specially prepared for mobile devices. In the case of Power BI, it is worth designing a separate mobile system for the most important KPIs.
SmartCodeIT can help you design your process, prepare MVP and implement an application tailored to your organization. We start with a short conversation about data, tools, exceptions and business purpose.