Data preparation, cleaning and transformation processes are present in virtually every company, yet they often rely on manual work, Excel operations, and repetitive steps. Whether it’s financial, HR, sales, procurement, logistics, or even ESG reporting, data processing in all these areas requires substantial manual effort: collecting and reconciling data, formatting, validating, and merging spreadsheets – all of which introduce a high risk of error.
In contrast, automated reporting provides a controlled, reproducible and secure workflow. The goal is not only to eliminate manual work, but to create a reliable, transparent and scalable reporting environment that supports the operation of the organization.
At the core of a company’s daily operations lies data. Many tasks arise that are repetitive in nature and require the processing of large volumes of information.
Why is it worth investing in data and process automation?
According to the experience of RSM’s process automation experts, automated processes:
- deliver 30–70% time savings by replacing manual steps,
- improve data quality through built-in validations and standardized logic,
- ensure consistency, as the same report is generated under the same rules in every period,
- provide a transparent audit trail for all data, and
- free up professional resources so they can focus on analysis instead of data preparation.
Automation is not just a technological advancement — it represents a shift in business mindset:
data becomes a key driver of decision-making rather than an administrative burden.
Process automation and optimization solutions
How does an automated data processing workflow operate?
Optimal process automation consists of several interlinked steps:
1. Integration and consolidation of data sources
Data from ERP, CRM, HR, logistics, and other systems is organized into a unified structure.
2. Data cleaning and preprocessing
Duplicates are removed, formats are standardized, and business rules are applied.
3. Validation and exception handling
Predefined checkpoints (e.g., tax number, amounts, and period checks) are applied, and error logs are generated.
4. Report and analysis generation
Automated reports are produced in Excel, PDF, or as visual dashboards (e.g., Power BI, Tableau).
5. Automatic scheduling and notifications
The process runs at predetermined times and sends notifications regarding results or exceptions.
This approach ensures that reports are delivered on time, with consistent quality, and transparent logic, regardless of which organizational unit uses them.
Data and process automation – practical applications
The benefits of automation are not theoretical possibilities; they deliver measurable results in daily reporting and data processing practices.
Supporting financial closings
During monthly and quarterly closings, financial and controlling data come from multiple sources — ERP systems, invoicing software, cost center records and Excel spreadsheets.
Manual reconciliation is time-consuming because the data often differ in structure, naming or period classification, therefore compiling closing reports requires coordinated effort from several team members.
An automated process standardizes these steps: the system imports data from various sources according to predefined rules, checks for completeness (e.g., missing invoices, incorrect dates, duplicate entries) and consolidates them into a unified format.
Closing reports — such as general ledger reconciliations or cost center summaries — are then prepared consistently, with erroneous entries automatically flagged in a separate list, simplifying corrections. The entire process runs within a transparent, reproducible, and auditable framework.
Tax Compliance: tax reporting and filing preparation
Preparing various regular tax filings and reports — such as monthly VAT returns, domestic recapitulative statements, or corporate tax advance payment calculations — is a recurring, data-intensive process in most companies. The main challenge in these reports is not the calculation logic, but the accuracy and consistency of the underlying data. Since source data come from different systems (ERP systems, general ledger modules, invoicing programs, Excel sheets), the reconciliation and error detection are often manual, spreadsheet-level tasks.
The goal of automated validation mechanisms is not to generate the returns and filings themselves, but to support the preparation and validation of the underlying data.
During the process, the system checks reporting and filing data against predefined rules — for example, flagging missing or incorrect tax numbers, mismatched delivery dates, inconsistent VAT amounts, or duplicated documents.
These discrepancies are automatically listed, allowing professionals to focus only on relevant issues rather than manually reviewing thousands of rows. The process can also incorporate comparisons with external sources, such as NAV’s Online Invoice system or cash register database (OPG).
Automated reporting removes most of the manual tasks from professionals, reducing error rates and shortening the time required for tax returns preparation.
Other Administrative and Corporate Data Processing Workflows
- Automation delivers tangible results not only in finance and taxation but also in the daily operations of many other organizational departments.
- In most companies, data is collected across multiple independent systems and Excel spreadsheets, while management reports and records must regularly be compiled from these sources.
- Typical examples of recurring, data-intensive reporting processes include:
- Procurement and inventory reports: order statuses, supplier performances, stock movements and goods-movement analytics from multiple sources (ERP, warehouse management, Excel).
- Sales and customer data reports: aggregated revenue summary, customer transactions or discount data from various systems; reconciling various product or partner codes.
- HR and time-tracking data: Reporting attendance, payroll or leave data when it needs to be consolidated from multiple sources (HR systems, payroll, manual Excel files).
- Compliance and internal controlling reports: preparing contract records, risk or cost reports, where source data comes from multiple files in different formats.
- As a result of automation, reports are generated according to a consistent logic from verified data sources across different company functions — whether finance, procurement, HR or operation. This not only reduces the risk of errors and the burden of manual data entry but also significantly improves data reliability and the quality of decision-making.
Profitable investment – supporting digital operations
Process automation not only increases efficiency but also provides long-term business advantages:
- data becomes more reliable
- resulting a faster, more accurate and transparent data processing environment
- control processes become more comprehensive and manageable
- the quality of decision-making improves.
Automation is also a key of a company’s digitalization strategy.
At the same time, it allows employees to focus on higher-value, analytical tasks instead of repetitive data cleaning and reconciliation activities.
Today, digitalization and data-driven operations are no longer competitive advantages—they are preconditions. Investments in data and process automation initiated today not only enhance current efficiency but also strengthen the company’s future capabilities in data quality, control and decision making support.
Frequently Asked Questions
Which areas benefit most from data and process automation?
Across all sectors of the economy, automation reduces manual effort and improves data quality in areas such as finance, taxation, controlling, HR, and logistics analytics and reporting.
How long does it take to implement a process automation project?
Depending on the company environment, implementation can take anywhere from a few weeks to several months. However, results are typically realized quickly.
What types of source data can be processed in an automated workflow?
Automated data processing solutions can flexibly handle a variety of data sources: data from ERP and invoicing systems, CRM platforms, databases or Excel spreadsheets. Common formats — such as XML, CSV, XLSX, SQL, and JSON — can be processed as well as API connections can be implemented. This allows the creation of consolidated, validated datasets in most cases.
