Industrial data acquisition software 2026: Excel vs. professional
Summary
Industrial data acquisition software replaces Excel — compare the differences, the benefits and the costs.
Picture the scene: every morning the shift supervisor at a power plant opens an Excel file into which the night shift has written boiler temperatures, pressures and flue gas values by hand. The file is called "Measurements_2026_FINAL_v3_corrected.xlsx". Last week's data is in a different file. The data from a month ago is somewhere on a network drive — or perhaps on someone's personal machine.
This is not an invented example. In 2026 a significant share of Finnish industrial plants still collect process data manually or semi-automatically into Excel spreadsheets. At the same time the automation systems — Siemens, ABB, Valmet, Beckhoff — produce thousands of measurement points every second, but that data goes unused, because it is not collected systematically.
In this article I compare Excel-based data acquisition with professional industrial data acquisition software. I go through the concrete differences, risks and benefits, so that you can judge when it is time to move on.
Why is data acquisition the cornerstone of industrial digitalisation?
Industry 4.0, energy efficiency, emissions reporting, predictive maintenance — every one of these depends on reliable, continuous and automatic data collection. Without data there is no analysis, and without analysis there are no well-founded decisions.
The requirements of industrial data acquisition are in an entirely different class from those of an office environment:
- Volume: a typical plant produces 200–2000 measurement points per second, continuously, 24/7
- Time criticality: a process deviation has to be detected within seconds, not days later
- Reliability: there must be no gaps in the collection, because missing history cannot be recovered
- Retention: legislation and quality management require data to be retained from months to years
- Security: production data cannot be put into cloud services without a risk analysis
Excel was not designed for these needs. It is a spreadsheet program, not a data acquisition system.
The 7 pain points of Excel-based data acquisition
Many industrial plants start with Excel, because it is familiar and already to hand. But familiarity does not mean suitability.
1. Manual entry causes errors
When a person writes down measurement values by hand, errors are inevitable. A misplaced decimal point, a misread value, a forgotten entry — each of these distorts the data. In manual data entry the error rate is typically 1–5 %. When there are hundreds of measurement points, that means dozens of incorrect values every day.
2. Real time is missing altogether
Excel does not update by itself. It shows whatever was last typed into it. Process deviations are noticed hours or even days later — sometimes only once the damage has been done.
3. Scalability runs out quickly
Excel starts to slow down noticeably once the row count passes a hundred thousand. With industrial measurement data that limit is reached in days. A year of data from a single plant can mean tens of millions of rows.
4. Version control is chaos
"Measurements_v2_FINAL_corrected_Mikes_version.xlsx" — does that sound familiar? When several people handle the same data, overlapping versions appear. Nobody knows which file is the correct and current one.
5. Automatic reporting is impossible
Daily, weekly or monthly reports cannot be generated automatically from Excel files. Every report takes manual work: copying, formatting, updating charts. This typically consumes 2–5 hours a week.
6. Security is non-existent
Excel files are copied onto USB sticks, sent by email and saved on local machines. Who has access to which data? Who edited the file last? There are no answers to these questions.
7. Integration with automation systems is missing
Excel does not support the OPC UA protocol. It does not connect directly to Siemens, ABB or Valmet systems. The data always has to be exported into some intermediate format first, which adds work stages and opportunities for error.
What does professional industrial data acquisition software do differently?
Automatic, continuous data collection
The software connects directly to the automation system using the OPC UA protocol and collects measurement values automatically, without human intervention. Every value is stored in the database with a timestamp. No manual errors, no forgotten entries, no gaps.
For example, DataPortia™ collects up to 2000+ measurement points per second from ten simultaneous OPC UA connections and stores them in a TimescaleDB time-series database.
- 172M
- rows per day — that is the capacity professional industrial data acquisition software reaches. In Excel this is simply impossible.
Real-time trends and dashboards
When data is collected automatically, visualising it in real time becomes possible. Interactive trend views update once a second, and the user can zoom, pan and compare hundreds of measurement points — directly in the browser.
Automatic reporting
Daily, weekly and monthly reports are generated automatically in CSV or PDF format. No more manual copying and formatting. Reports can be scheduled and delivered automatically — which typically saves 2–5 hours a week.
Long-term history management
A time-series database is designed for storing and querying billions of rows. Data is compressed automatically, and measurements from years back can be retrieved in seconds. This is critical for the documentation that legislation requires.
Local AI
The newest industrial data acquisition software offers local AI analysis. DataPortia runs Ollama models directly on the plant's own server: anomaly detection, forecasts and cost optimisation — without the data ever being sent to the cloud.
Practical example: a power plant moves to professional data acquisition
Imagine a district heating plant with three boilers, dozens of heat exchangers and hundreds of measurement points.
A power plant: from Excel to professional data acquisition software
Practical example
Before (Excel)
- Shift supervisors write down the hourly readings by hand 3× a day
- The monthly report is put together by hand — it takes 4–6 hours
- Deviations are noticed only at the start of the next shift
- Historical data scattered across dozens of files
- Energy efficiency optimisation rests on estimates
After (data acquisition software)
- 500 measurement points collected automatically every 10 s, 24/7
- Weekly reports are generated automatically as PDFs
- Real-time trends — deviations are visible immediately
- 24 months of historical data retrievable in seconds
- AI detects anomalies and suggests savings
- 200–300 h
- saved every year on reporting alone. That is 5–7 full working weeks freed up for more productive work.
When is Excel enough — and when is it not?
It is fair to say that Excel is not always the wrong tool. If there are fewer than ten measurement points and the data does not need to be kept for longer than a month, Excel can be enough. But the limit is reached quickly.
| Situation | Excel | Data acquisition software |
|---|---|---|
| Fewer than 10 measurement points | Enough | Overkill |
| 50–2000 measurement points | Too slow | Designed for this |
| Real-time monitoring | Not possible | One-second latency |
| Automatic reporting | Manual work | Scheduled reports |
| Historical data for years | File chaos | Time-series database |
| OPC UA integration | Not supported | Native support |
| Several users | Version conflicts | Browser-based |
| AI analysis | Not possible | Local AI |
How do you choose the right industrial data acquisition software?
If you have decided that Excel is no longer enough, the next step is choosing the right software. Here are five criteria:
- OPC UA compatibility — Make sure the software supports the OPC UA standard natively. This guarantees compatibility with all the significant automation suppliers: Siemens, ABB, Valmet, Beckhoff, Schneider Electric, Honeywell, Rockwell Automation.
- Scalability — How many measurement points can the software collect? How long can the data be retained? An approach built on a time-series database scales to billions of rows.
- Security — Does the software run locally (on-premises) or does it require a cloud connection? For industrial production data, running locally is often the only acceptable option.
- Ease of deployment — How quickly can the software be put to work? Are separate consultants needed, or can the installation be done independently? DataPortia, for example, can be installed in under 30 minutes.
- Total cost — Compare not only the licence price but also the hidden costs: training, maintenance, infrastructure, working hours lost to manual collection. Professional industrial data acquisition software typically pays for itself within months.
Summary: your data deserves better than an Excel spreadsheet
In 2026, an industrial plant that collects process data into Excel spreadsheets is leaving money, time and safety on the table. Automation systems produce enormous amounts of valuable data — the only question is whether it is collected and put to sensible use.
Professional industrial data acquisition software automates the collection, provides real-time trends, generates reports automatically and makes AI-based analysis possible — all locally, with no dependence on the cloud.
Moving away from Excel does not mean a complicated IT project. It means installing the software, establishing an OPC UA connection and selecting the measurement points. After that the data flows automatically.