.png)
SAP Data Quality: Is Your Data an Asset or an Anchor?
- Adsotech
- 2 days ago
- 4 min read
Truths About Fixing It Fast
The Data Cleansing Dilemma
Espoo, Finland - 15.01.2026 Many companies spend millions on large programs like S/4HANA, only to see progress slow down before real work even begins. In most cases, the reason is the same: poor data.
Low-quality data acts like dead weight. It blocks analytics, slows down AI work, and weakens every process that depends on reliable SAP data. Business users know clean data is required, but fixing it often feels slow, complex, and out of reach.
This creates a familiar problem. Traditional data work is usually run through IT, planned in long phases, and slow to show results. The business needs fixes now to keep work moving, but the usual approach promises months or years of effort. As a result, many companies stay stuck, unable to move forward with their SAP plans.
So how do you get visible results quickly while still building something that lasts—without launching a massive program?
Below are five practical truths about a business-driven way to improve SAP data quality and see results early.
Takeaway 1: Stop Waiting for IT. Put Business Users in Charge.
One of the biggest changes in how SAP data is fixed today is business-led cleansing.
Instead of logging tickets and waiting, data corrections are handled by the people who work with the data every day. They already understand what the fields mean and how mistakes affect their work.
The process follows three clear steps:
Extract - Data is pulled from SAP using clear rules that highlight missing or incorrect values.
Correct - Business users review the data in a guided Excel file. They correct values in a tool they already use and trust.
Upload - The corrected data is loaded back into SAP using controlled mass updates, with full tracking and checks.
This removes long handovers between business and IT. The people who know the data make the corrections, which speeds things up and reduces errors caused by guesswork.
Takeaway 2: It’s Not About Bad Data. It’s About Broken Processes.
Fixing data only matters because of what happens when the data is wrong.
Missing or incorrect SAP fields cause real problems in daily work, and those problems often block larger efforts like an S/4HANA move.
Here are a few common examples:
Order Fulfillment - If the Delivery Plant field is missing, sales orders cannot run through standard processing. Orders are delayed, stock is assigned incorrectly, and manual work increases. Customers feel the impact quickly.
Business Reporting - When the Material Statistics Group is empty or wrong, sales reports become unreliable. Demand planning suffers, and product performance is hard to measure. Decisions are made using numbers that cannot be trusted.
Finance and Accounting - A missing Material Account Assignment Group leads to postings going to the wrong G/L accounts. Month-end close takes longer, corrections pile up, and audit risk increases.
In each case, the issue is not “bad data” in general. It is a specific field causing a specific business problem.
Takeaway 3: Start with a 28-Day Pilot—Then Keep Going
Adsotech proposes a focused 28-day pilot as a practical way to get started.
The idea is not to fix everything at once. The pilot targets one problem area where poor SAP data is blocking a real business process today. That could be order handling, reporting, or finance postings. The goal is simple: clean the data that is causing friction right now and show clear results within weeks.
Just as important, the pilot is not treated as a standalone cleanup.
Data quality does not have a clear start and end date. New data is created every day, and without a repeatable way of checking and correcting it, the same issues return. The 28-day pilot is designed as the first step toward ongoing data work, using the same tools, rules, and ways of working after the initial cleanup is done.
The pilot follows six steps:
Define scope and success - Pick one area where poor data causes daily problems.
Installation and connections - Set up the tools needed to extract and update SAP data.
Kick-off and joint review - Look at the data together and agree on what “correct” means.
Define data rules - Apply clear rules so corrections stay consistent.
Business-led cleansing - Business users correct the data themselves.
Review and upload - Check the results and load the corrected data back into SAP.
After the pilot, the same setup remains in place. The work continues using the same products and data checks, shifting data quality from a one-time task to ongoing maintenance.
Takeaway 4: Go Beyond Empty Fields.
Good data work is not limited to finding blanks.
Many SAP issues come from fields that are filled in but do not match each other. These errors are harder to spot and often affect hundreds of records at once.
By reviewing field combinations across large data sets, it becomes clear when values do not belong together. For example, a Material Price Group may be used with the wrong Account Assignment Group across many materials. A simple check for empty fields would never catch this.
Rule-based checks make these patterns visible and help prevent the same mistakes from returning.
Takeaway 5: Keeping SAP Data Quality Under Control Over Time
A short pilot is not just a cleanup exercise.
Done properly, it delivers two outcomes at the same time:
Quick Results Critical data is corrected, manual work is reduced, and blocked processes start running again within weeks.
Repeatable Process The same extract, correction, and upload steps can be reused. Business users keep control of the data instead of handing the problem back to IT.
After the pilot, the same data flows and scripts can continue to be used through a subscription. What starts as a focused cleanup becomes a steady way of keeping SAP data in good shape.
Conclusion: What’s Your Next Move?
Improving SAP data does not have to be slow or IT-heavy.
A focused, business-driven approach can fix real problems quickly and make future work easier. The key is to start small, tie data fixes to real process issues, and let the people who work with the data handle the corrections.
If you could fix one SAP data issue in the next 28 days, which process would benefit first?
Comments