How to Reduce ERP Data Errors

Small ERP entry mistakes rarely stay small. A missing value, a wrong code, or an incomplete closeout can trigger delays, rework, audit issues, and extra supervisory effort across your operation. Deloitte and WalkMe put the problem bluntly in a recent SAP S/4HANA discussion: “Seventy percent of ERP go-live issues are attributable to humans.”

That does not mean your frontline teams are careless. It means your workflows often ask people to remember rules from training, work through friction, and make the right choice without support in the moment. In another example from the same discussion, a frontline ERP process allowed users to “close out a process without the required information,” which created “a bunch of incorrect data” and forced supervisors to review and restart work.

This article shows you a practical step-by-step way to reduce ERP data errors by supporting frontline teams inside the workflow itself. You will learn how to find the real sources of data quality problems, add in-app guidance where errors begin, and measure whether those interventions reduce rework and workflow compliance exceptions over time.

Why frontline teams struggle to reduce ERP data errors

ERP data quality problems often begin with workflow friction, not bad intent. In the Deloitte examples, users had already gone through training, but errors still appeared after go-live. Leticia Rosendahl described a frontline environment where “despite the fact that we had done training for some of these audiences,” users still submitted incomplete work because “there was nothing in the system that essentially stopped them.”

That pattern matters if you want to reduce ERP data errors in frontline operations. Workers who are “not really sitting in front of a computer” need to “do something quick,” “know what to do,” and “get it done and move forward with their day.” When field requirements are unclear or process steps are easy to skip, the result is not just lower data quality. It is incorrect maintenance orders, incorrect contracting records, and supervisory time lost to cleanup.

Deloitte also framed the broader issue in human terms: “The final mile of ERP success is the human mile.” If 70% of ERP go-live issues are attributable to humans, the answer is not more blame. The answer is better support in the flow of work. That is the promise of this approach: help employees complete live ERP tasks correctly before bad data spreads downstream.

What you will need before you start

Before you change anything, you need a clear view of where errors create the most operational pain. Deloitte’s adoption flywheel starts with “what data am I getting from our end users, from leaders, from sponsors, from customers” and then asks how to “look at those insights from a sensing perspective” and adapt. If you want to reduce ERP data errors, begin by identifying the workflows with the highest error volume, the most rework, or the greatest workflow compliance exposure.

Your baseline should be practical and tied to the live process. In the source examples, organizations looked at longer-than-expected cycle time, offline process behavior, incorrect data, and supervisory review burden. One client found “about 83% of their contract reviews were happening offline,” which immediately exposed a compliance and process execution problem. Another saw supervisors “having to take on this toll of reviewing and going back to the beginning of a process.”

You also need ownership across operations, IT, and process leaders. Deloitte described adoption as an “always-on framework” and a “continuous loop” that evolves based on real-time feedback. That only works if the people who own the ERP workflow, the system, and the frontline task agree on what correct execution looks like. Otherwise, your guidance will not match live work.

Step 1: Find where ERP data errors actually happen

Once you have a baseline, move from symptoms to the exact point of failure. Deloitte’s approach in SAP S/4HANA began by using process intelligence “to chart out what exactly was happening in that process, where the friction was, and how that was posing a challenge.” That is the standard you need if your goal is to reduce ERP data errors rather than just report them later.

Look at the specific screens, forms, and fields where users stop, guess, or move on without completing required information. In one frontline case, users “were just able to close out a process without the required information.” The result was “a bunch of incorrect data,” including “incorrect maintenance orders” and “incorrect contracting at the end of the day.” Those are not abstract data quality problems. They are visible workflow failures tied to a specific moment in the ERP process.

You should also map recurring error patterns to the business impact they create. In Deloitte’s source-to-pay example, process friction produced “longer-than-expected cycle time in their supplier onboarding process” and issues around source-to-contract. In the contract review process, 83% of reviews were happening offline, which undermined audit readiness and forced more work later. When you connect each error pattern to delayed fulfillment, invoice issues, stock inaccuracies, or supervisory rework, you can prioritize the workflows that matter most.

Step 2: Identify the friction behind workflow compliance gaps

After you find the error points, the next question is simple: why do people miss the process? In the source material, the answer was not a lack of effort. It was that “the change had not been fully absorbed” and users were treating parts of the process “a little bit as a check the box rather than fully adopting the process.” Workflow compliance tends to break down when employees must remember rules from earlier training instead of getting help during the task itself.

That is especially true in frontline operations. Rosendahl described a “very disparate workforce, very frontline worker, not really sitting in front of a computer.” In that environment, expecting people to recall detailed ERP rules from a classroom session is unrealistic. She also noted that “it’s not feasible for anyone to retain knowledge for six months before actually getting into a system” in remote shift environments. The friction is operational, not personal.

The downstream cost lands on supervisors. In the example, managers were “having to take on this toll of reviewing and going back to the beginning of a process” and “really just not having time to retrain people.” If you want stronger workflow compliance, you need to remove the conditions that make preventable mistakes easy. That means addressing unclear field definitions, manual checks, and process variation where the work actually happens.

Step 3: Add in-app guidance at the point of entry

Once you understand the friction, the most direct fix is to guide employees inside the ERP workflow. Deloitte and WalkMe describe this clearly: WalkMe closes the gap “by being there at the exact moment a user needs support, in the flow of work, in the application, at the right time.” That is how you reduce ERP data errors without forcing workers to leave the task to search for help.

In the frontline case, the intervention had two parts. First, WalkMe added prompting to the supervisor and the employee to say, “hey, did you do this right?” Second, it gave them “the option for in-app guidance of how to complete this.” That combination matters. Prompting catches risky behavior before submission, while in-app guidance explains the correct action on the same screen where the user is working.

This is more effective than static documentation or one-time training because it supports real execution, not memory. Deloitte said WalkMe helped “steer people the right direction, nudge them when they were looking like they were about to make a mistake, and make sure that we’re broadcasting loud and clear, here’s what we need you to be doing and how you need to do it.” In another phrase, WalkMe could even help “get it started for you to help make sure that it’s successful.”

The user feedback in the pilot reinforces the point. The team reported 100% participation feedback that “WalkMe is easy to navigate.” Another 86% said it “would have reduced the training time” they needed in the classroom, and 72% said it increased their confidence in using the system. Better guidance improves workflow compliance because people are more likely to follow the process when support appears in context, at the right moment, and in language they can act on immediately.

Step 4: Use screen-level context to deliver the right guidance

In-app guidance works best when it reflects what the employee is actually seeing. Deloitte described the value of “real-time insights” and “continuously evolv[ing] our interventions” based on live signals. Effective support depends on understanding the user’s role, the current workflow step, and the state of the page before you present guidance.

This is where screen-level context matters. The source material repeatedly emphasizes support “in the flow of work, in the application, at the right time” and “real-time guidance and prompting.” If an employee is about to close out a work order without the required information, the right response is not a generic policy article. It is targeted guidance on that screen, in that field state, before submission happens.

That context-aware approach helps you reduce wrong entries and speed up completion at the same time. Deloitte tied this model to “more process adherence,” less frustration, and a stronger end-user experience that can “minimize rework.” They also described WalkMe as a source of data and insight to understand “where the adoption pain points are” and then use in-application guidance “to help guide the users to the optimal outcome.” When guidance matches the live screen, supervisory intervention becomes the exception instead of the routine.

Step 5: Build controls that prevent avoidable errors

Guidance alone is useful, but the strongest programs also add controls before a transaction is submitted. In the frontline example, the ERP workflow allowed users to complete a process “without the required information.” That is a design gap. If you want better data quality, you need smart checkpoints that catch common mistakes before they become correction work.

Deloitte described a practical balance here: “making sure that we understand, we give people support that they need, but then there’s the real-time guidance and prompting and not allowing you to do what may actually be incorrect.” That is the model for guided controls. Use prompts, reminders, and guided paths to reinforce required approvals, attachments, acknowledgments, and other workflow compliance steps before the record moves forward.

The goal is not to slow people down. It is to help them complete the task correctly the first time. Deloitte framed the broader objective as “sustained adoption, sustained value, minimizing business disruption, and really making sure that we’re driving continuity in just work.” Preventive controls support that outcome because they stop avoidable errors at the point of entry instead of shifting the burden to supervisors, auditors, or downstream teams.

Step 6: Measure whether you reduce ERP data errors over time

If you do not measure the results, you will not know whether your interventions improved the process or just added more steps. Deloitte’s adoption flywheel is built around “looking at data,” “looking at feedback,” and “measure the outcomes on the back half as well.” The goal, as John Prescott put it, is to answer “with finality” and with “quantifiable metrics” whether the intervention worked.

Start by comparing pre- and post-guidance measures such as submission accuracy, exception rates, completion rates, and correction time. In the source examples, teams tracked cycle time, process adherence, incorrect data, and offline process behavior. One client used data to find that 83% of contract reviews were happening offline before intervention. Another measured user outcomes after guidance and reported more process adherence along with strong user feedback.

You should also measure supervisory rework directly. Rosendahl described supervisors “reviewing and going back to the beginning of a process,” which is a concrete cost. Track the time spent reviewing, fixing, resubmitting, and answering repeat questions on frontline transactions. Those hours often contain the hidden business case for ERP data quality improvement.

Adoption analytics then help you see where improvement is uneven. Deloitte described WalkMe as “a valuable source of data and insight to understand where the adoption pain points are” and where users are struggling. That allows you to adjust guidance continuously, prioritize the next use cases, and keep reducing ERP data errors over time rather than treating the issue as a one-time cleanup project.

Tips for success when rolling out in-app guidance in ERP

Once you have the framework, start small and build from evidence. Deloitte’s examples point to a few practical ways to improve rollout quality and workflow compliance without overcomplicating the program.

Keep these principles in mind as you begin:

• Start with one or two high-volume workflows where incorrect data creates obvious downstream cost, such as supervisory rework, cycle time delays, or audit burden.
• Keep guidance concise and tied to the immediate task. The source material stresses support “at the exact moment a user needs support” and “in the flow of work.”
• Focus on visible friction first. Deloitte found value by pinpointing where users struggled and then adapting interventions from those insights.
• Revisit guidance regularly. The source repeatedly describes adoption as “always-on” and a “continuous loop,” not a one-time deployment.
• Design for real frontline conditions, especially where workers are remote, shift-based, or unlikely to retain training delivered months in advance.

That approach makes it easier to show quick value while building a repeatable model for broader ERP process improvement.

Conclusion: a practical way to reduce ERP data errors

The fastest way to reduce ERP data errors is to support employees inside the workflow, before the mistake appears in a report or lands on a supervisor’s desk. Deloitte’s examples show the pattern clearly: find the real friction points, guide execution in context, and measure whether rework, incorrect data, and workflow compliance exceptions actually decline.

The lesson is practical. Training still matters, but it is not enough on its own. Frontline teams need help “in the flow of work, in the application, at the right time.” That is where better data quality begins.

If your goal is to reduce ERP data errors across frontline operations, the WalkMe action bar and screen-level context intelligence give you the layer to guide live ERP execution, reinforce workflow compliance, and prove where process performance is improving.