According to one recent report, only 5% of enterprise executives trust their data and have a high amount of confidence in it. The other 95% aren’t so sure.
If you’re implementing ERP software, it’s critical to ensure the data you’re moving is clean, accurate, and correctly formatted. After all, what good is a brand-new software solution if it’s running on information that’s outdated or unclean?
From duplicate entries to inconsistent field naming, there are many data issues that could result in ERP failure. Today, we’re looking at the top data quality issues in implementing an ERP system.
A Failed Payroll System Implementation
Panorama’s Expert Witness team was retained to provide a forensic analysis and written report to the court regarding the failed implementation of a major software developer’s ERP/payroll system.
The Dangers of Poor Data Quality in ERP
Ensuring data quality means planning, implementing, and controlling your organization’s workflows to make sure your data meets the following criteria:
- Is fit to be used
- Is consistent and accurate
- Meets the needs of your employees
- Meets the needs of your customers
- Answers the questions that apply to it
A successful ERP implementation is one that seamlessly integrates the various technology, processes, and data across your organization. In other words, as real-time information travels across your functional areas, it should not only be accurate but it should be easy to access, use, and understand.
If it isn’t, then your organization could incur several risks. Pushing forward with a project while ignoring poor-quality data could have the following consequences:
- Higher operational costs
- Lower team member performance
- Poor decision-making processes
- Lower levels of customer satisfaction
As your employees spend time addressing and correcting data issues, they have less bandwidth to devote to mission-critical initiatives.
At the same time, working with poor data can make it impossible to meet certain compliance standards. Especially in fields like healthcare and manufacturing, this is a concerning liability.
Understanding the Attributes of Data Quality
The first step to preventing poor-quality data from inundating your ERP system is understanding what constitutes good data quality.
There are six attributes of operational data that you can measure against defined standards. By doing so, you can see how your data stacks up and identify why it could be falling short.
Put simply, high-quality data is:
- Complete
- Consistent
- Timely
- Accurate
- Valid
- Full of integrity
Let’s look at each of these dimensions in greater detail:
1. Complete
Complete data is comprehensive data. By ensuring completeness, you can rest assured that your data fully answers any relevant question that you ask of it.
If you’re missing values due to data collection issues or user error, then this could create a bias within your information set.
It could also make it more difficult for users to perform supply chain management workflows and satisfy customers.
2. Consistent
Data inconsistencies often occur when information is transferred from legacy systems to modern ERP software. This can result in data that’s duplicated, mismatched, or misplaced.
An issue as seemingly benign as using a different field name in a dataset could have major consequences in an ERP implementation.
3. Timely
Real-time data fuels informed decision-making. If your executives are basing their next move on old data, then they could easily make a decision that is no longer relevant to the current situation.
Your ERP data must provide up-to-the-minute information if you want to keep pace with (and eventually outpace) your competitors.
4. Accurate
Data must be both correct and make sense in a real-world scenario. A dataset might have been accurate years ago, but if the scenario has changed, then it is no longer accurate.
If data that’s a few minutes old can be irrelevant, then surely you shouldn’t rely on data that’s years old.
5. Valid
Within a given dataset, there are certain conditions that a field must satisfy to be considered valid. Those conditions are based on your business requirements.
Most ERP systems allow you to restrict field entries to only allow pre-defined values, which can help prevent entry errors.
6. Full of Integrity
To have integrity, data must be reliable. This goes back to the statistic we mentioned in the intro. If you can’t trust your data, then its integrity levels are low.
Without this critical attribute, all your data can be rendered useless. While some of your data is likely trustworthy, how will you know which data this is?
How to Ensure Data Quality
Ensuring data quality in an ERP implementation begins with developing a plan that includes steps to:
- Identify data fields that are specific and unique to your business
- Create validation rules to standardize data
- Cleanse your data before migration
Start by focusing on high-priority data and work outward from there. When you’re confident that you’ve standardized your data at the point of entry, you can validate its accuracy. This is where you can use specialized tools to eliminate duplications and fill in missing values.
When these steps are completed, it’s time to analyze the data to understand how the issues occurred in the first place. Look for trends in missing or incomplete data to prevent a reoccurrence.
Speaking of reoccurrence, we recommend developing a change management plan and assigning data owners to ensure employees follow the correct processes when entering and manipulating data moving forward.
Your new system will function infinitely better than your legacy software if employees adhere to defined processes instead of using the creative workarounds and sloppy practices of the past.
As a final measure, you can implement robust testing and verification procedures to continually improve data quality (because we all know humans aren’t perfect and some errors will inevitably occur).
Avoid Data Quality Issues in Implementing an ERP System
Bad data can be a big problem. If you’re on the cusp of a digital transformation, it’s important to make sure you’re working with data that meets each of the six dimensions described above.
You can avoid most data quality issues in implementing an ERP solution by creating validation rules to standardize your data and cleansing it before it enters the new system.
Our ERP implementation company can help you prepare for ERP data migration early in your project so you can avoid dragging out the expensive implementation process with needless data drama. Contact us below for a free consultation.
