If you’re considering expediting your ERP implementation, you may want to think twice . . .

Data insights from a hastily implemented ERP system may lie to you.

Today, we’re exploring the data issues that can result from rushed ERP implementations and how to avoid these problems.

3 Data Issues Caused by ERP Implementation Missteps

1. Inaccurate Market Analysis

Faulty data insights can lead to incorrect assessments of market trends, customer preferences, and competitive landscapes.

For example, a retailer that relies on incomplete or outdated data may misjudge market demand, leading to poor product development or ineffective marketing strategies.

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.

2. Flawed Predictive Modeling​

Faulty data inputs can result in inaccurate predictions. If historical data used for forecasting fails to capture relevant variables or fails to account for changing market dynamics, the resulting predictions may be unreliable. This can lead to issues such as inadequate resource allocation and poor inventory management decisions.​

3. Ongoing Data Issues​

Failing to establish data governance when implementing new ERP software cripples your ability to spot data quality issues, such as duplicate records or incomplete datasets. You need a basis for validating the accuracy and reliability of data so you can easily identify issues and address them before they become ongoing issues

Data Quality Considerations

Our software experts have worked on a number of ERP failure cases where the project analysis revealed that data quality control measures were essentially nonexistent. Here are some data quality dimensions that should be applied to every ERP implementation:

1. Accuracy

Data accuracy refers to how closely the data describes the real-world scenario. You need a process to analyze and profile each data set. The values in each field must make sense.

2. Completeness​

Data quality depends on completeness. Results based on a dataset with missing values are highly likely to be flawed. You need data quality controls that work to prevent data collection issues and entry errors.

3. Consistency

Data inconsistencies can happen when migrating data from legacy systems to an ERP system.

You must establish processes for identifying issues like duplicate records. In addition, you need consistent data definitions and uniform data practices across the systems throughout your organization.

4. Integrity​

If your ERP data is unreliable, it might be because you lack quality control measures to ensure the integrity of your data.

During ERP implementation, be sure to establish processes to protect data against:

• Human error
• Transfer errors
• Misconfigurations
• Insider threats, cyberattacks, and malware
• Compromised hardware

Access controls and maintaining an audit trail are two important strategies for protecting data integrity.

5. Timeliness

Data must be up-to-date and current. Automation is essential if you want real-time updates.

Cultural Considerations

A data-driven culture is one that promotes critical thinking and ensures data literacy. This helps ensure data quality and maximizes the amount of information you have access to when making business decisions.

Developing a data-first mindset takes time and it starts at the top. Executive support is key as you model this type of thinking.

Design Your ERP Implementation to Support Better Business Insights​

An ERP implementation is supposed to result in better business insights. However, ask any software engineering expert witness, and they will tell you that this doesn’t always happen.

Panorama Consulting Group can help you proactively prevent common data issues. We’ll help you ensure data reliability by following ERP implementation best practices. Click below to learn more.

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