Are you aware that the success of Enterprise Resource Planning (ERP) implementations hinges significantly on data quality? Indeed, an ERP system's effectiveness is directly linked to the accuracy and integrity of the data it processes. Challenges such as duplicate entries, overlooked fields, erroneous addresses, invalid email formats, and manual data entry errors are common in ERP implementation and management. Moreover, the impact of poor data quality extends beyond mere inconvenience; Gartner's research suggests that organizations attribute an average annual loss of $15 million to substandard data quality.

What is Data Quality?

To understand data quality, we start with the basics. The Data Management Body of Knowledge (DMBOK) defines Data Quality (DQ) as "the planning, implementation, and control of activities that apply quality management techniques to data, ensuring it is suitable for consumption and meets the requirements of data users." Essentially, data must be adequate for its intended use in operations, decision-making, and planning to be considered of high quality.

Why Worry about Data Quality in ERP Implementation?

In an ERP system, business processes are interconnected and automated, enabling the real-time flow of information across various functional areas. Consequently, data quality issues in one module can compromise information quality and disrupt the operations of other modules.

Effects of Poor Data Quality in ERP Systems

• Elevated operational costs due to time and resources spent identifying and rectifying errors
• Compromised decision-making efficiency
• Decreased customer satisfaction
• Challenges in meeting data compliance standards

Unresolved data quality problems can also lead to the failure of ERP implementations.

To appreciate the role of data quality in ERP, let’s explore the dimensions of data quality.

Understanding Data Quality

A data quality dimension is a criterion for evaluating data against specified standards to gauge its quality. There are six fundamental dimensions of data quality, essential for understanding the various potential deficiencies in your data that could hinder successful ERP implementation or data migration.

1. Accuracy

Accuracy measures how well data reflects the real-world scenario. This dimension of data quality poses the question, "Does the information accurately and relevantly describe the real-world scenario across all datasets?" It emphasizes the importance of fundamental analysis and profiling to grasp the nuances of your dataset. Before proceeding to report, it’s vital to examine each field meticulously to ensure the values are logical.

For instance, in verifying a customer's email address, it’s advisable to check whether the domain exists and corresponds to an actual organization. This could involve setting up a web service connection to an email and address validation service provider. Similarly, aspects like bank account or tax-exempt numbers should not only pass a checksum validation but also be verifiable online or through authoritative bodies.

2. Completeness

Completeness assesses whether all necessary information is present. This dimension answers the question, "Do you have all the data necessary for your tasks?" Datasets with missing entries can lead to biased results. Such gaps may result from data entry errors or issues in data collection processes. Incomplete data could render the information unusable.

Consider this: lacking specific details in master records can lead to transaction processing errors or omit crucial data for analysis, such as a payment method or individual dimension values on customer or product masters.

To mitigate these issues, you might use Data Quality Studio to designate certain fields as mandatory, setting validation rules and conditions accordingly.

3. Consistency

Transitioning from legacy systems to contemporary ERP solutions like ERPNext might introduce data inconsistencies during the migration. If data from the old system doesn’t align with the new ERP patterns, or if file formats and column names differ, inconsistencies can occur. This dimension addresses the question, "Are there any duplicates or mismatches in the data?".

Preventing duplicate records is crucial during ERP implementation. For example, each customer or vendor should have a unique external item ID. Setting up rules to check for duplicates is necessary, although you might also allow for exceptions. For instance, a group of companies might use the same bank account for multiple vendors. In such cases, an exception rule with a warning allows continued data entry despite the potential conflict.

4. Integrity

Data integrity concerns the trustworthiness of your data. It prompts the vital question, "Can you rely on your data?". Compromised data integrity can impact the effectiveness of your ERP system and your overall business operations.

How can you verify if table and column relationships are accurate? Within an ERP system, this can be tested from a design perspective. For instance, in a Tax code table, you determine necessary codes for your operations. While integrating the ERP with other applications, however, challenges may arise.

Here's an example: After implementing Dynamics 365 Finance & Operations (F&O) and integrating it with an eCommerce platform, issues occurred because the sales tax codes were entered in varying cases, recognized differently by the eCommerce system despite being the same in the ERP.

5. Timeliness

Real-time decision-making depends on the prompt availability of data. Timeliness evaluates whether your data is current when needed. It addresses the crucial questions, "Is your data up-to-date and ready for real-time decision-making?".

Implementing mandatory fields and auto-completion rules can promote timeliness. A simple approach could involve mandatory fields or automatic data filling for certain entries on master data, enhancing the data entry process. Conditional mandatory settings might apply in scenarios like project initiation, where scheduling details are unnecessary until the project status advances to in-progress.

6. Validity

Data validity checks if a dataset meets specific required conditions. This dimension queries, "Does the data meet all the necessary business requirements?".

In ERP systems, many fields are designed to accept a range of valid inputs. For instance, the credit rating in Dynamics 365 F&O is a free text field, which might be limited to predefined, meaningful values for the business. Similarly, credit limits might vary, requiring different maximum values based on customer group or credit rating.

Avoid Data Quality Issues in Implementing an ERP System

"Dirty data," characterized by inaccuracies or inconsistencies, can cause major errors in billing, packaging, documentation, or inventory management, ultimately leading to customer dissatisfaction.

By setting up validation rules and cleansing data before its integration into new systems, many data quality problems can be avoided. Early preparation for ERP data migration, backed by expert consultation from our ERP implementation company, can lay the groundwork for success. Contact us for a free consultation.