10 June 2021

What is the Importance of Data Quality in ERP Systems?

Importance of Data Quality in ERP Systems

Did you know that data quality is critical for successful Enterprise Resource Planning (ERP) implementations? After all, an ERP system is only as good as the data flowing through it. Duplicate entries, forgotten fields, incorrect addresses, invalid email addresses, manual errors in data entry…... these are just some of the data quality challenges you face in ERP implementation and management. And it's not just the inconvenience that organizations face due to poor data quality. According to Gartner's research, organizations believe poor data quality to be responsible for an average loss of $15 million each year.

What is Data Quality?

Let's get down to the basics and understand what data quality means. According to an article on DATAVERSITY, 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, to assure it is fit for consumption and meet the needs of data consumers." Data quality is high if it is fit for its intended use in operations, decision-making, and planning.

Why Worry about Data Quality in ERP Implementation?

Most of the business processes in an ERP system are interlinked and automated, which means real-time information in an ERP is shared across different functional areas. So, data quality issues in one module will affect the quality of the information and the functioning of other modules in the ERP system.

Effects of poor data quality in ERP systems

  • Increased operational costs (with time and resources spent in detecting and correcting errors)
  • Inefficient decision-making
  • Lowered customer satisfaction
  • Inability to meet data compliance requirements

Apart from these effects, data quality issues, if not fixed, can lead to ERP implementation failure. To-Increase understands that high-quality data is critical for enabling digital businesses and offers a data governance tool, the Data Quality Studio.

Data Quality Studio is a solution for Microsoft Dynamics Finance and Operations (D365FO) to ensure high-quality master data and transactions, improve reliability in reporting, and reduce data entry errors.

To understand the importance of data quality in ERP, let's look at data quality dimensions.

The Dimensions of Data Quality

A data quality dimension is a feature of data measured or assessed against defined standards to determine data quality. There are six core data quality dimensions, which can help you understand the many ways in which your data could be falling short for successful ERP implementation or data migration.

                                          6 Data Quality Dimensions

1. Accuracy

Accuracy refers to the degree to which data describes the real-world scenario. This data quality dimension answers the question, "Is the information correct and relevant to the real world in every dataset?" This data quality dimension highlights the fundamental analysis and profiling required to understand your data set. Before you do any reporting, you need to look closely at each field to ensure its values make sense.

For example, when verifying a customer's email address, you want to check if the domain exists and refers to a real organization. For this purpose, you could ideally set up a web service connection to an email and address validation service provider. Similarly, you need to check if, e.g., the bank account or tax-exempt number is not only meeting a checksum validation but can be verified online or by some authorities.

2. Completeness

Data completeness denotes whether the information is comprehensive. This data quality dimension answers the question, "Is all the data that you need available?" Results based on a dataset with missing values could be biased. Missing values can arise due to data entry errors or data collection problems. If information is incomplete, it might be unusable.

Here's an example. Without specific details in master records, there might be errors in processing transactions or missing vital information for analysis purposes. For instance, this could be a payment method or individual dimension values on customer or product master.

As a mitigation measure, with Data Quality Studio, you can set specific fields as mandatory, with validation rules and potentially some conditions.

3. Consistency

When organizations move from legacy systems to modern ERP systems such as Microsoft Dynamics 365 F&O, there is a possibility of data inconsistencies creeping in during migration. If data entered in the legacy system did not match the new ERP pattern or the old files had different column names, or the data format is different, all these can cause inconsistencies in data. This data quality dimension answers the following question, "Is there any duplicate or mismatch of data?".

Preventing duplicate records is critical during ERP implementation. For example, we need only one external item ID used per customer or vendor. You then need to set up duplicate check rules. Potentially, you also want to define some checks, but with the option to have exceptions. There might be times when you need to enter bank account numbers across all vendors. But what if there is a chain of companies that uses the same vendor bank account? You can set up an exception with a warning for such scenarios and let the user continue with data entry.

4. Integrity

Data integrity refers to whether your data is reliable. It answers the critical question, "Can I trust my data?". Without data integrity, the efficiency of your ERP system and your business as a whole can be affected.

How would you know if the relations between tables and columns are correct? Within the ERP system, you can test this from the design point of view. E.g., in the Tax code table, you define which codes are required for your business. When performing transactions, you can use only the ones that are set up in the table. However, when integrating the ERP with other applications, there might be a challenge.

Here's an example. An organization implemented Dynamics 365 Finance & Operations (F&O) and wanted to integrate with an eCommerce solution. In the ERP, the sales tax code entered was sometimes in uppercase and sometimes in lowercase. Although this is considered as the same sales tax code in the ERP application, it was considered a unique entry depending on the casing in the eCommerce solution, leading to issues.

5. Timeliness

Real-time decision-making requires real-time data. Timeliness denotes whether your data is up-to-date at the time it is required. It answers the crucial question, "How up-to-date is my data? Can it be used for real-time reporting?".

Configurations for mandatory fields can ensure timeliness. There are also options to set up auto-completion rules so that data is filled in whenever required. The most simple example would be making fields mandatory or set auto-completion for certain fields on master data. By using journals or entering orders, the data is already available and will increase the experience for smooth data entry. Marking fields mandatory could also be conditional. E.g., on the project details, we don't need scheduling details when it is still in an initiating phase. The expected start and end date should be there before you can set the project status to in-progress.

 6. Validity

A field in a dataset may have to satisfy certain conditions to be considered valid. This data quality dimension answers the question, "Is the data valid according to the business requirements?".

For example, in ERP applications, various fields allow many valid values. E.g., specifying a credit rating or credit limit. The credit rating field in Dynamics 365 F&O is a free text field. A use case would be to restrict it to allow some pre-defined values that are meaningful for your business. You might also need maximum values for the credit limit. It may not be restricted to a single maximum value, as it can have a specific range based on the customer group or the value in the credit rating.

Next Steps

Dirty data exists when there are inaccuracies or inconsistencies within a collection of data. Incorrect data in an ERP can result in incorrect billing, packaging, documentation, or inventory. The result is unhappy customers.

An effective ERP implementation always follows an efficient data migration policy. After ERP implementation, if you don't have checks in the ERP to prevent human error, you will continue to have dirty data.

Working to ensure that your ERP system, such as Microsoft Dynamics 365 F&O has the most accurate data, can be a considerable challenge. However, this process is simplified with tools such as Data Quality Studio (DQS) from To-Increase. 

After all, human error can happen anytime, but having validations in place to monitor data quality is a step in the right direction. Take the first step toward data quality by watching our on-demand webinar on Data Quality Studio.

Watch our on-demand webinar to learn more about Data Quality Studio for D365 F&O

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André Arnaud de Calavon
André Arnaud de Calavon,
André Arnaud de Calavon,
Product Manager

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