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Learn why ERP data quality is important

Poor data quality can negatively affect various aspects of a company's operations. Learn what causes poor data and some steps to take to ensure ERP data quality.

Data quality is important for all systems, and the same holds true for ERP data. Supply chain leaders must ensure their ERP data is clean so their company operations can remain successful.

High turnover and lack of business process documentation can cause poor ERP data quality. Poor software integration and poor employee performance -- for example, entering data after a certain deadline -- can also lead to issues.

Learn more about why ERP data quality is crucial for all organizations using the software.

Why is ERP data quality important?

Various departments in an organization use ERP data, so poor quality data can affect many aspects of operations. For example, inaccurate ERP data may lead to accounting problems. Supply chain leaders will find it difficult to plan for the future if poor data leads to inaccurate raw material orders. Customers may decide to shop elsewhere if inaccurate data causes inventory shortages.

In addition, an employee entering data into ERP software after a deadline can lead to delayed shipments and missed orders.

The 5 standards for data quality

Every organization should adhere to five key standards for data to ensure quality. Here are the five standards that data should meet.

1. Precision

Data in a system must include enough information to be helpful to users.

For example, if sales results do not include the numbers from a year ago, then employees aren't able to compare the sales results to other data.

2. Timeliness

Timely data entry also helps organizations meet data quality standards.

For example, employees may have to enter data in the ERP system by a certain deadline to support additional processes within the company. These time-sensitive data elements can include QA checks, sales orders, inventory checks, time sheets and production data.

3. Reliability

Reliability is another key standard for data quality.

If an end-of-month sales number is $2,345 in one system and $2,335 in another system, then the data is not reliable. Users should ensure their data is consistent.

4. Validity

Confirming data validity can also help ensure the data going into the ERP is accurate. For example, a user may enter the last two digits of a year, but the system requires all four digits.

If ERP software is not able to detect invalid data automatically, then periodic audits and reporting can help identify quality issues.

5. Integrity

Data integrity can help ensure the information is of high quality. For example, if a user accidentally spells an employee's name wrong when entering it in the system, then the data does not have integrity.

4 steps for ensuring ERP data quality

Employees must be proactive about ensuring ERP data quality. Here are some steps supply chain leaders and others can take to ensure the data stored in ERP software is clean.

1. Conduct end-user training

One of the best ways to reduce or avoid data quality issues is by properly training users and emphasizing that meeting data quality standards must be a part of everyday activities.

Learning management systems can provide interactive training for users, including testing them on their knowledge of ERP systems.

2. Use ERP software's validation capabilities

Many ERP platforms can validate data as users enter it and display an error message if data does not meet standards.

Some ERP software also enables administrators to implement custom business rules so users will receive error messages if their data entry does not align with those rules. For example, some systems will stop a user from entering a new order for a certain product if the product is unavailable.

3. Carry out exception reporting

Reports can flag any abnormal data elements.

ERP software's exception reporting can provide users with a daily or weekly summary of business elements such as orders or invoices, enabling users to review the reports and confirm data accuracy.

4. Complete system documentations

Documenting business processes can also help ensure data quality.

A manager training a new employee may misremember a particular aspect of data entry or the trainee may misunderstand how to enter certain data. Documenting processes can help ensure all employees are entering data the same way and cut down on errors.

Reda Chouffani runs the consulting practice he co-founded, Biz Technology Solutions, Inc. He is a healthcare informatics consultant, cloud expert and a business intelligence architect who helps enterprise clients make the best use of technology to streamline operations and improve productivity.

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