How do you ensure data quality and accuracy when loading ERP master data?

for simplicity we can write ERP master data is important information that describes the most essential things an organization needs to run its ERP system smoothly. These things are called business objects, and they include customers, vendors, products, materials, accounts, and organizational units. This information is kept in one central place, like a big library, so that it can be easily accessed by different parts of the ERP system. For example, when you want to sell something, you need to know whom you're selling to, what you're selling, how much it costs, and when it will be delivered. All of this information comes from the master data, like a big cheat sheet that helps you remember all the important details.

Master data in ERP is like a dictionary that tells the system how to organize and store all the data. It includes things like product names, prices, customers, and employees. Without master data, the system can't organize and store information correctly. So, it's crucial for making sure the system works well and helps the company succeed.

A a manufacturing company that relies on its ERP system to manage production and inventory. The material master data describes the raw materials used in production and is a key piece of information in the system. Inaccurate, incomplete, or out-of-date material master data can cause problems such as incorrect inventory levels, delayed production, and lost revenue. Conversely, accurate and complete material master data can help the system plan for production more efficiently, maintain optimal inventory levels, and avoid costly mistakes and delays. Maintaining high-quality ERP master data involves regular data cleansing, standardization, and validation processes, as well as effective governance and management of the master data.

One thing I have found helpful is by maintaining high-quality ERP master data, organizations can improve their business operations, decision-making processes, and overall performance.

Quality of ERP Master data is critical as it has a direct implication with the dollar value. Ex:, if the Master data has incorrect dims in a DC, the direct challenges and cost factors are additional labor to handle the operational volume and "unwarranted" shipping charges due to incorrect cartonization. It could have other several downstream impacts as to incorrect labeling of Hazardous goods, or being shipped through an incompatible channel, leading to heavy fines/penalties. One way to maintain a clean record is to ensure there are reliable feeds from downstream applications to the ERP. For example: A WMS can be seen as a system of records for Item Master as WMS captures the greatest and latest item attributes and send a feed to ERP.

ERP master data quality is important because it helps companies make good decisions. If the data is bad, then the decisions can be bad too. Imagine if your teacher gave you wrong information for a test. You might get a bad grade even if you studied hard. Similarly, if the ERP system has bad master data, it might tell the company to do things that aren't right. For example, it might tell the company to make too many products or buy too much inventory. This can waste money and hurt the company. So, it's important to make sure that ERP master data is accurate and up-to-date.

In my experience to load ERP master data accurately, organizations need to follow best practices. 1. Establish data governance with clear roles and responsibilities for managing master data. 2. Define data templates with clear data fields, formats, and rules for each type of master data object. 3. Perform data profiling to identify anomalies and data quality issues. 4. Use data migration tools to automate the data loading process. 5. 5. Conduct data validation to ensure accuracy, completeness, and consistency. 6. Establish ongoing data maintenance processes for regular data validation, cleansing, and standardization.

To load ERP master data in Dynamics 365, you need to make sure that the data is clean, consistent, and in the right format. This can involve using special tools to check the data and fix any mistakes. Then, you need to put the data into Dynamics 365 in the right order, so the system can understand it. This can involve mapping the data to the right fields in the system. Once the data is loaded into Dynamics 365, you can use it to make good decisions and run the company more efficiently. It's important to make sure the data is accurate and up-to-date, so the system can work correctly.

One thing I have found useful is following best practices say in the case of customer master data 1. Establish data validation rules that specify the required fields, formats, and values for customer data 2. Validate customer data by checking it against the validation rules to ensure accuracy and completeness 3. use exception reporting to identify any data that does not meet the validation rules 4. Review the exception report to determine if the data needs to be updated or corrected 5. Update or correct the data as required and repeat the validation process until all customer data meets the validation rules 6. Establish ongoing data validation processes to ensure that new customer data is validated before being added to the ERP system

Validating ERP master data is like checking your work before turning it in. You need to ensure the data is correct, complete, and in the right format. Use tools to check for errors or missing information. Regular validation is important to catch mistakes before they cause problems.

In my experience, creating a data health dashboard in a QPR process analyser helps to monitor and maintain data quality by highlighting erroneous data. It also allows you to review the root cause of the incorrect data if a process flow is associated with it.

You need to regularly check that the data is accurate, complete, and up-to-date. Use tools to identify and fix any errors or missing information. Establish data governance policies to ensure consistency and quality. Continuously monitor data quality and conduct periodic audits to maintain high standards.

The audience for this article needs to be clear that this is about mass ETL/RETL/ELT/TEL, etc. Because iPasS changes the entire context and purpose of the article. And the word "Load", as in a large Batch job, is not a desired use of iPasS if it can be avoided

Data can be master or transactional and both are very important. Most people get into the project and then see the volume of data that needs to be converted. Most Master data are manageable but transaction although very important from statutory point of view is not thought thru. You may be loading 1 year old data but you need a clear strategy to archive old data and be able to retrieve it. There are few strategies for it and it needs a cost benefit analysis before we embark on it.

Another important aspect that many don't think about are mergers/ divestitures and co-existence scenarios. Need a clear strategy how to approach it because these are areas that can derail most projects because when they happen, they come with strict timelines. Also multiple existing systems can be a challenge too since most projects are not greenfield projects. Many are brown field ones and there can be many permutation combinations for that too. This also brings into picture a solid MDM system and robust PaaS/ IaaS architecture.

Ensuring data quality and accuracy when loading ERP master data is essential for organizations to optimize their business processes and achieve better performance. According to a recent survey by Gartner, poor data quality is costing organizations an average of $15 million per year in losses. Additionally, organizations that prioritize data quality outperform their peers by 70% in revenue and 80% in operational efficiency, according to a study by Experian. By implementing best practices for data governance, data validation, and ongoing data maintenance, organizations can improve the quality and accuracy of their ERP master data, leading to better decision-making, increased efficiency, and improved business outcomes.