An ERP system is only as good as the quality of data flowing through it. Data and analytics leaders should establish a metrics-based approach to understand the quality and status of their data before data migration. Therefore, during a cloud ERP implementation, it is best to include a plan for data cleansing and management.
Attributes of ERP Data Quality
Data quality can be determined using several contributing factors like accuracy, completeness, relevance, validity, timeliness, and consistency. You can improve data quality through data cleansing, which is the process of identifying and correcting inaccurate or corrupt records from a database. The manual part of the process is what makes data cleansing such an overwhelming task. While most of the data cleansing can be automated, it needs to be monitored and reviewed for any inconsistencies. This can be hard to do if you don’t have a process in place.
Importance of ERP Data Quality
ERP data is becoming increasingly integral to business operations. Data-driven companies integrate data into all their business processes instead of treating it as a separate entity. Integrating data into all your business processes means that data quality can have a huge impact on ERP functionality and your business functions from finance and procurement to creating a business strategy.
Data quality is also critical because of compliance-related issues. As regulations regarding data continue to become more stringent, it’s imperative that companies manage their data efficiently and securely. If your ERP system contains inaccurate or incomplete data, it becomes harder to stay compliant. This is not only true for sensitive financial data or personal data but also for other types of business data. Data quality issues can also impact customers by leading to lost records or making it difficult to provide effective customer service.
Steps to Resolve Data Quality Issues While implementing Cloud ERP
Develop a Data Quality Plan
Identify the data that is critical for your business. Focus on high-priority data, and start small. Identify the fields that are unique to your business and document the information that you are specifically looking for. It would be beneficial to create specific validation rules at this point to standardize data formats and cleanse your data, as well as automate this process for the future. Ensuring that the information is standardized before it enters your database will make it easier to maintain it in the future.
Validate Your Data
After standardizing your data at the point of entry, validate the accuracy of your data. Research and invest in tools that can automate manual data cleansing tasks and allow you to clean data in real-time. A few modern tools even use artificial intelligence and machine learning to validate your data. You can still validate the accuracy of your data online without using these tools, but, it would require a lot of manual effort which most businesses don’t have the bandwidth for.
Remove Duplicates and Add Missing Values
Duplicate records in your database consume unnecessary time and effort and also end up costing more in maintenance and marketing spend. Duplicates can also lead to inaccurate reporting. Many tools are available in the market that can analyze your raw data in bulk and automate the deduplication process. Some records like emails, phone numbers, industry, company size, etc. cannot be automatically corrected. It’s important to find and add the missing data, whether it’s via online research, contacting relevant people, or third-party data providers.
Analyze Your Data
After your data has been standardized, validated, deduplicated, and missing values have been added, monitor for errors or trends in errors as it may be an indication of a larger problem. Later, you can analyze this data to provide better information for business intelligence and analytics. Clean and up-to-date data can support better analytics and in turn, lead to better business decisions.
Implement Automation Tools
After cleansing the data, you must standardize and cleanse the flow of new data as it enters your system by creating automated workflows. These workflows can run in real-time or in batches (daily, weekly, monthly) depending on the amount of data entering your systems. These workflows can be applied to both new data as well as the existing data in your database. Implement tools that can automate manual data cleansing tasks and streamline the process.
Communicate With The Team
Now that you’ve cleansed your data and set up automated workflows, it’s important to communicate the new standardized cleansing process to your team. This will ensure that your data remains clean in the future. You must also set up a periodic review with your team so that you can identify and resolve any issues before they become a major problem.