In the era of big data, maintaining the health of your organization’s master data is more important than ever. A thorough master data health check can not only reveal issues within your data but also provide insights into how to optimize your data management practices. In this comprehensive guide, we will delve into the various aspects of conducting a master data health check, discuss the importance of data quality, and explore the role of AI and tools like Semarchy xDM in enhancing master data management.
Understanding the Importance of a Master Data Health Check
Master data health checks are imperative for ensuring the smooth operation of any business. They allow organizations to identify and rectify data-related problems, which can significantly impact the company’s bottom line. According to a study by Forbes, data-related issues cost most companies more than $5 million annually, with some estimating losses in excess of $20 million per year.
Master data health checks also enable businesses to ensure their data aligns with their strategic business objectives. A whopping 95% of organizations agree that robust information management is crucial for business success.
Moreover, these health checks can help identify and resolve discrepancies between different departments’ perceptions of data-related problems and potential solutions1.
The Anatomy of a Master Data Health Check
A master data health check involves analyzing your data against various criteria to ensure it aligns with business model requirements. This process typically involves examining the dependencies between your master data objects and any new application-specific validation criteria.
Previously, these consistency checks had to be run for each master data type individually. However, new application jobs now allow checks for multiple master data types at the click of a button. This development enables businesses to run checks for all master data types and select versions with version-specific master data in the job template.
Key Elements of a Master Data Health Check
A master data health check typically involves several key elements. These include:
Database Configuration
The first step in a master data health check is to verify if the database is configured correctly. This involves checking if the database buffer pools or buffer caches are configured sufficiently high, whether the table and index layout match the default schema, and if the table and index statistics are up-to-date.
Schema Verification
Another critical aspect of a master data health check is schema verification. This involves checking if any changes have been made to the tables and indexes and whether these changes comply with the business model requirements.
Table and Index Statistics and Fragmentation
For optimal performance, it is essential that database table and index statistics are up-to-date. This ensures that the database optimizer can choose the best access plan to retrieve data for each SQL query.
Old Version Counts
Performance issues can arise if too many old version rows accumulate over time. Therefore, it is crucial to delete old versions regularly.
Leveraging AI for Master Data Health Checks
The advent of AI has revolutionized many aspects of business, including master data health checks. AI can speed up the health check process by automating certain tasks, such as data cleansing and validation, and identifying patterns and trends that humans might miss. By taking advantage of machine learning algorithms, organizations can improve their master data management processes and ensure their data remains healthy and reliable.
Measuring the Health of Your Data
When conducting a master data health check, it’s essential to measure the health of your data. This can include looking at aspects such as data completeness, consistency, and accuracy. Tools like Semarchy xDM can aid in this process by providing comprehensive data quality checks, ensuring your master data is not only complete and accurate but also consistent and reliable.
Master Data Management Health Audits with Semarchy xDM
Semarchy xDM is a powerful tool for conducting master data management health audits. It provides a comprehensive suite of features for managing and improving data quality, including data profiling, data cleansing, and data enrichment. With Semarchy xDM, organizations can effectively manage their master data, ensuring it is accurate, complete, and reliable.
Conducting a Master Data Health Check: Step-by-Step
Now that we understand the importance of a master data health check and the key elements involved, let’s delve into the step-by-step process of conducting one.
Step 1: Database Configuration Check
Verify if the database is configured correctly. Check if the buffer pools or buffer caches are configured sufficiently high and if the table and index layout match the default schema.
Step 2: Schema Verification
Check if any changes have been made to the tables and indexes and whether these changes comply with the business model requirements.
Step 3: Update Table and Index Statistics
Ensure that database table and index statistics are up-to-date. This will allow the database optimizer to choose the best access plan to retrieve data for each SQL query.
Step 4: Delete Old Versions
Regularly delete old versions to prevent performance issues that can arise if too many old version rows accumulate over time.
Step 5: Ensure High Cache Hit Ratios
Ensure high cache hit ratios for optimal performance.
Step 6: Leverage AI for Data Health Checks
Use AI to automate certain tasks such as data cleansing and validation, and to identify patterns and trends that humans might miss.
Step 7: Measure Data Health
Measure the health of your data by looking at aspects such as data completeness, consistency, and accuracy.
Step 8: Conduct a Master Data Management Health Audit with Semarchy xDM
Use a tool like Semarchy xDM to conduct a comprehensive master data management health audit, ensuring your master data is accurate, complete, and reliable.
By following these steps, you can conduct a thorough master data health check and ensure your data aligns with your business model requirements.
Conclusion
Master data health checks are an integral part of maintaining and optimizing your organization’s data management practices. By regularly conducting these checks, you can ensure your data remains healthy and reliable, leading to improved business outcomes. Tools like Semarchy xDM, coupled with the power of AI, can aid organizations in conducting comprehensive data health checks, ensuring the accuracy, completeness, and reliability of their master data.