Mastering Master Data Management: Avoid These Two Missteps
Master Data Management (MDM) is a critical business process that involves managing the organization's most important data assets. However, companies often need help implementing MDM, which can lead to significant missteps affecting the entire organization. Let's discuss two common missteps and how to avoid them.
Not Having an MDM Plan
The first misstep to avoid is needing a solid plan in place. A bad plan is even worse than having no plan at all. A well-thought-out MDM plan must consider all major and minor aspects of your business's master data management. Here are some key questions you should ask:
Where is the data?
How reliable is the data?
Who needs access to the data?
How secure is the data?
How will people access the data?
By answering these questions, you'll understand how your MDM strategy needs to work.
Take Ownership of the Data Cleanup Process for Effective MDM
Various applications and spreadsheet data are brought together in a typical MDM strategy. For example, your marketing team may use a CRM like HubSpot, your accounting team could be using QuickBooks, and your consultants use their spreadsheets uploaded to Google Drive.
However, when it's time to implement an MDM strategy, the data turns out to be a mess with multiple records for the same customer that contain slightly varying data. It's up to you to own the cleanup process, as it's not the responsibility of the IT department to clean up the data.
It's common to assume another person will clean up the data, but that never happens. As a result, the data needs to be more reliable and efficient, defeating the MDM strategy's very purpose. Don't make this misstep; take responsibility for cleaning up the data for effective MDM.
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Sayed is a seasoned technology executive with a distinguished career in design and development of Business Intelligence & Analytics. Over the last 20 + years, he has a proven track record of effective leadership and execution, having implemented end-to-end BI solutions, including data engineering, data visualizations, infrastructure design, and execution for various multinational companies to provide actionable insights, increased resilience, and higher profits.