The final step for implementing best practices for Master Data Management (MDM) is establishing an ongoing process and set of controls to ensure the maintenance and adherence to the data structures and mapping is successful. A robust Data Governance Model for Master Data Management has 3 key elements – people, processes, and technology.
In part 2, Tom continues the discussion of Data Structures, and diving into Marketing, Customer and Sales best practices. There is a lot of content to cover and he promises if teams follow these best practices your business intelligence process will run much smoother.
In this blog, Tom dives into the details about setting up Data Structures and highlighting best practices. Data Structures are the way data is organized and defined within the ERP. Master Data Structures are the secret sauce for effective and efficient business intelligence.
Having strong Master Data Management starts with having a strategy for Master Data. This step identifies how the company wants to analyze the business as a whole and by each function. It includes all departments, levels of the business and stakeholders. The strategy also accommodates the future needs of the business.
Companies struggle day in day out with cleaning data before they can analyze it or send it out as “actual”. There are many analyst hours spent on cleaning up or manipulating or structuring the raw data repeatedly. This is a symptom of weak master data management. Master data management is the rule book for how data is organized within the business system. For example, if you want the individual customer sites to roll up to a parent company, then you need an identifier defined for “parent company” when defining the customer profiles along with bill to and ship to addresses.
