Master Data Strategy – The Foundation of Master Data Management
by Tom Oldham
Master Data Strategy
As a business intelligence practitioner, Master Data Management is a topic that I am passionate about. It is the rule book for how data is organized within the business system and is critical for business intelligence. In my previous blog post, I gave an overview of Master Data Management. In this blog, I am going to go deeper into the critical first step of developing a Master Data Strategy.
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.
Define goals and objectives
The executive team should develop a charter that includes goals and objective for the Master Data Strategy team. Goals and objects define what a good strategy encompasses. With Master Data Management that is usually defining goals around timely, accurate, efficient data input and extraction for running the business and improving operations. Management should also provide the team with their vision of the future from a business standpoint. This may include items such as growth rates, new business segments, additional product lines, new manufacturing processes, etc. A good strategy works for today and can adapt to future needs in the 5-year time horizon.
Assemble the team
Developing a master data strategy requires a team of experts who understand the ERP system, the key business metrics, the product structure and how the data flows. These are individuals who have deep knowledge of a functional area or business process. They should understand the details and have the ability to see the big picture and how things can be improved. I would caution that some individuals may see this process as a threat to their job. They may be the only employee who provides critical KPI’s each month. These individuals will need extra coaching and reassurance if they are to be productive team members.
Some Questions to ask when developing the strategy
• What is your Business Analytics strategy or vision?
• What is the overall business analytics strategy or vision concerning your businesses, companies, offices, plants, warehouses, customers, suppliers, products, etc.?
• What are the key measures for your businesses (KPI’s)? How do you view your companies financially?
• How do you view the business from a sales perspective?
• How do you view the business from a plant, warehouse, or distribution perspective?
• How do you want to view the business in the future?
• Can the strategy flex and account for mergers and acquisitions?
The hard work
The multi-functional team needs to do the detailed work of mapping out the most important master records and related “smart” coding to align with each in the ERP system. Then review how well the current data structure aligns with how the company wants to analyze the business. This may seem excessive and tedious, but the devil is in the detail. This step helps identify gaps such as having important data offline, outside the ERP in other data sources. It can also uncover if there is no data matching the vision. If critical data is missing or offline, the strategy should challenge the team to identify ways to create it or code it and load it. It will be worth it.
Best Practice “Smart Coding”
The strategy should include the best practice of “smart coding.” Smart coding puts “intelligence” into your coding system. Identify your vision for how you want to analyze each function and a “smart” coding system should be mapped and loaded. The best example of smart coding is a chart of accounts where each character and value mean something which then enables the automated financial reporting required that fits the “vision” of how to measure the business.
Benefits of a robust strategy
With a Master Data Management Strategy, companies find they are better able to focus business efforts on activities that increase profitability and decrease inefficient analytics because of improved data accuracy, visibility, and transparency or in other words “Business Intelligence” for all.
In my next blog about Master Data Management, I will discuss best practices for data structures.
Author: Tom Oldham, CMA, CFM, is a Product Manager at Cyberscience focused on Manufacturing Solutions.