Master data refers to the information that is essential to running your business. It is the core data used to build transactions and unique data combinations, such as bills of materials and quoted price lists, and orders and invoices. It’s critical that master data be as complete, accurate and consistent as possible across all users and systems to help reduce inefficiencies and reporting errors, and minimize added costs.
 
Not surprisingly, Master Data Management (MDM) is the foundation of high-performing, data-driven organizations. So how can you manage your critical repository of data? One of the best ways is to establish an MDM strategy. If your business does not have one in place, you’re not alone. A recent global study found that 94% of U.S. organizations face data governance challenges. Consider the examples below that stem from master data issues: 

Scenario 1: A valuable customer has contacted your customer service department complaining that a recent order they received does not meet agreed-upon specifications — maybe the dimensions are off, or the part is made of the wrong material, or perhaps there were not enough items in the case they ordered.
 
Scenario 2: Your organization receives word that upon customs inspection of a recent order of raw materials, the Harmonized Tariff Code filed at the port of entry did not accurately reflect the true material composition of the order, and now your organization is facing fines for the mistake.
 
Scenario 3: During the production scheduling process, a scheduler notices that there are not enough pieces of a particular sub-component available in inventory to complete the next production run. It seems that those responsible for demand planning did not have minimum inventory coverage levels for the item set up in the planning system. Now the Sourcing Department must spend more to rush order the item, and production will fall behind schedule. 

What Causes MDM Issues

All of the above scenarios can be traced back to less-than-ideal MDM. The source of such problems vary, but two reasons these challenges arise are rooted in how the organization manages its systems architecture and how they govern their data.
 
Systems Architecture
Many organizations struggle with master data due to their underlying systems architecture. If systems are disjointed and don’t have interface capabilities to “talk to each other,” master data is not easily transferred between systems. At best, master data may be manually keyed into each system by different areas of the organization, or, at worse, data is not shared at all between systems. This type of technology landscape leads to a greater risk of inaccuracies, inconsistencies, missing or duplicated records.
 
Master Data Governance
Another root cause of master data issues is lack of an MDM governance model. MDM governance involves: 

1. An organizational structure to define, manage and execute policies and procedures that ensure data quality across the enterprise,
2. Maintenance processes to standardize how data is created, maintained and archived,
3. A data model defining what the data consists of,
4. Security over who can approve changes, create, modify and delete data, and
5. A monitoring function over data quality management. 

A data governance model ensures a plan is in place the can support the strategic direction, design, implementation and ongoing support for MDM across the organization. Without it, initiatives to address data issues and/or a disjointed technology landscape are short lived and fall short of achieving their desired objectives. 

Quick Ways to Enable MDM Capabilities

For businesses that are just beginning to enhance their MDM capabilities, there are some relatively quick and low-cost ways to add value. Most MDM issues arise not from technology challenges but from a lack of a clear process and human error. Consider addressing issues by: 

• Optimizing the process for creating and updating master data. As processes are defined, establish stakeholder roles and responsibilities around the processes related to master data.
• Creating a data dictionary for key master data attributes. By identifying the most critical data areas, developing clear and consistent definitions of what that data represents and how it is used, and making this data dictionary available to business users, you can often avoid simple errors and misunderstandings in data entry. Involving business stakeholders in creating and maintaining the data dictionary further enhances MDM ownership within the business. 

To achieve leading MDM practices, organizations must address process, people and technology. Best practices involve highly proactive data quality management processes, with documented and enforced policies and procedures around data creation and maintenance, supported by a defined data model and measurable key performance indicators (KPIs). It requires people taking ownership for defining and maintaining data at the right level of the organization and a cross-functional data governance team to continually monitor and address MDM processes and quality. Finally, successful MDM requires supporting technology to report on consistency in the use of master data across systems, and a systems architecture that allows for easier and reliable technology interactivity to share and update data.

Cohen & Company is not rendering legal, accounting or other professional advice. Any action taken based on information in this blog should be taken only after a detailed review of the specific facts and circumstances.