Do you struggle keeping 2 or more sources of data up to date with name changes, address changes, email updates, phone number changes or more? The reality is that most of us do. If you have 3, 4 or more data sources to integrate, then you know the challenge increases geometrically. If this is your customer world, Master Data Management can help.

Master Data Management

Master Data Management (MDM) is not just a technology. It is a strategy, it involves people, it has standard processes and it is also a technology. MDM is based on the premise that a company must have a single master copy of customer data or a single version of the truth.

“MDM is not just about technology and MDM program managers should use a business driven framework to ensure that all the component parts of MDM are addressed. MDM programs need to align with the business vision and strike the right balance between technology and governance and organizational issues.” ~ Saul Judah, Research Director at Gartner

For our purposes, we talking about master customer data. There is also an enterprise approach that includes other data like finance and human resources. Departments that will be interested in this approach are sales, marketing, customer service and others. Companies should consider a customer MDM approach when you are large enough and have several significant customer data bases to integrate and analyze.

With customer MDM, you want to create a master data file that keeps track of differences in customer data from multiple systems. As you integrate two or more customer systems, you may have addresses that don’t match for the same customer. In one case, you may have a maiden name and in the other system the current name. In one system, you may have a home address and phone number while in the other you have a work address and phone number. In one system you may have a first name of Pat and in the other Patricia. All of these differences make it extremely difficult to create and maintain a single set of master data and can throw your customer analytics out of whack.

It is common to think that Master Data Management (MDM) and Data Warehousing (DW) are the same and can accomplish the same results. They are in fact remarkably different and business (non technical) staff should appreciate, at a high level, the distinctions. Business goals for creating amazing customer experiences, are at stake. Customers do not tolerate bad and duplicate data. It is a common source of complaints at call centers. Great customer journeys can be derailed because of simple customer data problems. Executives who are obsessed with customer experiences will appreciate that attention to customer data detail is critical. If staff see duplicates in a customer system like CRM, they will not tend to trust analytics in a business intelligence solution.

1. Different Goals – The primary goal of a DW is analytical in nature. It looks at historical transactional data. (Read more here …) The primary goal for MDM is to establish a single version of the truth for a customer from one or more customer systems. MDM requires solving the root cause of the inconsistent data, because master data needs to be propagated back to the source system in some way. In data warehousing, solving the root cause is not always needed, as it may be enough just to have a consistent view at the data warehousing level rather than having to ensure consistency at the data source level.
2. Different types of data – MDM looks primarily at customer data but not transactional data. A DW generally looks at customer (a little) and transactional data (a lot).
3. Different reporting needs – In MDM, the focus of reporting is more about oversight of the data, duplicates and data quality. In DW reporting is all about analyzing the data and providing insight.
4. Where data is used – In a data warehouse, usually the only usage of this “single source of truth” is for applications that access the data warehouse directly, or applications that access systems that source their data straight from the data warehouse. Most of the time, the original data sources are not affected. In master data management, on the other hand, we often need to have a strategy to get a copy of the master data back to the source system. This poses challenges that do not exist in a data warehousing environment. For example, how do we sync the data back with the original source? Once a day? Once an hour? How do we handle cases where the data was modified as it went through the cleansing process? And how much modification do we need make do to the source system so it can use the master data? These questions represent some of the challenges MDM faces. Unfortunately, there is no easy answer to those questions, as the solution depends on a variety of factors specific to the organization, such as how many source systems there are, how easy / costly it is to modify the source system, and even how internal politics play out.

Master customer data that is trusted is of huge value to your customer initiatives. The reverse of that will cripple many customer systems. If the insights gained from your analytics isn’t trusted because of duplicates and poor quality customer data, it will be hard to gain traction. Fixing that, after a CRM implementation, for example, is almost impossible. Employees will not use a system they do not trust. Management will not require a system to be used if it is perceived to have useless data. Establishing a single version of the truth, early on, is foundational to a customer focus.

It is critical that clear definitions of customer data are established. Who is going to do that? A Steering Team (with a focus on strategy) should be appointed with business leaders heavily involved. At a minimum, staffing should include a data steward. If you have enough customer data, a dedicated team should be appointed.

There are some natural constraints that we face in establishing master customer data management as an important initiative. Here are 8 requirements for an effective master customer data strategy.

1. Secure an executive sponsor early.
2. Create a business case for why master customer data is important.
3. Pursue an iterative approach and avoid “big bang” implementations.
4. Plan for organizational and culture change.
5. Take a multi-dimensional strategy.
6. Understand the importance of data governance and appoint a steering team early.
7. Establish clear metrics for success.
8. Rely on the “right” technologies.

Master data management should be both a local and corporate responsibility. Many times duplicate data has to be resolved by staff who are closest to the customer. That may be at the local level. Clear timeframes for resolving conflicts should be established and enforced to make sure duplicates do not linger too long.

Here are the key ideas:

1. Start by defining your goals and strategies for managing master customer data.
2. Begin by appointing an oversight team.
3. Encourage a culture that values quality customer data.
4. Understand that nothing frustrates customers more than not getting the basics of customer data right.
5. Establish quality customer data as a norm.
6. Great insight flows from quality data.
7. Recruit an executive sponsor “who gets it” early.
8. Make quality data everyone’s business.
9. Recognize the barriers and “deal” with them early.