Gaining a Clear Picture of Master Data Management (MDM)

In many respects, the interest in Master Data Management (MDM) is a result of the lessons that organizations have learned during their struggles to implement effective Customer Relationship Management (CRM) and Enterprise Resource Management (ERP) applications. These enterprise applications were intended to provide a 360-degree view of a given customer – providing a single, comprehensive snapshot of data from across the enterprise. What many organizations discovered, however, was that the view they came up with was fuzzy or distorted, because the underlying data used to generate it was poor in quality.

The same customer may be listed under different names in different systems, organizations may have multiple accounts, addresses may be incorrect, or data may be incomplete. Any of these examples of bad data can have a significant impact on the bottom line, whether directly by increasing costs and reducing results, or indirectly through decreased customer satisfaction and loyalty.

What we’re seeing now is that businesses have recognized the importance of high-quality data as the basis for any business intelligence, CRM or ERP programs that they wish to implement. The educational process is over. Marketing, IT and other executives no longer need to be convinced that poor data quality is a critical problem. Instead, they need to know how to improve it and keep it clean.

MDM has emerged has a process to enable organizations to align master data assets across multiple systems and departments, and maintain them on an ongoing basis. It enables organizations to ensure that accurate and consistent information is shared across the enterprise, and that decision-makers are able to rely on this information. It is part of the answer to improving the effectiveness of CRM and ERP programs.

Q2. So why is achieving accurate MDM so difficult?

One of the primary challenges in managing master data is that it resides in many different systems and within many different departments throughout an enterprise. Compiling accurate MDM often means reconciling disparate systems with multiple master data sets. The data may be housed in legacy data systems and managed through a multitude of different processes, with new systems and processes being implemented all the time. Trying to bring these multiple systems and processes together can be a challenge.

Additionally, accessing the data and bringing it together is only the first step in achieving accurate MDM. Once it is brought together, organizations still need to address issues related to inconsistent data structure, accuracy and completeness. Poor quality data brought into a single, master data system is still poor quality data.

The other challenge is centered on people. Data stewards are either non-existent or, typically, there isn’t agreement across divisions around a consistent definition of master data. This problem stems from a lack of corporate sponsorship and/or a true data governance plan where stakeholders are identified and held accountable.

Q3. So what typically has been the approach of organizations to solving this problem and what should they be doing?

Typically, organizations have looked to MDM and Customer Data Integration (CDI) vendors and system integrators to provide a comprehensive, all-in-one solution for MDM. What they find, however, is that MDM is much more than a single vendor or single technology solution.

MDM is everything that is needed to create and maintain an accurate, timely and complete view of the customer across multiple channels, business lines and enterprises where there are multiple sources of customer data in multiple application systems and databases. This includes not only technology, but also people, processes, and most importantly corporate commitment.

Since there are multiple components involved in a total solution, organizations should look for best-of-breed components and architecture in each area. Any technology that is chosen should be sufficiently flexible and scalable to fit into existing technology and infrastructures. And, most importantly, companies need to adopt an enterprise-wide data governance plan that is endorsed at the highest levels of the organization.

Q4. What is the role of data governance and data quality in successful MDM projects?

Data governance is a term used to describe an enterprise-wide data management initiative to manage how organizations govern input to and access of master data. This includes operational efficiency and security procedures as they relate to data.

Adopting a well-defined data governance plan across the organization is critical to achieving accurate MDM. The outcome of such a plan is a corporate-wide agreement on data standards for master reference data. This is data that describes common business entities like products, customers, and suppliers, and location and reference data is the profile data about an entity that is used to uniquely identify that entity. For example, customer master data may include such attributes as first name, last name, gender, address, phone number, DOB, etc. The primary objective of the data governance plan is to oversee the data collection processes, reduce data redundancy, and increase data accessibility and availability.

Data quality is a necessary component to achieving MDM goals. A solid data quality solution should provide a solid foundation and understanding of the data quality issues. For example, some of the important functionality that good data quality technology can support includes:

• Global Name Parsing, Standardization and Validation
• Global Address Parsing, Standardization and Validation.
• Unique Entity Identification and Consolidation based on probabilistic matching
• Global Data Augmentation with Point-Level Geocoding, Reverse Geocoding and Demographic Data Append.

An effective data quality solution should allow access to heterogeneous data environments to support the creation of the master data in both batch and real-time with continuous logging, monitoring and auditing capabilities to ensure that data remains clean.

Q5. What is the importance of privacy and compliance issues with CDI and MDM for today’s enterprise?

With increased governmental reporting and disclosure requirements, the need to centralize and maintain high data-quality standards is more important than ever. In fact, 40 percent of respondents to a 2006 data quality survey conducted by The Data Warehousing Institute indicated that data quality had directly contributed to compliance problems.

With regard to protecting an individual's personal information from fraud, companies need to ensure that the ability to view and update customer data is limited within the organization. This is where a comprehensive data governance plan, which spells out the processes and procedures for entering, changing and maintaining records, is developed and embraced.

Beyond this, the key to complying with most new government regulations related to customer data comes back to data quality. If your organization has a complete, accurate and comprehensive view of your customers across your enterprise, you are positioned to meet most compliance issues that you may confront. For example, these could include new location-based tax requirements or lists of individuals, organization or countries that you are restricted from doing business with.

For executives in charge of regulatory compliance, poor data can result in the company facing public embarrassment, damage to brand equity, significant fines and even lawsuits. The day is coming when data quality is a line item on auditing checklists. The dissociation of auditing-by-report and underlying data quality must be addressed through measurement and assurance of data quality management. This means moving away from embedded code base in legacy systems to executive sponsored enterprise-wide data governance plans supported by automated workflow solutions that give companies the ability to create, monitor and improve data quality business rules centrally.

As companies calculate the ROI of data quality, the cost of reactive compliance should be high on the list. A forward-thinking organization should include data quality as a part of its everyday operations.

Q6. How is MDM a key enabler for the agile global enterprise?

Organizations today face increased competition driven by globalization and aggressive mergers and acquisitions activities across many industries. To stay competitive, this constantly changing business landscape requires that they not only deliver exceptional products and services, but also provide an exceptional customer experience. Customer loyalty is key for the agile global enterprise, and effective customer communication is a key to customer loyalty and retention.

Enabling effective customer communication means managing customer interactions at all existing touch points and having the ability to identify your most valuable customers. By managing master data, such as customer names and locations, as a single source of truth about those customers in a central hub, companies can stay agile. They can make the real-time decisions, one-to-one messaging, and customized offers required in today’s fast-paced, global economy.

Q7. How, and how quickly, are organizations going to see a return on that investment? Is it really worth it?

Companies will be best served by focusing on specific areas of opportunity and immediate value when deploying an MDM strategy. This means identifying value in specific data domains across the organization, such as customers, vendors or suppliers, rather than across all master data domains. This makes for a manageable, phased roll out with immediate, measurable impact to the business.

Of course, any MDM strategy will only work if companies ensure that there is buy-in from all departments, and that data stewards are aligned with increased emphasis on maintaining and measuring quality metrics associated with the “trusted” data. If this isn’t the case, you face the same fate as organizations that failed to consider data quality as a key driver for adoption of CRM investments in the past.