Demystifying Master Data Management
Many years ago, a company launched a new product and wanted showcase a trailer to its customers. The excited CEO decided to send a letter to all its key customers to know about the upcoming product. He wrote the letter and asked its executive team to "Make it happen". So they went to CRM, ERP, billing systems to find a list of customers. To their surprise each application returned a different list of customers and no single system had a true view of their entire customer base. The CEO learned about this and was understandably irate. What kind of company does not know about its customers?
Unfortunately many companies face the same issue and do not have precise information about their products, suppliers, inventories and customers. Every time organizations add new legacy applications to different business unit's data integration issues between different legacy systems increases. As a result, the concept of creating a centralized data management framework is of growing importance. A recently conducted survey confirmed that more than 80% of organizations nowadays try for a centralized master data management framework.
In a survey conducted in 2013, 21% of organisation rated their data quality as "high" or better, up from 15% in 2008; most rated it "fair". Also, the number of organisations with no data quality technology at all has fallen from a third to 15%. This suggests that some progress at least is being made in the uphill battle with data quality in large organisations. The proportion recognising that poor data quality is costing them at least $1 million annually has also doubled in the five-year period. However, even today fully one third of the survey respondents are not measuring the cost of poor master data. Some 14% of respondents believed that bad data was costing them more than $10 million annually, up from 10% in 2008.
These numbers clearly speak about a need for a centralized and robust Master Data Management framework.
So what is Master Data Management?
Master Data Management is an organization-wide approach for providing access to Master Data in an integrated unified view, timely available for all users and applications providing a "trusted source of data". MDM furthermore maintains a consistent "record of records" for all master data sets throughout the organization, and includes the necessary policies & procedures to maintain the integrity and sensitivity of master data.
Clients are nowadays pressing the need of having a unified MDM structure. Listed below are some of the benefits that can be achieved by having a robust MDM structure
- Universal truth : One single reference for Master Data
- Enhanced operational efficiencies
a. Reduced data redundancy
b. Predictable & more accurate data flow
c. Unified framework for acquiring, processing and managing data across the organization
d. Reduced costs in data maintenance
- Increased Compliance
a. Well defined roles & responsibilities
b. Well defined processes maps and frameworks
c. Documented standards, ready to be applied & enforced
Master Data Management (MDM) is a framework of three key tenants i.e. people, process and technology components which work in collaboration to ensure that master data is coordinated across the organization.
"Data" as we know is the main backbone of any business. Master Data Management also treats "Data" as the primary asset for any organization and defines six key areas for it
When the organization institutionalizes the objective of "treating data as their primary asset" success will be evident in the following ways:
- Data Quality
a. The quality and value of the data can be measured and demonstrated through key performance metrics
b. Data defects are easily detected and proactively corrected
c. Redundancy is removed and data is correctly normalized
d. Improved data quality attibutes to increased business profitability e,g, improved data usage, iproved business opporunities etc.
- Data Standards
a. Data standards are based on relevant data categorization frameworks for e.g. UNSPSC, SIC etc.
b. All stakeholders talk about the same structure thus making adoption easy
c. Data standards are incorporated into applications using the data
d. Products and service catalogues adoption can be increased using a standard framework of data
- Data Processes
a. Efficient processes and frameworks in place for data maintenance.
b. Common master data processes are used across the organization
c. Defined roles and responsibilites for all stakeholders
d. Well doucmented processes and guidelines that can be communicated to the end user group across all business units
- Data Integrity
a. Data security policies and procedures are defined and aligned with business needs and outcomes
b. Regular security audits are conducted to maintain data integrity
c. Defined roles and responsibilities between creators and maintenance of master data and transactional data
- Data Engineering
a. Innovative tools are used to manage the large volume of data across the organization
b. Data flows across the enterprise application architecture is documented, understood and available for use
c. A robust data architecture enables increased flexibility, speed and agility of the enterprise
- Data Governance
a. Clear governance structure following a top to bottom approach
b. Define a centralized group for end to end management of master data
c. Well defined training plan for end users at all levels and all format of data (item master, vendor master etc.)
Master data management is not a new problem, however, with the introduction of compliance regulations like Sarbanes-Oxley in US and Basel II in Europe and organization's increasing interests in having a performance driven data framework, have given it a new kick start. Unstructured data has to be tagged so that organizations can make sense of it and they start connecting the dots between the unrelated pieces of unstructured and structured data. While companies will be spending billions to renovate enterprise applications through service oriented architecture, they also need to understand that critical master data management capabilities and processes are required to produce data and information of high quality and consistency to make all enterprise applications work together. Without these capabilities and processes, the investments will not enable the best business decisions and deliver maximum benefits and business value.
Related Reading: White paper on Realizing the Business Value of Master Data Management