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Establishing 'Making it Happen' as a 'Formal & Predictable' Discipline
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Master Data Management- Making a Right Start

Here is one brief de-mystifying and context-setting field-tip for master data management, which talks about the mind-set with which we should approach MDM. MDM is 75% definition+discipline and 25% MDM platforms and automation.
 
This page of 'Principles and Rules' is linked to:  BI business intelligence end-to-end view, Data Quality, Data Warehousing,

We have not got a section on Master Data Management in ExecutionMiH.com as yet. If you refer to our Authoring Initiative and the detailed proposed content structure on BI, you will see that it is part of our plan. Here is one brief de-mystifying and context-setting field-tip for master data management, which talks about the mind-set with which we should approach MDM.

MDM is not only Business Intelligence. BI is one of the providers as well as a beneficiary of MDM

Master data management means creating an integrated and quality reference for master data within the organization for all kind of usage (operational, transactional, analytical and decisional). Business Intelligence is one of the applications for MDM. An ERP is as much a stakeholder for MDM as is data warehouse.

CIM (Customer Information Management), PIM (Product Information Management), SIM (Supplier Information Management) are examples of initiative under Master Data Management.

MDM is 75% definition+discipline and 25% MDM platforms and automation.

MDM essentially talks about generating, processing and storing the master data in a consistent and accurate manner. An organization needs to have for master data, the following elements to be defined and adhered to:

  • Data standards
  • Data controls
  • Data domains
  • Common data model
  • Business rules for the master data
  • Processes to manage the master information.

The above is as old as the concept of data and process quality. Once that is done, creating platforms for MDM like meta-data repository, integrated and central master data repository, DW Dimensional Model around master data etc..will become easier.

It is agreed that many of the MDM platforms (like a meta-data repository) enforce many of these commonsensical tasks. However an organization firstly has to establish the mind-set of sincerity in following the rules. If an application form or a new system do not follow the data standards and common data model, the MDM initiative will fall apart. A healthy mix of training, incentive, accountability, penalty and sponsorship at the highest level, will make it happen

MDM is a business (just like data quality and BI initiatives) and an enterprise level initiative

While you can have a functional level BI initiative, but MDM has to be an enterprise initiative as all the master data belongs to an enterprise level. MDM (like any other data management initiative) needs to be owned and sponsored by Business.

MDM business case is both in adding to share-holder value and in avoiding share-holder risks

MDM (like data quality) sometimes is seen as risk avoidance. A good MDM initiative can be as useful in enhancing top line as well as bottom like. For example a single Customer view can have a great upsell and cross-sell opportunity.

You do not need a separate organization for MDM

Your Data management & BI organization can also look after master data management. You do not need to create a new and separate organization. Data Steward role is the key driver for Master Data Management. ExecutionMiH.com strong recommendation is to have a single organization model to handle all BI and Data Management initiatives, because of their close interlink-age.


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Relevant Links to this page
Principles & Rules → Data Warehouse application is not limited to Analytics → Principles & Rules → Store as much detailed and granular data in data warehouse as possible → Principles & Rules → Data Normalization is not the best approach in Dimensional modeling → Principles & Rules → Keep the same names and definitions for all data elements → Principles & Rules → You cannot have a super-flexible Data warehouse → Principles & Rules → Dimensional models can be extensible and scalable → Principles & Rules → Data Marts should be ideally based upon a business process and not on a department. → Principles & Rules → Business Intelligence competency groups should be well-linked with business → Practice Techniques → Aggregation Queries on slowly changing Dimensions → Practice Techniques → Documenting your data-integration system → Principles & Rules → For a Data Warehouse/Data-Mart solution, analyze well, but be decisive → Principles & Rules → Maintain a trail of the key dimensional elements from source system to loaded → Principles & Rules → Conformed dimensions are must for cross-drilling → Practice Techniques → Checksum Approach for identifying the changed records from source systems → Principles & Rules → Data Quality is a subject of business ownership and not of IT-ownership → Principles & Rules → Don't create a hype on Data Quality Program. → Principles & Rules → Sponsor for a Data Quality Program → Practice Techniques → Business Case for Data Quality → Principles & Rules → Data Quality is not Perfect Quality → Principles & Rules → Engage the Vendors in Data Quality Program → Practice Techniques → How to get more data along with Sales leads → Practice Techniques → Ask for dates instead of number of years → Principles & Rules → How to Maximize the effectiveness of Data Stewardship → Practice Techniques → Field Tips Series#1- Data Mapping and Assessment → Principles & Rules → Data Management Standards for Data Entities will be a mix of collaboration and top-down → Principles & Rules → Data Management standards for data entities are not only for IT systems → Principles & Rules → Cascade your standards and guidelines to business partners and Vendors → Principles & Rules → Data quality assurance and control guidelines are no-brainer. Publish one immediately and evolve thereafter. → 
 
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Relevant links to this page
Data Warehouse application is not limited to Analytics
Store as much detailed and granular data in data warehouse as possible
Data Normalization is not the best approach in Dimensional modeling
Keep the same names and definitions for all data elements
You cannot have a super-flexible Data warehouse
Dimensional models can be extensible and scalable
Data Marts should be ideally based upon a business process and not on a department.
Business Intelligence competency groups should be well-linked with business
Aggregation Queries on slowly changing Dimensions
Documenting your data-integration system
For a Data Warehouse/Data-Mart solution, analyze well, but be decisive
Maintain a trail of the key dimensional elements from source system to loaded
Conformed dimensions are must for cross-drilling
Checksum Approach for identifying the changed records from source systems
Data Quality is a subject of business ownership and not of IT-ownership
Don't create a hype on Data Quality Program.
Sponsor for a Data Quality Program
Business Case for Data Quality
Data Quality is not Perfect Quality
Engage the Vendors in Data Quality Program
How to get more data along with Sales leads
Ask for dates instead of number of years
How to Maximize the effectiveness of Data Stewardship
Field Tips Series#1- Data Mapping and Assessment
Data Management Standards for Data Entities will be a mix of collaboration and top-down
Data Management standards for data entities are not only for IT systems
Cascade your standards and guidelines to business partners and Vendors
Data quality assurance and control guidelines are no-brainer. Publish one immediately and evolve thereafter.
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