Information Quality & Master Data Management?

Master Data Management is the process used to create and maintain a “system of record” for core sets of data elements and their associated dimensions, hierarchies and properties which typically span business units and IT systems.

Master Data, often referred to as “Reference Data”, may in your organization take the form of Charter of Accounts, Product Catalogue, Stores Organization, Suppliers and Vendor Lists but to name a few.

In his article “Demystifying Master Data Management”, Tony Fischer uses Customer as an example of Master data and how, if not understood and managed appropriately, can cause all sort of headaches for a company, in this case the CEO himself!

“Years ago, a global manufacturing company lost a key distribution plant to a fire. The CEO, eager to maintain profitable relationships with customers, decided to send a letter to key distributors letting them know why their shipments were delayed—and when service would return to normal.

He wrote the letter and asked his executive team to "make it happen." So, they went to their CRM, ERP, billing and logistics systems to find a list of customers. The result? Each application returned a different list, and no single system held a true view of the customer. The CEO learned of this confusion and was understandably irate. What kind of company doesn't understand who its customers
are?”

So what are the typical barriers that hinder organizations from addressing their master data management problem? My colleagues and I typically encounter four primary barriers:

Multiple Sources and Targets: Reference data is created, stored and updated in multiple transactional and analytic systems causing inaccuracies. Synchronization challenges between disparate systems

Ability to Standardize: Most organizations cannot agree on a standardized view of master data. There are a lack of audit policies that comply with federal regulations

Organizational Ownership: Disagreement within the organization as to who takes ownership of master data management, business or IT. Assignment of accountability with cross-functional processes is difficult

Centralization of Master Data: Organizational resistance to centralizing master data since there is a sense that control will be lost. Challenges to find a technology solution that supports existing systems and the lifecycle of master data management

Organizations that are addressing such barriers typically have a successful master data management process in place that contains the following components:

Data Quality: Focus on the accuracy, correctness, completeness and relevance of dataIncorporate validation processes and checkpoints. Effort is highest in the beginning of a MDM initiative to correct quality issues.

Governance: Cross functional team formed to establish organizational standards for MDM related to ownership, change control, validation and audit policies. Focus includes establishing a standard meeting process to discuss standards, large changes and organizational issues.

Stewardship: Assignment of ongoing ownership of MDM stewardship. Typically MDM stewards are business users. Accountable for the implementation of standards established through MDM governance

Technology: Create an architectural foundation that aligns with the other three components. Implement a technology that centralizes reference data. Align processes with the technology solution to synchronize master data across source and analytic systems

As we can see, master data management is not a one-time initiative but rather a long-term program that runs continuously within the organization. To be successful organizations need to instill an iterative approach that helps develop a program that continuously monitors, evaluates, validates and creates master data in a consistent, meaningful and well communicated way.

What is your organization doing about Master Data Management? Have you had success in establishing a Data Governance program? Who own the process in your organization, IT or the business?

Top 5 mistakes in Data Warehousing

Top 5 reasons why many data warehouse managers fail to deliver successful data warehouse initiatives:

Data Quality: Quality of source system data that is to be integrated into the data warehouse is “overrated” and thus time to resolve is “underestimated”

• Bad information in means bad information out. The CPM applications that will source data from the warehouse will suffer diminishing adoption if not addressed upstream
• Data integration strategy must include methodology to address erroneous data
• Significant level of involvement from business and IT to help resolve (decision and execution of) challenges

Data Integration: Lack of robust data integration design results in incomplete and erroneous data and unacceptable load times

• What happens when you are the process of loading data and you start receiving exceptions to what is expected? Is data rejected and you are now faced with the dilemma of partial data loads? How do you avoid manual intervention?
• What checks and balances do you have in place that ensure what you are extracting from source systems is being populated into the target? Can you audit your data movement processes to ensure completeness as well as satisfy regulatory obligations?
• Your processes can handle the data volumes you are dealing with today but can they handle the data volumes of tomorrow? How easy is it to reuse existing processes when adding additional source systems/subject areas to your Warehouse?

Data Architecture: Creating a solution that is not able to scale after an initial success will result in a redesign of the architecture

• After the first success the business will quickly want to extend the usage of the solution to a greater number of users, will the performance continue to live up to expectations?
• As users mature and adoption improves so will the complexity of information usage, i.e. more advanced queries, can the design continue to perform as expected?
• Increased usage and maturity results in the demand to integrate into the solution additional data sources/subject areas. Is the architecture easily extensible?

Data Governance & Stewardship: With no controls established around data usage, its management and adherence to definitions, data silos and erroneous reporting begin to reappear

• Stakeholders must be identified and give decision rights to help improve the quality and accuracy of your common data
• Practices around the managing of standard definitions of common data and business rules applied must be established
• Understand who is responsible for the data and hold them accountable

Change Management: Not preparing an organization to utilize what is being built results in the investment in data warehouse not being fully realized and thus deemed a failure due to low user adoption

• “Build it and they will come”; providing information access does not necessarily equate to information usage.
• Helping the business understand how they can leverage these newly available data often results in changes to the way that they work. “Day in the life of” today vs. “day in the life of” tomorrow
• Education and training programs are required
• Integrated project teams (business and IT) are essential to the success of data warehouse initiatives, with individuals becoming champions within the organization for change and adoption