Master Data Management at DIG

Last week I mentioned that Dan Power from Hub Solution Design will be speaking at DIG 2008 on the topic of master data management. Dan has over 20 years of experience in enterprise technology and is a frequent contributor on the topic of MDM in industry magazines such as DM Review. Dan recently added a post to his blog on speaking at DIG and the importance of master data management in the context of data governance, business intelligence and performance management platforms. I cannot agree more. Every reporting, dashboard and planning application is not only dependent on getting quality data like sales, but are also equally dependent on having common “hierarchies” of the business. Hierarchies may be a standard chart of accounts, products or organizational structure. Without a common way to consolidate these hierarchies, those sales numbers may not be right! Master data management and data governance practices start to address these common issues. We are looking forward to hear Dan’s perspective on master data management and its linkages to business intelligence and analytics.

The Price You Pay When Your Data is Questioned

I read this article in yesterday’s Wall Street Journal that I found interesting and relevant to DIG. There is always a debate on the value of having “one version of the truth” and the necessity of accuracy in corporate data. To date, that hasn’t been the case with certain types of performance measurement, especially website visits. Well, comScore is paying the price through shareholder value and their stock price. The issue stems from the accuracy of “clickstream” data that comScore, like their competitor Nielson, collect and track the popularity of websites on the web. Google announced that advertising clicks grew by 20%, while comScore reported only a 1.8% growth. Well, who is right?

This data is critical for marketers when deciding where to spend their ad dollars. You should read the full article to gain a full appreciation of the entire story, but here are a few snippets that are relevant to the importance of having “one version of the truth”.

Sarah Fay, chief executive of both Carat and Isobar US, ad companies owned by Aegis group said “We have not expected the numbers to be 100%”. It’s good to see that no expectations were being set out of the gates. Not sure this would fly when discussing something like revenue for an organization.

The article goes on to point out that comScore and Nielson data doesn’t always match up. “To complicate matters, disparities between comScore and Nielson data are common, as the two companies use different methodologies to measure their audience panels.” This isn’t something we don’t here inside the four walls of a corporation for something like a measures calculation rule.

Brad Bortner, an analyst with Forrester Research points out “There is no truth on the Internet, but you have two companies vying to say they are the truth of the Internet, and they disagree.”

And finally, my favorite quote in the article came from Sean Muzzy, senior partner and media director at digital ad agency http://www.ogilvy.com/neo/. “We are not going to look at comScore to determine the effectiveness of Google. We are going to look at our own campaign-performance measures”. This would be the equivalent of “if you don’t like the results, try a different measure.”

I have always wavered on the need for accurate data for certain types of measurement, especially something like clickstream analysis. I guess that wavering has now fallen to the side of the camp with the other types of data that require precision and accuracy.

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?