Data Management » Bob Lambert

On DW federation, whac-a-mole, and integrating business data

Jan 022010

Information Management recently sent around their pick of best IM blog articles of 2009. Among them was Forrester’s James Kobelius’s reaction to Bill Inmon’s “incineration of a straw man concept that he refers to as ‘virtual data warehousing (DW).’”

According to Mr. Inmon, virtual data warehousing reminds him of the carnival game called whac-a-mole. He says “just when you think this incredibly inane idea has died and just when someone has delivered what should have been a deathly blow, out it pops again from another hole.” Continue reading »

Cloud databases and business/IT alignment

Data Management, IT No Responses »

Aug 232009

Today, the foundation of most of our custom-built systems is a relational dbms. While development frameworks vary, they overwhelmingly access and maintain data in relational tables and columns. As I write I routinely save this post in a MySQL database, and at work I tend SQL Server applications. Millions of others develop, use, and extract analytical data from thousands of SQL Server, DB2, and Oracle applications, on servers and networks maintained in-house by in-house administrators. Continue reading »

BI Business Case Basics: Three Things to Remember

Data Management, IT, Project Management No Responses »

Jul 282009

Here are three things to remember when putting together a BI business case:

Excerpt from "Show Me the Money: A DM/BI Business Value Primer", Bob Lambert and Tri Truong, Information Management Special Reports, March 24, 2009

Intangible benefits don’t count.
BI has no inherent value.
Senior managers often make decisions about future outcomes with insufficient data. Continue reading »

Don’t forget to get it done

Data Management, IT, Project Management No Responses »

Jun 112009

In a recent article at Information Management, Maria Villar and Theresa Kushner offer 4 Steps to Create an Effective IT and Business Partnership, a very useful list of ways to ensure “strong partnership between IT and business”. To the authors this partnership “is the most important, and often overlooked, component to successfully managing critical business data. Undertaking business intelligence, data quality or an enterprise data management [program] without full cooperation and collaboration between IT and the business is a formula for frustration.” The authors suggest these four steps: “know your partner, develop a relationship, define roles and responsibilities, and establish open, regular communication channels.” I recommend reading this article because IT folks (like me) seem tempted to neglect the habits that enable building a solid relationship with business people. Continue reading »

Coming soon: data like money

Data Management, IT No Responses »

May 232009

It is a commonplace to say we should manage data like a resource. But when you think about it, data is an asset but not a resource. Data isn’t a thing like real estate, employees, or customers, but rather it represents all of those things. In data-geek-speak, data is a meta-resource that holds information about resources. That makes data a lot like money. Continue reading »

Study data early to improve application alignment

Data Management, IT, Project Management No Responses »

May 112009

A recurring theme in the literature on IT over the years has been frequent failure of IT projects. Most studies lay the bulk of the blame on requirements (examples here and here). One way to improve accuracy and fit-to-purpose of requirements, and thereby promote project success, is to include data analysis as well as process analysis in the requirements plan.

I’ve cited here the need to start data interface analysis early to avoid budget and schedule blow-ups when, as a result of not thinking early about interface complexity, data integration work turns out to be bigger and nastier than anticipated. Continue reading »

DQ, he isn’t so dumb he just needs glasses

Data Management, IT, Project Management 1 Response »

May 032009

In a recent very thoughtful post on data quality, Paul Erb plays out an analogy comparing data users with Don Quixote and data quality professionals with Sancho Panza, then reverses the analogy to cleverly coin the “Sancho Panza” test of data quality professionals. He encourages data quality professionals promoting the critical role of data quality to apply a what would Sancho say test to ensure that they are aligned with the needs and interests of data consumers. Continue reading »

No business value in nulls

Analysis, Data Management, IT 2 Responses »

Apr 052009

It seems I’m frequently in conversations about using null to represent a business value. To paraphrase, say there are credit and cash customers, and there’s a suggestion to set “Customer_Type” to “C” for credit and null for cash. To data and database professionals this is obviously a bad idea, but it’s not obvious from a business point of view. Continue reading »

Someone’s integrating your data

Data Management, IT No Responses »

Mar 122009

Here’s a little-recognized fact about data integration: if you run a business or any sizable chunk of one, someone is integrating your data.

In my professional life I have on occasion suggested data integration efforts. Sometimes my suggestions have been accepted and sometimes not. As an IT professional I understand that different managers have different priorities, and in a given business situation sometimes other things are more important for example than having a single, consistent source for all customer records, or making sure production data matches financial data.

But as a customer? That’s different. Continue reading »

Beware the devils in the details of data integration

Data Management, IT, Project Management No Responses »

Mar 012009

Excerpt from "Illusions, Allusions – Let’s Get Real about Database Design", October 4, 2002

Much of today’s IT application development – custom or off-the-shelf – involves integrating data from legacy systems, third- party software products and external data sources such as demographics or mail lists. More often than not, data integration is unexpectedly complex, either due to data quality issues or the nature of the data integration itself. Continue reading »

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