Tag Archives: Data Modeling

Abstracting and recombining all the way to the bank

In the past I’ve never understood what people really mean they say “think outside the box” but Jim Harris, in a recent OCDQ blog post, helped me figure it out.

Mr. Harris ends with this provocative line: “the bottom line is Google and Facebook have socialized data in order to capitalize data as a true corporate asset.”  The post starts with a cold war analogy and proceeds to describe how Facebook and Google have made big money as “internet advertising agencies:” offering free services with which users (like us) serve up personal data in return for use of the service, then selling advertising space based on our data (hopefully anonymized).

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Get an early start for on-time data modeling

I’m a data modeler, so I enjoyed Jonathon Geiger’s recent article entitled “Why Does Data Modeling Take So Long”.  But why does he say it like it’s a bad thing?

Mr. Geiger’s bottom line is exactly right: “Most of the time spent developing data models is consumed developing or clarifying the requirements and business rules and ensuring that the data structure can be populated by the existing data sources.”  On the projects he describes, no one took time before modeling to determine available data sources and identify business entities of interest, relationships among them, and attributes that describe them before database design started, so the data modeler had to do it.

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Thoughts after agile training: strengthening values, reducing the cost of honesty, and growing apps

I recently completed ScrumMaster training ably presented by Lyssa Adkins. Throughout the two-day class we appreciated Lyssa’s Zen-like, enabling, style. If her name is familiar, it’s because Ms. Adkins is the author of the book Coaching Agile Teams, one of the leading texts on the subject.

I’ve participated on agile projects, but so far only in a piggish/chickenish role, once in a three-week stint as a consulting architect and twice as the project manager serving as interface to the non-agile organization.

To me Ms. Adkins rocks at making students very introspective and critical of their past project experiences.  These lessons stand out:

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Metadata goals, ROI, and point solutions

Recently there has been a long, and very interesting, discussion of do-it-yourself versus third-party metadata tools on LinkedIn’s TDWI BI and DW discussion forum (membership required to follow the link). I have followed but haven’t commented, but I suppose I contributed when Information Management kindly published my article on DIY metadata.

The discussion is extremely informative, presenting the views of a variety of knowledgeable professionals in different situations, and describing successful and sometimes not-so-successful efforts to solve the essential metadata challenge: how to document what information is locked up in databases. Continue reading

Use conceptual data modeling in requirements definition

I’ve often thought that conceptual data modeling was an underused tool in the arsenal available to requirements analysts, and in a recent conversation I found that many were surprised that it would be used in the requirements phase at all. Checking the Business Analysis Body of Knowledge (BABOK) I found data modeling listed among the tools available to requirements analysts to “to describe the concepts relevant to a domain, the relationships between those concepts, and information associated with them.” There’s also Steve Hoberman’s excellent book on the topic, Data Modeling for the Business, an introduction to data modeling aimed at a business audience. Continue reading

SQL Saturday #30, Richmond Virginia, April 10, 2010

Thanks to all who attended my presentations at SQL Saturday on April 10.  Here are the materials from my two presentations:

The Business End of Data Modeling (2.5m powerpoint presentation)

Normalize Metadata For Data Integration Analysis (5.5m full version, zip including presentation and code samples)

Normalize Metadata For Data Integration Analysis (small) (2m reduced size version, graphics removed from ppt file)

Here are some quick notes for those looking to run the Metadata prototype:

The prototype metadata database includes SQL Server 2008 data definition language and data manipulation language (DDL and DML) needed to create the database and populate it with tables and columns from Microsoft’s AdventureWorksDW sample database. It also includes a sample requirements spreadsheet and source-to-target map, and SSIS jobs to load the spreadsheets to corresponding metadata tables. These define fictional requirements and mappings to populate the AdventureWorksDW FACTInternetSales table from tables in the AdventureWorks sample database.

AdventureWorks and AdventureWorksDW are available here: http://msftdbprodsamples.codeplex.com/Wikipage (accessed 4/14/2010)

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

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

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

A proposal for Enterprise Information Architecture

While many organizations understand the value of managing the information resource, for many others information management remains abstract and difficult to define.  In an effort to make it concrete here’s a hypothetical proposal to provide an Enterprise Information Architect for a hypothetical organization that really needs one. Continue reading