Tag Archives: Business Analysis

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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Building a writing culture in application development

One of the key skills needed in today’s IT shop is communication, and one of the best ways to improve ability to communicate is to write blog posts and articles.

In spite of “IT guy” stereotypes, communication and analytical thinking about business are among the most important skills in application development. Developers, analysts, and managers require ability to interact effectively with business people, to conceptualize solutions that match business needs, critically evaluate those solutions, and effectively make the case for one of them. Of course this is true of the overall project business case, but more importantly it applies to the daily “IT guy” to business person conversations that happen throughout analysis, design, development, and testing. Continue reading

Agile development: rugby analogy considered harmful

Recently my friend Mark Hudson posted about the inappropriateness of the term “sprint” for an agile project phase, preferring the cycling term “interval.” That post really struck a chord with me.

As a rugby union fan and former wing/fullback I’ve always thought the whole rugby analogy was wrong. Agile development is continuous and fluid, yet the agile originators chose the word “scrum” for its daily standup meetings.  In rugby union a scrum is a set play resulting from a minor penalty, like offside in American football or a foot fault in tennis.  If you like the rugby analogy the right term would have been “ruck,” which is kind of like a scrum but part of the continuous run of play (in the other kind of rugby, called rugby league, the scrum has devolved into an almost meaningless stylized ritual – which I guess happens on some agile projects). 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)

Business requirements up front

“Our goals can only be reached through a vehicle of a plan, in which we must fervently believe, and upon which we must vigorously act. There is no other route to success.” – Pablo Picasso

It is an old story: about 30% of IT application projects succeed, 45% are “challenged,” and the other quarter fail altogether.   That’s the consistent result over the years of the Standish Group Study of Project Outcomes.  Jorge Dominguez, here, displays a chart of the remarkably similar results since 1994.  Not a pretty picture, right?  Some question the validity of the Standish studies, but Scott Ambler parallels the Standish story in a recent Dr Dobbs column called “Lies, Great Lies, and Software Development Project Plans,” which itemizes the strategies commonly used by IT project managers to “stay out of trouble” when schedule/budget results don’t match initial estimates.  For example, “18% change the original schedule to reflect the actual results”. Continue reading

Study data early to improve application alignment

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

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 pretty good requirements analysis checklist

Recently I was asked for a high level requirements plan for a large IT conversion.  I googled around a little for something standard.  I found some good references (see links at the bottom of this post), but not exactly what I was looking for: a simple, method-agnostic layout of the high level steps and checkpoints in requirements for a big project, emphasizing interactions with business people.   I then rifled my files to find the example below. Continue reading