12. May 2008 by Rick Abbott.

So my wife goes to the Bethesda (Maryland) library to return some books.  The library is situated close to the downtown area, so parking is at a premium.  The library used to have parking meters in place, with payment required during library hours. 

For some reason they replaced this conventional system with a “new” system that requires patrons to do the following:

1. Park the car
2. Make note of the parking space number
3. Walk into the library, key the number into a machine which produces a ticket good for two hours (free of charge)

The process for handing out tickets is as follows:

1. The meter enforcer (I don’t think you’re allowed to say “meter maid” anymore) periodically goes to the machine and prints out a list of all parking spots that are reserved.
2. He/she then trudges out to the parking lot, walks around, and writes tickets for any cars that are not on the list

Anyone with half a brain can see the hole in this system - if you park your car and walk into the library after the meter enforcer has printed the list, you’re dead meat.  Plus the fact that anyone can park there free of charge for two hours is just insane.  I would have loved to be in the requirements sessions when they came up with this bright idea.

And no, my wife is not going to pay the fine.

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12. May 2008 by Rick Abbott.

I ended Part 1with the excerpt stating that CEOs and other senior management need to take an active role in defining the analytical and data warehouse strategy for their companies.  I thought this was an important statement in that I’ve never read a business book that has advocated that level of intimacy between the executive ranks and data management initiatives.  But it underscores the importance the authors give to analytics in driving innovation.

In the first post, I talked about the key elements of the “House of Innovation”, including the importance of flexible business processes that connect N=1 and R=G.  The key concept here is that it’s not enough for a company to define their business processes - they need to determine how their processes will change to satisfy the demands of N=1.  And the key driver to this change is real-time analytical capabilities.  According to the authors, it’s not enough to implement the current approach of collecting data periodically in a large repository to be analyzed at a later date.  Analytics need to be embedded within the business processes, providing insight to managers as they make decisions on what and how to produce, and how to align resources.

From a technical perspective, this is equivalent to business activity monitoring (BAM), which has been around for 8+ years (that I know of).  It involves putting sensors and other monitoring devices (both hardware and software) into machines and applications, and feeding that information flow directly into decision support systems used by line managers.  The authors also touch on the need to pull together (in a batch process) disparate data to form a 360 degree customer view.  This forms the framework for integrating and understanding the real-time information flow. 

The problem I have with all of this is that the authors make two large and broad assumptions - that data quality is not a problem, and integrating data into a repository is relatively easy.  One the one hand I understand their not wanting to get into a technical discussion around cleansing and integrating data.  But in the majority of large companies, this is the number 1 technical issue (I’m conveniently lumping these two issues together, even though they’re two separate steps).  So in assuming away these two issues, they’re removing a prime contributor to both effort and risk associated with implementing.

All in all, I enjoyed reading the book, and gained a new perspective on the importance of data management in driving business innovation. Although I have issues with the implementation aspects of their theories, I agree with the business side of their argument.  And of course it was nice to get some props from the business world for the importance of analytics in driving business innovation.

Bottom line - this is a must read book for anyone in the data management arena (e.g., BI, DW, data integration).

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