What I learn today applies at work tomorrow.

Let me start with a bit of honesty: MIS531 was the hardest class I've ever taken.  It's also responsible for the first B I received since high school trigonometry.  And unlike trig, where I earned a grade equal to the amount of effort I put forth, I worked at 531 like it was a second full-time job.  The only way I could have spent more time on trying to learn the material would have been if I had quit my job or given up sleep for 7.5 weeks.

That being said, by the time I started 541, I realized that I learned more than I thought I did in that class.  I'm certainly not an expert at SQL or ER diagrams or normalization, but I have a foundation of knowledge that I couldn't see in the middle of my hair-on-fire approach to 531.  Now that we've started discussing fact tables, dimension tables, normalization and the like in this class, I'm even more confident in that foundation.

The use of the retail example to illustrate dimensional modeling works well for me.  In The Data Warehouse Toolkit, Kimball says they use that example because most people understand it; as a retail manager,  I might go so far as to say I understand it better than most.  As I read about dimensional modeling,  I can see how my company has attempted to apply the goals of data warehousing to our systems throughout the 10 years I've been with the company.  When I first started, we had very little visibility to specifics.  I could pull up sales information and see total sales and a bit about individual departments like Cafe or Children's, but I couldn't drill down beyond this summary type data.  Sometime after 2008, we rolled out a new application called Reporting Center, and now management had visibility to a entirely new level of information.  Instead of just seeing total sales for the Children's department, I could now see what percent came from Teen, Young Readers, Picture Books, and more.  I could also manipulate the report to see it at a weekly, monthly, quarterly, or annual glance.  It was a game changer.  The old method of reporting was so ineffectual, I used it so infrequently that I can't even remember the name of the application at this point.  Apparently, I'm not the only manager who found this new visibility useful, because since its launch, we've added in a large number of other critical metrics such as Membership, gift cards, shrink, inventory results, and more.  The team that designed this data warehouse clearly understood the primary goals:

1. Data warehouses must make an organization's information easily accessible.
2. Data warehouses must present the organization's information consistently.
3. Data warehouses must be adaptive and resilient to change.
4. Data warehouses must be a secure bastion that protects information assets.
5. Data warehouses must serve as the foundation for improved decision making.
6. The business community must accept the data warehouse if it is to be deemed successful.  (Kimball, 2002, pp. 3-4)

I was surprise to learn that the Balanced Scorecard has only been around since the early 1990s.  I've worked for a number of District Managers, and only the savviest used balanced scorecards prior to 2013 when we added a scorecard into the Reporting Center, but I had never considered the history of this tool.  I prefer to break big goals into smaller, achievable pieces so I have always liked using scorecards.  As Kaplan and Norton stated in Using the Balanced Scorecard, "Lofty vision and strategy statements don't translate easily into action at the local level" (Kaplan & Norton, 1996, p. 76).  Further, the balanced scorecard shows the performance of the entire district, and thanks to my competitive nature, I can't help but enjoy measuring my store's performance against everyone else's.  I have not implemented a personal scorecard in my building, but can absolutely see the benefit of doing so now.  I typically share our general performance results and in instances where we take such measurements, individual performance results, but I like the idea of enhancing the communication of corporate objectives at the team level.  This type of learning is particularly useful and exciting because I can take it and apply it immediately.

After spending last week learning about Big Data, I got curious about how my company uses some of the data it collects on a daily basis.  While I didn't get a complete answer to my question yet, I did stumble on an interesting marketing piece from IBM about how Barnes & Noble uses IBM Netezza data warehouse platform to provide sales and inventory information directly to publishers.  See http://www.ibmbigdatahub.com/sites/default/files/document/IMC14749USEN.PDF to view the entire document.

Last week's material also made reference to Harvard Business Review asserting that data science is the "sexiest" job of the 21st century.  I wanted to read the entire article, so I found the issue in the UofA online library system.  HBR does a monthly Spotlight package, and it turns out that "Data Scientist: The Sexiest Job of the 21st Century" was part of the October 2012 Spotlight.  Andrew McAfee and Erik Brynjolfsson also contributed to the package with "Big Data: The Management Revolution," which I found particularly interesting given what I ultimately hope to do after graduation.  McAfee and Brynjolfsson discuss volume, velocity, and variety or data, and ultimately assert that "data-driven decisions are better decisions -- it's as simple as that.  Using big data enables managers to decide on the basis of evidence rather than intuition.  For that reason it has the potential to revolutionize management" (McAfee & Brynjolfsson, 2012, p. 63).  This caught my attention because I can look at my peers and tell who still operates on gut and who actually puts the data to work to drive their business.  It will be a difficult change for those who believe they know the business so well they don't need data, but every time they make a decision based on intuition instead of facts, their risk of making the wrong call far outweighs mine because I'm leaning on the data for support.

Ultimately, I am intrigued by the material we have covered so far and excited to learn more about Big Data and how I can use it in my current position as well as in the position I hope to achieve next.



References

Barton, D., & Court, D.  Making advanced analytics work for you.  Harvard Business Review.  Oct 2012, 10(90), pp. 79-83.

Davenport, T.H., & Patil, D.J.  Data scientist: The sexiest job of the 21st century.  Harvard Business Review.  Oct 2012, 10(90), pp. 70-76.

IBM.  (May 2012).  Barnes & Noble: Helping suppliers track sales and inventory in real time.  Retrieved from http://www.ibmbigdatahub.com/sites/default/files/document/IMC14749USEN.PDF

Ignatius, A.  Big data for skeptics.  Harvard Business Review.  Oct 2012, 10(90), p. 12.

Kimball, R., & Ross, M.  (2002).  The data warehouse toolkit: The complete guide to dimensional modeling.  New York: John Wiley & Sons, Inc.

McAfee, A., & Brynjolfsson, E.  Big data: The management revolution.  Harvard Business Review.  Oct 2012, 10(90), pp. 59-68.