BI: It Will Change the Way You Think.

Most of the people in my life know I'm working on my Master's degree and periodically ask about my classes.  Sometimes the response is a nod or word of encouragement and sometimes it's more like "Oh, that sounds terrible!"  The latter usually occurs when whomever I'm talking with doesn't understand the course name, so they assume it must be hard or uninteresting.  Business Intelligence elicited a much different response over the past seven weeks.  Many expressed curiosity about BI and asked questions because they actually wanted to know more about what I was learning.  As each week passed, my answers became better and better, as I gained a more in-depth understanding of BI myself.

At the beginning of the class, I knew that companies used BI to learn more about customer behaviors and market trends, but didn't know much about how that was accomplished.  Our textbook, The Data Warehouse Toolkit, made me fearful that this would be another class in which I had to do battle with SQL.  Thankfully it wasn't, and I can say with confidence that I learned a lot of really useful skills and that my perspective on Big Data has matured.  I hadn't spent much time thinking about how quickly we generate data or how much space that data uses.  The first lecture on this topic captured my attention right from the start, and I began what became an ongoing conversation about data with several friends and co-workers.  At first I presented stats about data that occurs every 60 seconds:

• 100,000 new tweets
• 400,000 Skype calls
• 700,000 Google queries
• 2,000,000 emails sent
• 1500 blog posts
• 100 domains registered
• 80,000 wall posts on Facebook
• 1000s of YouTube videos posted (Ram, 2014, p. 4)

The following week, Dr. Ram tweeted a link to http://pennystocks.la/internet-in-real-time/ which shows the Internet in real time.

Now I had a visual aid, and kept it available on my phone to show anyone who asked questions about my current class.  I would pull it out and say that I'm learning what we can learn from all of this activity that occurs every minute online.  Several people asked when the timer started on that link.  When I would reply that it started the moment I opened the page, the "are you serious?!" face that followed was priceless.

As we progressed on to dashboards and balanced scorecards, I was excited to learn more about tools I use in my job every week.  The KPI selection process was new to me, but I could immediately see where my company could do better in this area.  I'm still planning to take an opportunity to try to present a revised District scorecard at work.  I believe our current model could be improved upon based on what I learned in Lecture 4: "The balanced scorecard should tell the story of the strategy" (Ram, 2014, p. *).  We're in the ballpark now, but there is too much unnecessary information presented and it's all presented in the same format.  It's at once distracting and disengaging.  Now that I know what a balanced scorecard is capable of, I could potentially help my boss revise ours to focus on strategy and true KPIs.

The web and social media analytics portion of the class were my favorite.  I enjoyed manipulating Google Analytics to learn more about the Eller MIS Online website visitors, and analyzing the results to make recommendations.  Using LinkedIn InMaps showed me valuable information about my own professional network, and showed me the importance of understanding the data to which I have access.  This played out on a far larger scale when I used Netvizz and Gephi to analyze my Facebook friends for the final assignment.  I'll admit, I had so much fun tinkering with my visualization Gephi that I actually lost track of time.  Each time I applied one of the various tools for stats or partitions or layouts, I found a new way to analyze my own social network.  It was so interesting, I actually posted my finished product on Facebook, and discussing and explaining what I learned in class this week has been downright entertaining.

When I told my favorite undergrad professor I was returning to school to pursue a Master's degree, she congratulated me and said "It will change the way you think."  Dr. Deirdre Pettipiece holds a PhD in English Literature, and is currently the Dean of Arts and Humanities at CUNY and has never given me bad advice.  It hasn't occurred in every class, but each time I learn something that changes the way I think, I smile because Dr. Pettipiece was right again.  This class was exceptional in terms of changing the way I think, and has provided me with new insight about how I hope to direct my career once I complete the program in December.  Much has been written about how to convince a boss or CEO to implement Business Intelligence; my goal is to be the type of leader who doesn't need convincing.  BI has captured my interest and attention, and I can clearly see how that will help me set myself apart from other managers moving forward.

References

Elliot, T. (2014).  Business intelligence cartoons: 6 of the best.  Retrieved from http://www.matillion.com/insight/business-intelligence-cartoons-6-of-the-best/

Ram, S.  (2013).  Introduction to big data and business intelligence [PDF document].  Available from https://blackboard.eller.arizona.edu/bbcswebdav/pid-521118-dt-content-rid-4449378_1/courses/MIS_587-910-142-MISO/Lecture_Notes/Lecture2.pdf

Ram, S.  (2013).  Introduction to balanced scorecard [PDF document].  Available from https://blackboard.eller.arizona.edu/bbcswebdav/pid-521123-dt-content-rid-4449381_1/courses/MIS_587-910-142-MISO/Lecture_Notes/Lecture4.pdf

4.74 Is the New 6

I've owned a LinkedIn account for several years now, but I didn't flesh out the details until just last year.  As of the beginning of this week, I had 39 connections.  When I learned Tuesday that InMaps requires a minimum of 50 connections to function, the race was on.  I poured through the list of suggested connections, my Gmail contacts, my Facebook friends, and even scrolled through every name listed in my phonebook.  In order to get the 11 additional connections needed to use InMaps, I figured I needed to send at least 15 invitations (and ideally more like 20).  With this in mind, suddenly connection selection became a game.  I sent invitations to people I would have spent days (or even weeks) agonizing over whether I should make contact, like the Corporate Investigator I worked with this time last year.  Instead of worrying if he would remember me, I just hit the send button.  By the time I went to bed Tuesday night, I already had three notifications that people had accepted my request to connect.  When I woke up Wednesday, I had three more notifications.  At this point, I was halfway to my goal of 11 new connections, and the next couple hours felt like I watching a fundraising leader board as additional notifications pinged my phone.  It was exciting, and by 9am Wednesday morning, I had 51 new connections.  Mission accomplished.

Imagine my surprise when I logged into LinkedIn to set up InMaps, and discovered a new "Who's viewed your profile" page.  This tool shows profile views, the top location of viewers, viewer titles, and how many viewers found my profile using LinkedIn search.

While this tool is far less sophisticated than MicroStrategy or Google Analytics, it appears to be an attempt to share the same type of information with LinkedIn users.  If a person was actively using LinkedIn to generate leads, I can imagine this being quite valuable to track network progress.

After securing my 51st connection, I was excited to see what else I could learn about my network from InMaps.  I expected to see three distinct sub-graphs: Barnes & Noble, University of Arizona, and Arizona State University, alongside a small assortment of outliers.  However, when I loaded my map, the picture was a bit different.

I still had three distinct sub-networks, but they contained different information than I predicted.  I ended up labeling my three main network areas as current Barnes & Noble employees, former Barnes & Noble employees, and University of Arizona.  There is definitely some overlap between the two type of B&N connections, and one node with a significantly higher degree of centrality.  My most influential connectors are Danae, Julie, and Jason.  If I look outside the graph to consider why, it's easy to discern a pattern.  I direct reported to Danae and then Julie at B&N, for five of the last six years.  Danae works for a different company now, but she is the primary connector among current and former B&N employees.  And while Jason was never my boss, he has been a peer since 2011 and the person I would most likely identify as my mentor.  It makes sense that my connection with him influences my connections with other active employees in the company.

The absence of any real pattern relating to ASU surprised me, until I realized that my ASU connections are scattered.  In other words, I know each of them, but they apparently don't know each other so when represented graphically, they make up a large segment of the outliers group.  

As I waited for connection notifications yesterday, it occurred to me that all this talk about network science reminded me of the Six Degrees of Kevin Bacon game.  I had always considered Six Degrees as something of a pop culture phenomenon, but I learned that it is actually based in network science from the 1960s.  Psychologist Stanley Milgram conducted an experiment that resulted in the Six Degrees of Separation theory, "according to which everyone and everything is six or fewer steps away, by way of introduction, from any other person in the world" (Jean, 2013, p. 1).  While network science wasn't a term used at that time, it appears this experiment served as an early project in that field.

Interestingly, I came across a New York Times article from 2011, which reported the results of recent Facebook research and posits that "the average number of acquaintances separating any two people in the world was not six but 4.74" (Markoff & Sengupta, 2011, p. 1).  If 6 sounded like a small number, 4.74 is shockingly small.  I shared this information with several friends and coworkers yesterday, mostly for the purpose of gauging their reactions. Their response was similar to mine: after years of accepting 6 as the magic number, the thought of 4.74 "hops" between any two Facebook-using humans sounds impossibly small and for those less comfortable with technology, even a bit scary.

Once I got over my initial shock about how small the world really is, this knowledge actually made me more comfortable about my Tuesday night LinkedIn connection campaign.  My concern about the Corporate Investigator not remembering that I had been part of a case resolution in 2013?  Irrelevant.  My Regional Loss Prevention Director was already connected to the Investigator, and I was already connected to the RLPD so it was a short hop, even if he had forgotten about that case from last year.  For that matter, now that I know that a mere 4.74 degrees separates me from anyone else I may want to connect with, I will be less distressed about forging new connections in the future.  If I don't know someone but want to, chances are it's a short distance to travel to get there considering my growing network.

References

Jean.  (2013, September 2).  Six degrees of Kevin Bacon: a webinar on graph visualization (and movies).  Retrieved from http://linkurio.us/6-degrees-of-kevin-bacon-a-webinar-on-graph-visualization-and-movies/

Markoff, J., & Sengupta, S.  (2011, November 21).  Separating you and me?  4.74 degrees.  New York Times.  Retrieved from http://www.nytimes.com/2011/11/22/technology/between-you-and-me-4-74-degrees.html?_r=0 

Ram, S.  (2014).  Network properties [PDF].  Available from http://courses.eller.arizona.edu/mis/587/ram/Lecture13/

Fortune 500, Small Business, and University: Well, they're all fruit.

According to EConsultancy, as of 2012, 51% of Fortune 500 companies use Google Analytics.  Mine is one of them.  In fact, in 2013, Gill Solutions published a list of the Top Ten Companies that Use Google Analytics, and Barnes & Noble is #6.  Access to data from bn.com would have made for incredibly interesting material to use for Assignment III, but Fortune 500 companies don't grant access to that type of data for just anyone.  Although I'm not just anyone, I'm also not part of the team that tracks and analyzes that information.

My next big idea for Assignment III involved a local real estate agent's website.  The site is hosted by Zillow, and he uses Google Analytics to track activity.  The owner says it's useful to see how visitor activity increases when he posts a new listing versus when he updates a new one, and it also helps him decide where to spend his time and energy in terms of location.  I had a productive introductory chat with the owner: I learned a little about how the real estate business works and he was excited about the opportunity to receive a little free assistance with website, which is admittedly not his strength.  Unfortunately, my initial research revealed that due to an error that occurred during Zillow's payment structure transition, the owner's website was currently displaying nothing more than an ad for Zillow.  The owner quickly resolved the problem with Zillow, but because he and I agreed that the data would be inaccurate in terms of site traffic, we agreed that now was not the right time to attempt an analysis.

I ended up using the Eller MIS Online website for the purpose of my assignment, and actually learned a lot about what to look for when analyzing data as well as what I might want to do with that information if I were in a position to implement changes.  The Google Analytics modules provided by Google were very helpful, more so than the modules provided by MicroStrategy for last week's assignment.  However, knowing what we all know about Google the company, it comes as no surprise that Google produces high quality, engaging training for its product.

To begin my analysis, I made a short list of questions I hoped to find the answers to, and figured my opinions about who uses the MIS Online site would either be confirmed or overturned.  As an in-state, female student in the program, what I learned about geographics and demographics did not surprise me.  I can look back on most of my classes and say with confidence that a majority of my peers reside in Arizona and that male students easily outnumber female students.  This information wasn't shocking, or even particularly interesting, until I considered whether Eller is satisfied with these ratios.  Suddenly, this data because very useful, because decision-makers at Eller can use the status quo as a catalyst for change if a goal about diversifying the student body exists.

Earlier this week, Dr. Ram asked via Twitter about the difference between Google Analytics Premium and the standard offering.  Freemium offerings often attract a base of customers, but it depends on the company whether paid-for services are worth the investment.  LinkedIn is a great example --  popular opinion has yet to settle on whether this social media's site premium service is worth the expense.  Knowing this, I was naturally curious about what set Google Analytics Premium apart, and had done a bit of research at the beginning of the week.  I assumed that the Fortune 500 companies using Google Analytics were paying for service simply because their site traffic would be significantly larger than that found on a small business owner's website.  I found a great side-by-side comparison chart on Tomer Tishgarten's blog, All That I Know About Marketing Technology, that is both easy to read and in the world of technology, incredibly current.  He also included a great image, which truly highlights the fact that Google Analytics Premium and Standard are cut from the same cloth, but definitely not the same animal.

The chart showcases key differences relative to account management services, data limits, and data processing time.  An awareness of these key differences makes it quite obvious why my company would pay for service, while the local real estate agent I spoke with would not.  In other words, both may be fruit, but a business's decision about whether to select the apple or the orange is heavily dependent on how they intend to use Google Analytics as well as how much traffic the site generates.

Now that I know enough to be dangerous, I reached out to the real estate agent again and asked him to get in touch near the end of the year, once he has enough reliable site traffic logged.  I would still like to help him improve his site based on existing traffic, and I learned a lot this week about what questions to ask to make the most of whatever we learn about visitors at homeselleraz.com.  I am also hopeful that as I continue to steer my career at Barnes & Noble around my newly acquired skills in this program, I will create an opportunity for myself to work with the team responsible for analyzing data from bn.com using Google Analytics.


References

Farina, C.  (2012, July 9).  51% of Fortune 500 companies use Google Analytics.  Retrieved from http://www.e-nor.com/blog/web-analytics/51-of-fortune-500-companies-use-google-analytics

Gill, G.  (2013, April 3).  Top ten companies that use Google Analytics.  Retrieved from http://www.gillsolutions.com/top-ten-companies-that-use-google-analytics/

Tishgarten, T.  (2013, October 31).  Comparing Google Analytics standard vs premium.  Retrieved from http://www.allthatiknow.com/2013/10/comparing-google-analytics-standard-vs-premium/#.U6dZ4Y1dXi5

Dashboard Design and the Big 16

Each week I look forward to receiving a dashboard for my district at work.  I look forward to seeing how my store performs in comparison with the other ten stores, and reviewing with my team where we are succeeding and where we have opportunities.  But there is a lot of information on our dashboard, far more than what I consider the Big 4: sales, labor, Membership, and gift cards.  All of the information is presented in the same format, and some of the metrics are insignificant in terms of what actually matters for my annual performance review.  Kids Club conversion and email capture rates are interesting, but they have no bearing on my bonus or my raise, and while they contribute to success in the most critical areas, they don't drive my business like the Big 4 items do.  You can ask me at any point in the year how my team is performing in any of the most important areas, and I can tell you down one-hundreth of a percent.  Ask me how we're doing in any of the other twelve categories showcased on our dashboard, and I might be able to tell you if we're above or below goal.  I can focus on, and I can keep my team focused on, those four key areas every single day I'm at work, but sixteen balls in the air is more than most of us can juggle successfully.

My company has used various forms of dashboards over the years, and until this class, I hadn't really considered dashboards outside of the Barnes & Noble context.  However, this week's material opened my eyes to what a dashboard should aim to accomplish.  Peter McFadden, CEO of Excel Dashboard Widgets, defines a quality dashboard as "An easy to read, often single page [or single screen], real-time user interface, showing a graphical representation of the current status (snapshot) and historical trends of an organizations key performance indicators to enable instantaneous and informed decisions to be made at a glance."  Stephen Few, Principal of Perceptual Edge, similarly defines quality dashboards as "A visual display of the most important information needed to achieve one or more of the objectives consolidated and arranged on a single screen so that information can be monitored at a glance."

These definitions validated my opinion that we have too much clutter on our district dashboard.  Those extra twelve metrics might be interesting and worth sharing periodically, but all of the statistics associated with those areas can be accessed through our in-house Reporting Center, so they really just muddy the waters on our dashboard.  At the end of the day, our KPIs are exactly what I refer to as the Big Four, and that truly is the information that must be monitored at a glance.  Sales, labor, Memberships, and gift cards are so significant that effective Store Managers monitor the store's performance on an hourly basis.  The weekly snapshot helps me to determine if I need to put more energy into a particular area when compared with a neighboring store so it's productive and helps me decide where and how to spend my energy during a given week.  But the excessive attention to items that truly are not key performance indicators just dilutes the message.

Working with MicroStrategy this week was a useful assignment.  I actually checked out MicroStrategy's home page, and subscribed to the cloud platform that is available for free for one year.  After spending a bit of time exploring, I was surprised to receive a relatively personalized email from a CS Rep named Ed inquiring about my use of MicroStrategy.  I have signed up for a lot of "freemium" online services, but that is the first time I have been contacted by the provider.  While the assignment offered valuable learning points, I might have preferred an opportunity to rework my company's dashboard instead.  I could still do that on my own time at some point in the future, but using real data that matters and makes sense to me would take the assignment to a much more engaging level.  I realize that not all students use or even have access to a corporate dashboard with real data, so that presents a number of challenges in terms of actual execution.  For me it would be an incredibly valuable exercise, both academically and professionally, and the most exciting and useful parts of the MIS program occur when I discover a way to merge school and work for an assignment.  It's a form of practical application that leads to an understanding of the material that far surpasses case study exercises.

If I were to rework my district's dashboard, I would do so in light of Few's Common Pitfalls in Dashboard Design.  In addition to identifying mistakes that should be avoided in dashboard design, Few also asserts  what a dashboard should allow a user to do:

1. See the big picture
2. Focus in on the specific items of information that need attention
3. Quickly drill into additional information that is needed to take action

My weekly dashboard currently provides a big picture.  But sometimes we lose the forest for the trees, because there is just so much information on that single sheet of paper.  I know this to be true for several of my peers, who instead of looking at their sub-par Membership numbers and using that data to inform an action plan, will point to a non-critical metric such as email capture rate and point out that they perform above the company average.  Eliminating the noise from our dashboard would help Store Managers identify appropriate wins to celebrate and encourage them to take action where it really matters.  Point two directly ties into the first point because the excessive detail means that some users miss the specific items that need attention.  With regard to the final point, we can drill into additional information needed to take action from a separate program, not the dashboard itself.  Hopefully IT will find a way to link the two together in the future because it saves time, but having the ability to see the details at all is a vast improvement over where we were even seven years ago.

It appears that we are moving on to web and social media analytics now, so I may not find time to revisit my district dashboard before the end of this class.  However, my free subscription to MicroStrategy is good for one year, and I would like to see what I can do to improve the presentation of our KPIs.  I started calling the metrics that matter the Big 4 because my Regional VP has her own set of KPIs that she refers to as the Big 6.  I suspect that my district's dashboard is an attempt to implement something similar to what our VP uses, but we simply got carried away and created a visual display where nothing is important because everything is important.  Creating a streamlined version that is also visually appealing could be the perfect project to showcase my schoolwork on a professional level.

References

Few, S. (2005).  Dashboard design: Beyond meters, gauges, and traffic lights.  Business Intelligence Journal, 10(1), 18-24.

ProClarity Corporation.  (2006).  Common pitfalls in dashboard design.  Boise: Stephen Few.

Ram, S.  (2014).  Dashboard design and its use for analysis [PDF document].  Retrieved from https://blackboard.eller.arizona.edu/bbcswebdav/pid-521135-dt-content-rid-4449385_1/courses/MIS_587-910-142-MISO/Lecture_Notes/Lecture8.pdf

1 of 1500+ Blog Posts in the last 60 Seconds

The most interesting thing I encountered this week is the link Dr. Ram posted on Twitter: The Internet in Real Time, which essentially works like a ticker for Facebook likes, Tweets, Google searches, YouTube videos, and so on.  In fact, you can ask anyone close to me about it, and they will tell you I made them watch the numbers grow at some point in the last few days.  I had shared some of the facts from the first lecture about how quickly data is generated with friends and family, but the visual is far more expressive than stats alone.  Most everyone was quietly awed by the amount of traffic in 30 seconds or less, but I had the most fun showing it to a friend who is notorious for being so tethered to her phone that she often has whole conversations without making eye contact because she's too busy Pinning or Facebook-ing to look up.  Which is exactly what she was doing when I showed her, and after clarifying that all that activity had occurred since I opened the link, she Liked one more item on Facebook to contribute to the count, then put her phone in her purse and had an actual conversation.

This type of Big Data activity is exactly what kept me off Facebook longer than many of my peers.  I didn't like the idea of my non-business activities being tracked, so I refused to participate.  As recently as 2008, my husband showed me a photo someone had posted of me on Facebook and I reacted by picking up the phone and calling my friend and demanding he take it down.  He laughs while telling that story now, joking that it wasn't even a bad photo and teasing me about how I used to be afraid of the Internet.  In 2009, I caved and opened a Facebook account to share pictures I took while on vacation in Idaho with my Facebook-ing friends... while I was still in Idaho.  It was so much fun to share my adventures while they were happening, that I have been a regular user ever since.

The other material we covered this week went a long way to help me bridge my mental gap between looking at diagrams and imagining the user-end of a database.  I will be the first to admit that spatial skills are not my strong suit (talk to any of my booksellers who have ever helped me move tables in my store -- I can't see how something is going to look until I actually put it there!).  This visualization struggle is part of why I found MIS531 so very challenging.  I could either think in terms of the ER diagrams or the tables, but synthesizing the two caused me to feel like I was trying to mix oil and water.  The part of that class that made the most sense to me was normalization, and I spent hours on that part of my group's project (along with the data dictionary, which is updated with a near religious fervor as we worked toward our deadline).  Normalization felt comfortable, like solving for x in Algebra or balancing equations in Chemistry or even transposing music into a different key.  I blazed through that assignment and that portion of the project, feeling triumphant that after weeks of struggling, something finally clicked for me.

Since reading chapter 1 in the textbook last week, I've tried keep in mind the distinction the authors make about dimensional modeling and 3NF modeling:

"3NF modeling is a design technique that seeks to remove data redundancies.  Data is divided into many discrete entities, each of which becomes a table in the relational database....  Both 3NF and dimensional models can be represented in ERDs because both consist of joined relational tables; the key difference between 3NF and dimensional models is the degree of normalization."

(Kimball & Ross, 2002, p. 11)

This bit of information not only helped clarify the difference between what I'm learning now and what I learned last fall, but it also kept me from breaking out in a cold sweat when I read the directions for this week's assignment.  The four-step design process is straightforward, and really helped me to focus on the business process at hand instead of worrying about connecting the dots between diagrams and tables.  The end result is that I feel as though I learned something; my star schema may not be perfect, but I feel like I understood the purpose of the assignment and at the very least, like I'm running in the right direction.

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.

A little about me.

I graduated from Arizona State in 2005 with a BA in English Literature. I began my undergrad in the College of Business, but after taking a class on lit theory at the end of my second year, I discovered I had a talent for writing and an interest in gender and race theories, so I changed plans.  I usually credit (or blame) Charlotte Perkins Gilman's short story The Yellow Wallpaper for altering my course so radically.  However, by the time I was ready to start a Lit grad program, I realized that the English department was not where I wanted to spend my career.  So I took a job serving coffee at Barnes & Noble to pay rent while I figured out what I wanted to do next.  That barista job turned out to be the start of my career, because currently I'm a Store Manager for Barnes & Noble in $5.2M store.  B&N was a great choice -- I've had the opportunity to refine my leadership skills and develop my business acumen, but I still get to talk about books on a daily basis. 

Between school and work, I don't end up with a lot of free time at the moment, but when it comes along, my favorite place to spend it is in my kitchen. I like to experiment with new recipes (then manipulate them to my taste), and I enjoy working to recreate dishes I've had at restaurants.  My truck-driving husband and 14 year-old boy are usually more than happy to participate in my test kitchen experiments, even if that means sometimes we eat the same thing several days in row while I try to work out the details of a recipe.  The other part of this blog is actually dedicated to showcasing recipes I've developed, although I haven't updated anything there in quite some time. 

So how did I end up in MIS?  I discovered program while researching the MBA program at Eller. The more I learned about MIS, the more it seemed like a better fit for me than a good old-fashioned MBA. I'm hoping to use my degree to alter my career path, and transition from front-of-house management to corporate leadership role.  I've spent a fair amount of time working with my Regional Director of Loss Prevention at B&N, which has exposed me to data analytics and information security in a retail environment.  I was first introduced to the details of LP during a three-year assignment at a store in an urban neighborhood, where I dealt with LP issues related to theft and quality of life on a daily basis.  I also learned that I as much as I enjoyed catching the bad guys, I also loved assembling the puzzle of various bits of information to build a compelling case.  During my time in that store, my company began to organize LP data at the store level to address issues such as organized retail crime, and I was in the right place to help pilot a number of programs and then teach others how to use them effectively.  I plan to leverage my on-the-job experience with my new degree to make this the purpose of my career rather than an aspect of my job.

This class in particular has been on my radar since I started the program last May.  The amount of data my company collects on a daily basis both in stores and on-line is astounding, and I've asked many questions over the years about accessing specific bits of information.  I'm excited to learn about how other organizations are putting big data to work, and hopeful that I can take an idea or two back to work with me immediately.  Whether the opportunity occurs with B&N or elsewhere, I'm ultimately hoping to become one of the 1.5M data-savvy managers described inTen IT-enabled business trends for the decade ahead from McKinsey.