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Jaime Fitzgerald



Semantic Web Technologies and Open Data Innovation

 With my colleagues Kevin Cabral and Justin Goldbach, I attended and greatly enjoyed the latest event of the  Lotico New York Semantic Web Meetup which was about open data innovation, especially with a focus on and obviously, the role of Semantic Web Technologies in making open data more accessible, more useful, and more valuable to citizens and businesses.

The speakers were incredible:

  1. Gale A. Brewer, New York City Council Member, who is clearly passionate and experienced in the benefits of applying technology and data innovation to benefit citizens, improve public services, and enable non-profits to help more people
  2.  Jim Hendler, Tetherless World Senior Constellation Professor, Rensselaer Polytechnic Institute (RPI) -- a pioneer in the field and a fascinating speaker.
  3. Deborah L. McGuinness, Tetherless World Senior Constellation Professor RPI -- gave a fascinating talk.  My favorite line:  "the government would like nothing better than to see private companies use public data to make money, create jobs...create value with this."
  4. Peter Fox, Professor and Tetherless World Research Constellation Chair RPI. Wow.  A great presentation also, about "The eScience revolution: Semantic Web platform for massive scientific collaboration"
The implications of Semantic Web Technology, together with Open Data on the Web, including government data, creates incredible promise for innovation, transparency, and value creation.  The wheels are spinning.

Clear Line of Sight into the Value of HR Executives

 Awareness of the value of HR Executives is growing. Clear "line of sight" between Human Capital contributions and the bottom line has resulted in more measurable and better understood definitions of HR impact. A recent WSJ article points out that:

"At least 65 current and former human-resources managers serve as outside directors on 101 boards...Ten years ago, the number would have been no more than a half dozen..."

An interesting trend, for sure -- HR pros have increased their role in corporate boards by an order of magnitude.

Based on our experience working with executives in the Human Capital function, my team and I believe that the respect HR Pros are receiving is driven by two key factors:

  • Measurement: it's become easier (and more common) to measure the contribution of HR Pros to bottom line results. So the value that's always been there is now being measured more consistently.
  • Results-Orientation of Human Capital Pros: the HR function has evolved, increasingly, towards a "Human Capital" paradigm, which is inherently results oriented, ROI-driven, and does a better job of "connecting the dots" between HR functions and business outcomes. As a result, the best Human Capital professions in their field have become high impact, high value, and highly coveted C-level executives
It's great see see the huge intangible value added by Human Capital professionals becoming more visible!

Posted By:
Jaime FitzgeraldAlex Roberts
The Impact of Money in Sports: Correlation or Causality?

 According to the Wall Street Journal's sports metrics column, The Count, the NHL shows the strongest link between team payroll and winning percentage of all professional sports. It's a provocative and counter-intuitive conclusion, as many sports fans assume that baseball is most impacted by financial disparities between big-city teams and other, smaller market competitors.

The column compares the correlation between payroll and winning percentage in the 4 major sports:

  • NHL shows the highest correlation between payrool and winning: .49
  • MLB places a close second at .43,
  • The NBA and NFL lag behind at .24 and .15, respectively.
I'm intrigued, but also skeptical, and most of all curious to see more comprehensive analysis of the dynamics at play here. Glad to see the info, but several "open questions" remain:

  • Causality: since correlation does not prove causality, it may be premature to conclude (as the column does) that in the NHL "More Dollars Equal More Wins." What if causality runs in the other direction, with winning teams earning more revenue, which they in-turn spend retaining their best players?
  • What's The Mechanism?: how does extra money generate additional wins? If the NHL is really the league where money buys success most consistently, how is that investment achieved? One could think of this as the "Return on Investment" on professional sports payrolls....
  • Role of Sabermetrics? -- In most businesses, ROI is higher when investments are better screened and selected. Facts and analysis are used to make better decisions which achieve superior returns. This is where the NHL and MLB are especially different: player-valuation metrics are advanced and heavily used in Baseball, while they are less well developed in the NHL. I'm curious to investigate further how the "metrics environment" affects ability of team owners to "buy wins" in one sport versus another....
  • Effects of League Financial Structure?: the financial dynamics, salary cap rules, and revenue-sharing arrangements vary significantly amongst the professional sports leagues. What impact might those differences have on the "causal impact" of money in each league?

Posted By:
Jaime FitzgeraldAlex Roberts

Themes from the 2009 MDM Summit in NYC

  For the third year in a row, I attended the MDM Summit in NYC with teammates from Fitzgerald Analytics and with clients whose interests dovetail with the subject matter at hand. As usual, the event was thought-provoking and at times, even inspiring.

For those of us who are passionate about the challenges, but also the opportunities in the emergent field of data management, the event reminds us that "the wind is at our back." A mixture of technology trends and business imperatives means Data Management will continue to grow as a field, and the quality of solutions will continue to mature.

This year I was struck by several "highlight themes":

  • Emphasis on doing "first things first," which means for example:

    ...doing enough data profiling before data integration...


    ...establishing viable data governance before rolling out major solutions or change programs...

    This realism was positive, and a sign that more "real cases" are leading to more consensus around "preconditions to success" in the MDM realm.

  • Linking strong Data Management with revenue upside. 

    John Fleming, Head of Strategy & Governance for Client Data at MERRILL LYNCH, was especially impressive in articulating the connection between better integrated and managed data, and better customer retention, more innovative revenue capture, etc.

Posted By:
Jaime Fitzgerald 

Data Management Conference Predictions

  Together with teammates and clients, I'm about to head uptown for the long-awaited MDM New York Conference. Having attended annually since 2007, I've found the event always thought-provoking regarding this critical topic which is becoming a fast-growing but still emergent discipline/function.

Some of the "highlight topics" we expect to be especially resonant:

  • MDM: NOUN OR VERB -- explores the key question "is Master Data Management something you buy or is it a long term commitment?"
  • MDM in the Health Care Industry -- obviously a hugely important topic with regard to digital medical records
  • Data Governance -- and "oldie but goodie" topic, a perennial, because it's such a "key precondition" to success in data management, so essential to getting ROI on MDM investments

More to come...

Posted By:
Jaime Fitzgerald

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Friday, December 10, 2010

Each NBA Shot is a Decision: What's the Decision Model?

These days, the sports world is full of people paying close attention to statistics....
  1. General Managers use unconventional statistics to find hidden value in players....
  2. Fans discuss stats when debating what a player is worth, how much playing time he should get and who he should be playing against.
  3. Journalists use statistics to write stories - at best they'll use numbers to create a new narrative; at worst they'll use numbers to conveniently support an intuitive--but false--"story hook"
David Biderman, who writes "The Count," a Sports and Numbers blog for the WSJ, seems to have fallen into the latter category with his recent piece on what the Miam's Heat's passing means for their scoring chances.  His convenient but suspicious "story hook" is that the more the Heat pass, the lower their shooting percentage gets.  Here at Team Fitzgerald, we are deeply skeptical.
Biderman's contention is that because the Heat's shooting % is higher when they pass less, they should start acting more selfishly, pass less, and voila...they can boost their shooting percentage.  If it were that simple....

But wait!...correlation does not equal causation.  Do players get higher percentage shots when they pass less?  Or do they pass less when they have a high percentage shot, for example on a fast break?  

Let's take an important step back:  each pass or shot is a decision.  Professional players make it thousands of times, and coaches monitor how well they make that decision.  We praise players who take higher percentage shots when they have them, and blame them when they give up a good shot with a pass they didn't need to make.  On the other hand, if a player does not have a high percentage thought, we praise the decision to pass...nobody admires a "forced shot" with low probability of hitting the net.

If a player has a sweet fast break, or an open path to basket, the rational choice is to shoot (or dunk) bc they already have their high percentage shot.  If the defense is able to get back, and the player can't easily take it himself, the rational choice is to slow it down and look for a set play that may involve more than 1.  These are the decisions a player makes based on the set of facts in front of them in the moment.  By not eliminating fast break plays, controlling for shot location, etc...Biderman is left with a data sample skewed enough to result in incorrect conclusions.  Separating them would be a better way to see the impact of passing on a given basketball play. 

So mathematically, let's run a quick scenario.  During each game hundreds of "pass vs. shoot" decisions will be made.  Rational team players shoot when the odds are in their favor (high percentage shot) and pass when they don't have a good shooting opportunity, seeking a better one.  Meanwhile, the shot clock ticks...and as time passes, the calculus changes.  Given the risk of running out of time on the shot clock, extra passes become less attractive and shots on the basket become more imperative, and players rationally are more willing to take lower-percentage, lower-quality shots.  Shooting percentages can be expected to fall, because players are forced to shoot without having found an easy layup or fast-break dunk. 

The article was based on data from only two games by one team.  We wish we could find a similar but  potentially more valuable data-set:  shooting percentages by teammate, segmented by the number of seconds on the shot clock.  This would allow us to test our hypotheses, which is that game patterns, and the related passage of time, cause patterns in shooting percentage, and that passing less is correlated with, but does not cause, higher shooting percentages.
We feel that Biderman's piece misses is what's at the heart of why statistics in sports are so captivating to so many of us. They allow us to reveal truths that were previously hidden.  Biderman's piece doesn't make an attempt to dig deeper into why the Heat score less when they pass more.  There is no hidden truth in this article, only a potential straw man. 

What do you think?

Alex Roberts & Jaime Fitzgerald 


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