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Zephyr on "Cloud Watch"

Zephyr doing his part for the family business...

Posted via email from brenda's posterous

[Note: This was a test of the Posterous Service for use with my to be launched (redacted) project.]

Lessons from the Crisis: Behavior Matters

The July/August issue of the Harvard Business Review has a feature by McKinsey & Company on 10 Trends You Have to Watch.  The premise is after a year in turmoil, business executives are starting to look towards the future.  However, the world has changed, and with it, so have some key trends.

The trend that caught my attention – Management as Science -- falls squarely in the datarati realm:

“Data, computing power, and mathematical models have been transforming many realms of management from art to science. But the crisis exposed the limitations of certain tools. In particular, the world saw the folly of the reliance by banks, insurance companies, and others on financial models that assumed economic rationality, linearity, equilibrium, and bell-curve distributions. As the recession unfolded, it became clear that the models had failed badly.

It would be wrong to conclude that managers should go back to making decisions only on the basis of gut instinct. The real lessons are that the tools need to incorporate more-realistic visions of human behavior—most likely by drawing on behavioral economics, becoming more dynamic, and integrating real-world feedback—and that business executives need to get better at using them. Companies will, rightly, continue to seek ways to exploit the increasing amounts of data and computing power. As they do so, decision makers in every industry must take responsibility for looking inside the black boxes that advanced quantitative tools often represent and understanding their functioning, assumptions, and limitations.”

In retrospect, this makes perfect sense.  Human behavior is far from universally predictable.  Recall how the U.S. Government expected citizens to re-invigorate the economy by engaging in non-essential shopping with that first stimulus check.  Instead, what did many do?  Paid bills, bought groceries or tucked it away for the tough times to come.  Survival instincts won out over an algorithm.

Once you recognize that behavior matters, a natural follow-on is, “where does behavioral data come”?  No surprise, Google has a veritable treasure trove:

“Wu calls Google "the barometer of the world." Indeed, studying the clicks is like looking through a window with a panoramic view of everything. You can see the change of seasons—clicks gravitating toward skiing and heavy clothes in winter, bikinis and sunscreen in summer—and you can track who's up and down in pop culture. Most of us remember news events from television or newspapers; Googlers recall them as spikes in their graphs. "One of the big things a few years ago was the SARS epidemic," Tang says. Wu didn't even have to read the papers to know about the financial meltdown—he saw the jump in people Googling for gold.”

As for the rest of us, we can mine internal and public datasets, setup prediction markets, employ sentiment tools and/or hire behavioral economics consultants.  First though, I’d recommend familiarizing yourself with the field of behavioral economics, and pay special attention to the datarati ties. I plan to ease myself in with Dan Ariely’s Predictably Irrational. 

If you have experience applying behavioral economics in your business, or reading/learning suggestions, please share what you can in the comments or via email.

Community Insights: Services, Business Solutions, Portfolios, Management Units & Clouds (continued)

Cross posted from SOA Consortium Insights.  Join our conversation!

Last month, I shared some of our Community of Practice’s work in flight regarding services, portfolios, management units, and the fit within the overall IT landscape.  At the time, the following diagram best represented our on-going conversation.  [Click on diagram to enlarge]

Along with the diagram, I posed some questions for broader community feedback:

1.  Has your organization’s management perspective shifted away from business solutions (applications, business process implementations) to business capabilities/functions?  If so, in which areas: IT funding, IT delivery, IT operations, and/or IT product management?

2. Are you engaging in service portfolio management practices, such as service value prediction and assessment, marketing, sourcing, rationalization, refresh, and retirement?  If so, what techniques and/or tools are you employing?

For example, to manage service sprawl that began from federated service development and was compounded by a merger, one of our members is considering marketplace techniques, both real and predictive, to let developers choose which of a redundant set of services survives, rendering the less popular obsolete.

3.  Who manages the service portfolio?  How does this compare, contrast with the management of the business solution and IT asset portfolios?

Since that post, our group continued its discussion, which is reflected in this updated version of the Services, Portfolios, Management Units & Clouds diagram.  [Click on diagram to enlarge]

 

The major changes:

1. Explicitly stated a usage relationship between the IT Delivery Value Chains and the Service Portfolio.
2. Assigned management responsibility of the Service Portfolio to the Enterprise Architecture group, and declared that the Service Portfolio is part of the Enterprise Architecture Portfolio. (answered our question #3)
3. Indicated the Business Capability map is one of many Business Architecture Artifacts, and that those Business Architecture Artifacts are managed as part of the Enterprise Architecture Portfolio.
4. Explicitly stated the business-driven relationship between Business Capability criticality and Project Portfolio prioritization.
5. Collapsed the Cloud Service view into the main diagram.
6. Noted the specific management policies at each portfolio, recognizing the need for federated policy administration, communication, and enforcement, or as Todd Biske would say, “governance”.

We welcome your feedback on any aspect of our evolved diagram, as well as your thoughts on the following questions. (Question #3 is new)

1.  Has your organization’s management perspective shifted away from business solutions (applications, business process implementations) to business capabilities/functions?  If so, in which areas: IT funding, IT delivery, IT operations, and/or IT product management?

2. Are you engaging in service portfolio management practices, such as service value prediction and assessment, marketing, sourcing, rationalization, refresh, and retirement?  If so, what techniques and/or tools are you employing?

For example, to manage service sprawl that began from federated service development and was compounded by a merger, one of our members is considering marketplace techniques, both real and predictive, to let developers choose which of a redundant set of services survives, rendering the less popular obsolete.

3. Is your organization using, or considering, ITIL V3 to manage the full service lifecyle from inception (business architecture) through development/consumption (IT Delivery Value Chains), operations (IT asset and runtime management) and product management (service portfolio management)?

Please share any thoughts on the post, and/or our direct questions, via comment, blog post, or discussion forum.  Just link to this post, we’ll find you.

 

[Disclosure: The SOA Consortium is a client of my firm, Elemental Links]

Vacation Photos: Federal Stimulus Gone Wild?

Last week, I took an ‘off the grid’ vacation in Rangeley Maine.  The area is beautiful, featuring Maine’s crystal clear lakes, lush woods, western mountains and more than a few moose.  On Thursday, I hiked a small (easy) section of the AT:

Oddly, here’s what I encountered.  What could that orange thing be?

Did you guess this?

Takes “shovel ready” to a whole new level, eh?

Lessons from Baseball Science: A picture is worth 1000 data points

It’d be easy to chalk up today’s choice to my being in pre-vacation mode, but in truth, I’ve had this New York Times Baseball Science article open in a tab for nearly a month.  When I first read it, I immediately thought of connections to my recent post Lessons from Googlenomics: Data Abundance, Insight Scarcity. 

In the referenced Wired Googlenomics article, Hal Varian asks, "What's ubiquitous and cheap?" His answer "Data." He follows up with “And what is scarce? The analytic ability to utilize that data.” 

The Baseball Science article highlights an innovative way Major League Baseball is collecting even more player data – defense and base running – via a new system of high-resolution cameras and supporting software:

“A new camera and software system in its final testing phases will record the exact speed and location of the ball and every player on the field, allowing the most digitized of sports to be overrun anew by hundreds of innovative statistics that will rate players more accurately, almost certainly affect their compensation and perhaps alter how the game itself is played.

…In San Francisco, four high-resolution cameras sit on light towers 162 feet up, capturing everything that happens on the field in three dimensions and wiring it to a control room below. Software tools determine which movements are the ball, which are fielders and runners, and which are passing seagulls. More than two million meaningful location points are recorded per game.” 

However, the system output is “simple time-stamped x-y-z coordinates” which require sophisticated algorithms to turn the raw data into insights:

“Software and artificial-intelligence algorithms must still be developed to turn simple time-stamped x-y-z coordinates into batted-ball speeds, throwing distances and comparative tools to make the data come alive.”

Beyond turning the raw data into meaningful information regarding player actions and game outcomes, the teams, league, and legions of fans and broadcasters, still need to figure out how to act on, and manage, this data trove:

“Teams have begun scrambling to develop uses for the new data, which will be unveiled Saturday to a group of baseball executives, statisticians and academics, knowing it will probably become the largest single advance in baseball science since the development of the box score. Several major league executives would not publicly acknowledge their enthusiasm for the new system, to better protect their plans for leveraging it.

“It can be a big deal,” the Cleveland Indians’ general manager, Mark Shapiro, said. “We’ve gotten so much data for offense, but defensive objective analysis has been the most challenging area to get any meaningful handle on. This is information that’s not available anywhere. When you create that much data you almost have to change the structure of the front office to make sense of it.””

The above two challenges, making the data meaningful, and developing actionable business insights, are accomplished by individuals that Hal Varian refers to as the “datarati”:

“Varian believes that a new era is dawning for what you might call the datarati—and it's all about harnessing supply and demand. "What's ubiquitous and cheap?" Varian asks. "Data." And what is scarce? The analytic ability to utilize that data. As a result, he believes that the kind of technical person who once would have wound up working for a hedge fund on Wall Street will now work at a firm whose business hinges on making smart, daring choices—decisions based on surprising results gleaned from algorithmic spelunking and executed with the confidence that comes from really doing the math.”

In the baseball world, Billy Beane and Theo Epstein are considered ‘datarati’ archetypes. 

As a geek by trade and a lifelong baseball fan, I find myself intrigued by this new data collection technology and the resulting analytic and management possibilities.  Of course, it also got me thinking beyond baseball, and sports, to wonder what other fields (no pun intended) might benefit from digital camera based data collection and data point to scenario reconciliation.

From my own background, I can envision the technology being applied to analyze and improve efficiencies in retail stores, warehouses and factories.  How about you?

Some questions to consider:

Could this data collection technique benefit your organization? 

How about as a data consumer?  Can you think of an external scenario that might provide meaningful “simple time-stamped x-y-z coordinates” to your organization?

Has your organization embraced the rise of the datarati?