Simplify Business Decision-Making Using Predictive Analytics

On the evening of May 1, 2012, as I enjoyed a twighlight jaunt along Battery Park in New York, I heard what sounded like parade in the distance. Curious about its source, I walked toward it and realized it was a May Day protest march organized by the Occupy movement. The march, which had begun a few hours earlier in the vicinity of Union Square, snarling traffic throughout midtown and lower Manhattan, had reached the Financial District and was being carefully monitored and corralled by a large police force that had erected barriers all along the route in an attempt to impose some order upon the chanting, sign-wielding, drum-beating masses.

Having lived in various parts of the Boston Metro area for 24 years, I have experienced the local public transportation system from many angles: at different times I have been a regular user of the bus, the “T” (Boston’s nickname for its subway), and the commuter rail – all managed by the Massachusetts Bay Transportation Authority (MBTA). I have also experienced public transportation in many cities around the world: I carry active public transportation cards for Boston, New York, Washington, Chicago, London and Rome. Given the choice, I would much rather take public transportation than a taxi or car. Sadly, I have found that in some ways, Boston’s public transportation system is uniquely and frustratingly inadequate.

After a decade of fast-growing budgets, military decisions makers are now facing significant budget cuts and must allocate resources accordingly. A number of military officers and senior civilians are confronted with a down budget for the first time as decision makers.  Their mental framework for allocating resources and prioritizing needs is entirely defined by their experience of a growing budget and dramatically fewer constraints than today. As a result, the prevailing decision heuristics (the subconscious cognitive mechanisms, or “mental accounting”, by which humans make decisions) in use today are heuristics that worked in a very different environment and under very different constraints, and are unlikely to perform adequately in the new budget environment particularly during a transition period.

Every day we hear reports of new cyber-threats, and every single time they point to the same culprit: people as the weakest link in cyber-security. In addition to my earlier rant on cybersecurity and human behavior, a great piece was posted a few weeks ago in Government Computer News that articulates the issue very well. A case in point is the recent drone virus revealed by Wired. It is a great example of the lack of appreciation for the tradeoffs you need to make when running missions. After the 2008 incident in which an infected removable media drive was the vector of entry for a worm into an overseas secret-level DoD network, the use of USB drives has been severely restricted throughout the military.

Icosystem is teaming up with Agile New England to sponsor the Boston Coderetreat for the Global Day Of Coderetreat on December 3rd, 2011. Zach Shaw, a Senior Software Developer at Icosystem and organizer of the Boston Software Craftsmanship Group, will be facilitating the event.

Icosystem is often asked how we know what parts of a business to model since a business can be such a complex system.  The answer lies in “emergent behavior”.  Emergent behavior can be understood through a simple example like modeling traffic flow.  Drivers think only about controlling their own car. In the simplest situation, they speed up when the car in front of them moves and slow down when the car in front of them brakes.  Even with these two simple rules, the interaction of cars produces complex traffic jams as shown in the following videos: 

Enormous amounts are invested by foundations each year into the non-profit sector. The traditional model of evaluation of foundation-funded programs is a very linear one:

I am a big fan of LinkedIn’s. I even PAY for the service from time to time, just to be a power user for a couple of months. There are many ways in which the LinkedIn experience could be improved – their search capability is appalling and exploration is exhausting (we have a solution for THAT), not to mention the lack of tools for managing and making sense of a large group of connections, as well as their streams of updates of all sorts (we have a solution for THAT too!).

When developing any type of predictive model, it is normal to think of “data” as a set of numbers, typically numerical measurements based on observations. It is a commonly held opinion that the accuracy of a model depends entirely on the quality and quantity of available data, as reflected in the use of statements such as “garbage in, garbage out.”

I recently heard tell of a Fortune 1000 company with a sales force in the tens of thousands that discovered its sales representatives were spending too much time in low potential accounts. It is a “repeat” business, and the sales force was spending most of their efforts on either the big buyers of their products or in places where there was little or no current business. Their research revealed that if the sales force was more focused on middling accounts, those that were only partially penetrated, they could expect better results. How can this be? It seems to imply that sales representatives are acting “irrationally” or against their own interests.