At times, our consulting practice is faced with a conundrum: the evolution of certain technologies and practices for enhanced information exploitation suggest changing business operations in a way that might reduce, or even eliminate, some participants' roles. In other words, implementing technical changes to benefit the organization simultaneously have a determinental impact on individuals within the organization.

In terms of self-preservation, it is not in the best interests of these individuals to support new technical initiatives that might result in their own termination. Yet in order to do their job the right way, they are obliged to do what is right for the organization, right? This situation resembles the game theory concept of a zero-sum game, in which moves that benefit one player equally have a negative impact on another player.

The challenge, then, is to determine how to socialize the evolution of the program in a way that demonstrates mitigation for any individual impacts or displacements. For example, when suggesting an action whose side effects include the elimination of a specific person's role, seek ways to evolve that person's responsibilities to support the change process and long term maintenance of the technical evolution. Doing so will finesse the "zero-sum" situation and will provide new challenges for both staff training and organizational improvement.

Blackjack is a fascinating game, a steadfast mainstay of the gaming industry, and is also the subject of what might be called "duelling analytics." The seminal discourse, Thorp's "Beat the Dealer," explained how computer simulation provided a means for establishing a basic strategy for playing blackjack to significantly reduce the casino's edge and enable an (almost) level playing field for the player.

However, in the intervening years, the gaming industry has taken evasive action, with institution of rules that limit the ability to exploit the basic strategy - increasing the number of decks in play, modifying the payouts, only dealing out 50% of the cards in play, etc. These innovations are likely the result of the casinos' own analytics - determining how slight variations to the rule sets adjusts the casino edge, then implementing those rules.

Of course, some advanced knowledge in statistics and probably can tip the edge back to the players, as long as they tread carefully. For example, take a look at Mezrich's book on the MIT blackjack team.

Analytics is key for a game like blackjack, in whcih the rules are clearly defined, dealer actions are prescripted, and except for the few actions of placing a bet or collecting your winnings, is largely automatic. However, poker, while being another popular casino attraction, is considered more of a game of skill, not luck, and while analysis does provide insight into how to play certain dealt hands, the skill lies in the abilitiy of the player to transform limited information into decisive action. Watching poker tournaments on TV, which are enhanced by showing the viewer each player's hands, demonstrates how thought processes drive the activity. So while analysis is important, success is a combination of information processing and operational intelligence.

So, here is the question: can we abstract the difference between blackjack analytics and poker analytics and apply these within a business intelligence program?