Managing Complexity using Big Data

Posted by Erich Schneider on September 8, 2013

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While SAP is launching the Big Data Bus, the global Business Strategy consulting community is gearing up for the 5th annual Global (Peter) Drucker Forum where this year’s theme is “Managing Complexity”.

Is there possibly some context?

While in general it could be assumed that more complexity could make decision making more difficult, let’s consider a hypothetical scenario in the world of sports betting in Las Vegas, where additional context in the form of Big Data, could maybe improve decision making.

In this hypothetical scenario, which of course is a huge simplification considering the amount of variables which can be contemplated, I tried to describe the context between Big Data and the challenge of managing complexity. Starting out with a simple scenario and adding gradually more details, additional context and reference points, where you can judge for yourself if additional information can provide more context, and potentially can create more complexity, or if more details (Big Data ) enable and simplify decision making in complex scenarios?

Hypothetical scenario

You are participating in a poker tournament in Las Vegas, and there is an opportunity to place a bet on a football game, as there is a short interruption in the poker tournament. So far you lost some money in poker so you like to hedge the outcome of your chances on winning by betting on a football game in parallel. There is a commercial break in the game and you have 5 minutes to make your bet. As you are not watching the game you need to rely on external information made available to you.

1st level of information: Team A leads team B in a NFL playoff game with 5 points.

Are you ready to make a decision on which team you put your money on, or do you want to use the 5 minutes and research more context, and manage complexity ?

Except if you are a die-hard fan of one of the teams, most likely, you will ask for more context in order to make an educated guess. As you have some basic football knowledge, you know that, unlike in soccer, that a 5 points difference in football is manageable to bridge, as a single touchdown can score at least 6 points, so let’s move to level 2.

2nd level of information: There are 2 minutes left in the game. Are you ready now? Maybe this is sufficient for you, or maybe because you can, you go on to the

3rd level of information: Team B is in possession of the ball. Although that sounds like pretty good information to place your bet, and maybe you would even increase the amount you are betting, would you like more context with that?

4th level of information: The number 1 quarterback of team B got out of the game with a concussion during 4th quarter, and one of the back-up quarterbacks is on the field leading the offense. Are you now ready to place your bet?

Most likely not, as the additional complexity of information (more context) changes the game, so you go for more details.

5th level of information: Player X (insert here the name of the player first popping up in your head just for fun sake) is replacing quarterback 1. Now, two different scenarios can be anticipated:

  1. You have enough information because you have a pre-conceived notion either for Player X or against him, and in this case you are most likely ready to place your bet now.
  2. If you do not have a strong perception and/or if you are a number person, imagine you have a SAP HANA database with NFL player stats ready at your fingertips and you could research in a couple of seconds what are the odds of player X making a touchdown in the last 2 minutes of the final quarter to win the game? Would you want to see that information, as you see the additional complexity as beneficial for your decision making? Let’s assume you do.

6th level of information: The odds are 60% that player X makes the touchdown, but considering player X only played in the fourth quarter, the real-time statistics available to you are limited. Are you ready to make the call based almost solely on historical stats? Entering the

7th level of information, which is a real- time sentiment analysis of social media comments about the performance of player X in this particular game by real people watching the game. As you know that player X has in the past shown quiet some deviations in his playing levels, which are smoothed out in the historical statistics, would you like to match the real-time sentiment analysis (crowdsourcing anyone?) with the statistics or would you rather pass?

Considering the additional context could potentially invalidate the hard numbers of the stats, do you proceed and place your bet with more or with fewer context?

Translating this gaming experience into the world of real Business scenarios in your industry, let’s ponder: If you have a choice to make decisions based either on level 1 information or on level 7 information, what would be your Business strategy? Where would you put your money and how much?

Would you deliberate that the additional information will improve your decision making capability, or would the additional context make the whole process too complex for your management team?

If you think the additional information would be beneficial, maybe you want to engage in a discussion with the SAP Big Data experts at TechEd 2013. And if you liked what you heard, maybe you could drop in at the TechEd session about SAP Enterprise Architecture and we could discuss the deployment options of the SAP HANA real time data platform in your unique Enterprise Architecture to enable Big Data business processes.

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