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Death of 'On Average' By @Schmarzo | @CloudExpo [#BigData #IoT #M2M]

Big Data is enabling actionability at the individual level

On average...you should replace the oil in your car every 3,000 miles.

On average...you should visit your dentist every 6 months.

On average...you should compensate casino players 20%.

On average...you should expect a 2% click-through rate on paid search ads.

People and organizations are accustomed to relying upon "on average" guidelines in order to manage their lives and businesses. It is a flawed but convenient way of normalizing life, because we haven't the detailed data or the analytic capabilities to do anything else.

But "on average" guidelines are severely flawed; 50% of your customers are above the "on average" line and the other 50% are below it. In reality, you are either spending too much or spending too little on your individual customers; you are wasting money on over-served customers and leaving money on the table on under-served customers.

However, big data changes that paradigm. We should reject running our businesses using "on average" rules of thumbs. We now have the detailed data and the analytic capability to understand behaviors, tendencies, propensities, characteristics, trends and patterns at the individual level - whether those individuals are humans or machines - and we can leverage these insights to make improved business decisions.

The Problem with "On Average"
Let's say that your NBA basketball team has to play the Golden State Warriors in the playoffs, and you are devising a plan to slow down Stephen Curry ("You can only slow him down, you can't contain him!"). We know that on average Stephen Curry shoots 48.7% from the field, but that's not very actionable. With a little bit of research, we can determine the following details about Stephen Curry's shooting percentages[1]:

 


Shooting Percentage

Effective Shooting Percentage

2-point shooting

52.8%

52.8%

3-point shooting

44.3%

66.5%

Stephen Curry's Effective Shooting Percentage analysis (which takes into account that a 3-point shot is worth 50% more than a 2-point shot) clearly dictates a defensive strategy of "chasing Curry off of the 3-point line" in order to prevent him from shooting 3-point shots (just ask the New Orleans Pelicans)!

However with even more data and analysis, we can uncover additional insights about Stephen Curry's individual shooting tendencies and propensities. Figure 1 shows his shooting percentages from different spots on the court[2].

Curry Shooting Chart

Figure 1: Stephen Curry Floor Position Shooting Percentage

We now have Curry's performance numbers at a level that we can act on - forcing him into shooting in the areas circled in red!

Let's take this analysis one step further, and imagine or brainstorm the additional information or data that you'd want to gather about his shooting tendencies and propensities such as:

  • How does he shoot on the road versus at home?
  • How does he shoot in certain cities (elevations, time zones)?
  • How does he shoot at certain times of the year (holidays, flu season, shorter daylight)?
  • How does he shoot against certain teams (defensive players, defensive alignments)?
  • How does he shoot in afternoon games versus evening games?
  • How does he shoot against the better teams (above .500) versus the worst teams (below .500)?
  • How does he shoot against teams in his conference or division?
  • How does he shoot when his game is nationally televised?

There are many other aspects that we might want to uncover and test in order to craft a more confident defensive strategy.

The Death of On Average and EMC World
In a world of more detailed and more diverse data sources, we can now discover tendencies, propensities, behaviors and characteristics at the individual level, putting an end to the world of making business decisions based upon "on average" guidelines.

For example: on average you should replace the oil and oil filter in your car every 3,000 miles. However, the most appropriate mileage can vary wildly based upon variables such as when the car was lasted serviced, who serviced the car, what sort of service was done, the age of the vehicle, compression of the engine, how hard the vehicle is typically driven, city or highway driving, amount of stop-and-go driving, in what weather conditions, and numerous other variables.

For example: on average, "they say" you should see you dentist every 6 months. However, the most appropriate time for you can vary depending upon your dental history, age, diet, stress, exercise, type of toothbrush, type of toothpaste, how often you floss, how long you floss, what your insurance plan covers, and numerous other variables.

I am going to discuss the death of "on average" and the power of data science to create analytic profiles at EMC World, where I am presenting "Expert Guidance To Achieve Big Data Maturity" on Monday, May 4th at 4:30. Be there, or be average.


[1] http://officialwarriors.tumblr.com/post/48149399753/stephen-currys-regular-season-shot-chart


[2] Numbers from Stephen Curry's 2014-2015 regular season

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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