Data analytics is everywhere.  Just turn on your favorite college or pro sports team.  Data abounds, providing scores, stats, and decision-making insight.  Some of the biggest names are involved- ever see the SAP-NHL Coaching Insights app, IBM SlamTracker, NFL Next Gen Stats (powered by AWS), or NBA CourtOptix (SAP & Microsoft), to name a few?  Statistics and professional sports have been together for years- Elias Sports Bureau has been the official statisticians for Major League Baseball since 1916- and they also gather stats for the NFL, NBA, MLS, WNBA, and PGA.  AI-generated data drives non-sports-related decision-making– across all industries.

The point here is that data analytics, often leading to predictive analytics (determining future performance based on current and historical data), is used everywhere and affects the decision-making process for leaders in business, sports, and government.

Data efficiencies help leaders at all levels.  Algorithms have greatly increased trading profits.  The prevalence of data in sports is highlighted above.  Do you remember Moneyball?  It’s a story about Oakland A’s general manager Billy Beane, who first used analytics to put a winning roster together for a club that didn’t have the money to pay for the best players in the league – by a longshot!  The squad became the first team in American League history to win 20 games in a row and brought the A’s to the 2002 playoffs.

While data analytics has obvious merit, some data still hasn’t been proven to be predictive.  Donald Trump won the U.S. presidency when no national poll predicted that result.  Clinical trials and the data received often have not proven that a drug works.

The issue is the effect of missing data.

The abstract in a report by the National Library of Medicine touches upon the effects of missing data:

Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results. In addition, it causes a significant bias in the results and degrades the efficiency of the data.

Here is a commentary from the Chicago Booth Review about the issue of missing data and how it may skew the results of predictive models.  See how they propose an improved method for handling missing data for better results.  It might just provide more confidence to trust the data!  What do you think?  Let me know by reaching out to me here.

~ Brian Kasal- The Leadership Matrix

Click Here- A Better Way for Finance (and Others) to Handle Missing Data

Added Bonus- Moneyball Official Trailer

P.S.- Did you see my last Leadership Matrix post? Has the Federal Reserve Outlived its Usefulness?

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