BROOKLYN DYNAMICS

Predictive Analytics for the 21st Century

Email UsINSYTE - New Sports Analytics App from BD

With offices located in the U.S.A. and Australia, here are some examples of our work:

  • Security and threat analysis, modeling, prediction and detection for major banks, (Australia & Malaysia).
  • A wide variety of analytics solutions for amateur and professional sports, (including machine learning/AI solutions). Talent ID & scouting, performance analysis, situational outcomes.
  • Patient and activity modeling and simulation for a major health care provider.
  • Market modeling and trend prediction for investment firms.
  • Modeling of specific species breeding and reproduction to eliminate environmental impact and assist with species survival.
  • Analytics projects for the Defence Forces involving high-level hardware integration and machine learning. Risk/Threat assessment.
  • Student recruitment, retention and student body management analytics for several universities.

Brooklyn Dynamics - An Overview:

1. Teams/Companies should work with a league-wide/Industry wide technology early to analyze available data. Analytical applications are rapidly copied, and the only way to achieve competitive advantage from data that every other team has is through innovation in application and execution. Teams/Companies that adopt data-generating technology early in the cycle can quickly develop analytical capabilities relative to it, and can maintain advantage even when it is adopted by others

2. Move toward predictive and prescriptive analytics. Most analytical activities in professional sports/Companies continue to be descriptive analytics—some form of reporting. But such analytics offer no guide to the future, and they don’t tell players, coaches or corporate managers at the frontline what to do. Predictive and prescriptive analytics are more powerful and useful. And if firms have been gathering descriptive analytics for several years, they probably have enough data to model it, which would then allow prediction, optimization, and recommendation.

3. Ironically, the greatest value from predictive analytics typically comes more from their unexpected failures than their anticipated success. In other words, the real influence and insight come from learning exactly how and why your predictions failed. Why? Because it means the assumptions, the data, the model and/or the analyses were wrong in some meaningfully measurable way. The problem and pathology is that too many organizations don’t know how to learn from analytic failure. They desperately want to make the prediction better instead of better understanding the real business challenges their predictive analytics address. Prediction foolishly becomes the desired destination instead of the introspective journey.

4. When predictive analytics are done right, the analyses aren’t a means to a predictive end; rather, the desired predictions become a means to analytical insight and discovery. We do a better job of analyzing what we really need to analyze and predicting what we really want to predict. Smart organizations want predictive analytic cultures where the analyzed predictions create smarter questions as well as over statistically meaningful answers. Those cultures quickly and cost-effectively turn predictive failures into analytic successes.

5. To paraphrase a famous saying in a data science context, the best way to predict the future is to learn from failed predictive analytics.

6. In a complex system, however, elements can potentially interact in different ways each time because they are interdependent. Take the airline control system—the outcomes it delivers vary tremendously by weather, equipment availability, time of day, and so on.

7. So being able to predict how increasingly complex systems (as opposed to merely complicated systems) interact with each other is an alluring premise. Predictive analytics increasingly allow us to expand the range of interrelationships we can understand. This in turn gives us a better vantage point into the behavior of the whole system, in turn enabling better strategic decision-making.

8. Predictive analytics is bringing new levels of speed, relevance, and precision to decision making. Prediction as a mode of engagement and insight will increasingly be a requirement for setting strategy. The companies and executive teams advancing, mastering, and integrating prediction as core to how they evolve strategies and manage will be the distinctive performers and leaders of the future.

9. Predictive analytics has the power to change what organizations do and how they do it. Many companies are equipped with the right technology, but most lack the organizational capacity to take full advantage of predictive analytics. In addition, many organizational processes are not built to make use of analytics and make it a competitive advantage.

10. High-performing organizations leverage the power of analytics by channeling their efforts in four areas: focus, adopt, adapt, and activate. These companies have embraced a new paradigm that promotes agility, fast execution, and lasting organizational change.

In an article titled, “The Value of Big Data: How analytics differentiates winners”*, Bain & Co surveyed executives at more than 400 companies around the world (most with revenues of more than one billion dollars). It found that only four percent of companies are really good at analytics, an elite group that puts into play the right people, tools, data and willpower into their analytic initiatives. This elite group is already using insights to change the way they improve their products and services. And the difference is already quite stark:

  • Twice as likely to be in the top quartile of financial performance within their industries
  • Three times more likely to execute decisions as intended
  • Five times more likely to make decisions faster

*The Value of Big Data: How analytics differentiates winners, published on September 17, 2013. By Rasmus Wegener and Velu Sinha