Business intelligenceis the collection of technologies that aid an organization with enabling theirworkers with data, information, and knowledge to make better decisions.
Organizationsare leveraging the business intelligence they collect to deploy and experimentwith different techniques to drive business decisions, functionality, andprofits. For example, in retail, business intelligence is used for customersupport to enable a user profile for customers and provide them with targetedgrocery coupons and advertisements. I chose this article because basketball ismy favorite sport to play and watch. Also, I’m currently taking IS 428 which isdata mining, and this article spoke about the combination of the NBA’s businessintelligence of using data mining to discover interesting patterns inbasketball game data for teams. As per my data mining class, data mining is abusiness process for exploring large amounts of data to discover meaningfulpatterns and rules.
Data mining is a subject of business intelligence. It’s theprocess of using raw data to infer important business relationships. Or in theterms of the NBA, data mining is the process of using raw data to infer yourteams advantage over other teams. If you watch an NBA game today, you’ll hear terminologiessuch as a small ball lineup, run or spread offense, or this particular playeris a key 3 and D player. Data and performance metrics have reinventedbasketball into a new game and helped provide metrics to these terms.
The NBAand its teams have embraced big data and analytics to maximize winning and playerpotentials. With new metrics consistently being used, it currently shows thatNBA teams that run a small ball lineup, perform fast breaks and can spread thefloor have a likelihood of scoring more points, containing the other team andwinning basketball games. That’s why players that can shoot 3 pointers well andplay really good defense are coveted for rotational spots on NBA teams toprovide support for superstar players. The 3 and D player is important becauseof their potential to make a higher percentage of 3 points shot and theirpotential to affect another player from scoring on the defense end. Datametrics collected from players can show which players excel at making jumpshots and playing defense. The way the game is played now has changed dramaticallyfrom the way it was played 25 years ago.
One is able to understand this newwave by watching and listening to different NBA games because broadcastersoften talk about it. One main element I learned from this article was the NBA’suse of advance scout, which was the first data mining and knowledge tool fromIBM that NBA teams utilized. Advanced scout was first introduced to the NBA inthe 1995-1996 season to most of the teams. Advanced scout first identified tothe Orlando Magic, during the playoffs against the Miami Heat, that using asmall ball line up would prove to be advantageous for them on the court.
Theylost their previous two games and advanced scout provided information that withcertain players in the game, the Magic outscore the Heat by an average of 15points. This tool helped the team make a better decision as they were able towin the next two games and become an early example to prove that performanceanalytics can provide useful decisional support for teams. The article talksabout the creation of advanced scout and the ripple effect it had. What I foundinteresting was that before the introduction of advance scout, the best metriccollected for basketball success was the percentages of shots made by a playeror a team during the game.
Now, there are so many high tech data metrics usedby an NBA team to increase their chances of winning more games.