When I talk to people about big data and data analytics I try to tailor the message to their experience level. For example, if I’m talking to data scientists I’m generally talking at a much different level than if I’m talking to people who have no clue what the term ‘big data’ means.
For the latter group, I try to find popular examples of data analytics to help them grasp the concepts behind analytics and big data. One of the examples I use often is the movie Moneyball. Most people I talk to have seen the movie and with a little explanation, they can see how the movie and storyline evolves from the use of ‘old’ methods of instinct and ‘rules of thumb’ to analyze baseball players to the ‘new’ methods of data analysis.
Using the example of Moneyball helps people grasp the concepts of data analysis and understand how data can be used to drive decision making capabilities (ot at least assist with decisions).
I like using examples like Moneyball. It’s not quite an example of big data in action, but it is a great example of how organizations can use data to make better decisions.
This morning, I read another example of a sports team using data analytics and big data to build a better team and organization. The Toronto Maple Leafs have recently began using big data methods and systems to analyze data from across the National Hockey League (NHL) to assist in driving new decisions about players, lineups and the overall operations of the team.They are following the Moneyball example and replacing their hunches with data to attempt to make better decisions.
Time will tell if the Maple Leafs win a championship in the next few years. While that challenge is quite daunting, a major challenge for the Maple Leafs (and any other organization) is to apply data-driven decision making to their organization while also keeping those ‘hunches’ around to help guide overall decisions.
Data is wonderful but data works best when you find ways to combine that data with instinct and experience. That’s how you win championships and grow revenue using big data.