Statistical analysis in sports has been around for a long time, but the topic of sports and analytics has attracted more attention in the last decade. The release in 2011 of the movie Moneyball (based on the book Moneyball: The Art of Winning an Unfair Game, by Michael Lewis, published in 2003) made the use of analytics a popular subject for public consumption.
The day one “plan” of a dash of Hive, a pinch of SQL and two cups of R, turned into a stew of Hive, Pig, MapReduce, Java, Pentaho Data Integration and R. And the algorithms we ended up deploying look quite a bit less sexy than the elegant formulations we started with.
The bad news is that our efforts took a different turn every other week during the two month duration. The good news is that, in the end, what was learned will provide “lift” to the customer in the form of a prototype for a new piece of data business.
In retrospect, I guess I shouldn’t be surprised. The speed bumps we experienced along the way are common in the data science world where, alas, data sets are unlike the ones we analyzed in statistics classes.
The dynamics of a sports organization involve three main forces: the team, the game, and the fan. In essence, the team is the passion that gets the fan fired up, the game is where it all happens, and the fan keeps it alive. Of the three dynamics, the one the sports organizations can control the most is the fan experience.
The team and the game have many variables: Injuries can bench a quarterback or an unlucky miss of a three-pointer with two seconds left in the game could cost a team the championship. But the fan experience is the area sports organizations can shape the most by providing the right products and services the fans want at the right time and in the right format.
Is your mobile business intelligence (BI) strategy aligned with your organization’s enterprise mobility strategy? If you’re not sure what this means, you’re in big trouble. In its simplest form, enterprise mobility can be considered a framework to maximize the use of mobile devices, wireless networks, and all other related services in order to drive growth and profitability. However, it goes beyond just the mobile devices or the software that runs on them to include people and processes.
Question: What is the maximum level of accuracy with which you can predict the toss of a fair coin?
Answer: 50%
It does not matter that you’ve set a mandatory minimum forecast accuracy level of 90%, or even 60%. There is no incentive, no bonus, no allocation of restricted stock options that could make me forecast a coin toss at better than 50%, nor any threat or punishment. The only thing threats accomplish are to invent ever more clever ways to tamper with the coin; to cheat.
Which brings us to this important forecasting principle: forecast accuracy is first and foremost a property of the data itself.
In my post “Mobile BI” Doesn’t Mean “Mobile-Enabled Reports” I highlighted two main areas that affect how organizations can go about realizing the benefits of mobile BI: enterprise mobility and BI maturity.
Today I want to focus on the latter and outline high-level strategies that require different avenues of focus, time, and resources.
Before an organization can execute these high-level strategies, it must have the following:
- An existing BI framework that can be leveraged
- Current technology (hardware and software) used for BI that support mobile capabilities
- A support infrastructure to address technical challenges.
If you have not witnessed the deluge of big data and business analytics media coverage to date, then welcome back from the coma you were apparently in for the last couple of years. For the rest of you, perhaps you have the same nagging question that I have: Are big data and business analytics such a big deal that if our organization is late to the party in deploying them, we will never catch up to our competitors?
Early in my career, I was encouraged to always ask even the simplest and most obvious questions, including questions about well-known topics that were assumed to be understood by everyone. With that in mind, let’s answer the question, “What is business intelligence (BI)?”
As you read this post, you probably fall into one of these three categories:
You know exactly what BI is because you eat, sleep, and breathe it every day. BI is in your business DNA.
The term means nothing more than the name of an exotic tech cocktail that might have pierced your ears, figuratively speaking of course.
You‘re somewhere in between the two extremes. You’ve been exposed to the term, but haven’t had a chance yet to fully digest it or appreciate it.
Do you have something to learn about BI? Let’s roll up our sleeves and get to work.
{According to Rick Smolen, a former Time Life journalist and photographer] one of the biggest opportunities is making better use of previously ignored “dark data”. “For years, meteorologists have had to filter out ‘bioclutter’ from Doppler radar weather systems – the “noise” generated by flocks of birds or bats. But when bird researchers realized they had 15 years of invaluable data on migration patterns they were delighted!”
Just as we know that mobile isn’t just about one or two sexy apps, the step to gain the ability to deliver reports on a mobile device alone isn’t synonymous with mobile BI.
In order to deliver the true business value of mobile BI, organizations need to formulate a carefully thought-out mobile BI strategy that not only leverages the technology’s strengths but also minimizes its weaknesses within a supported infrastructure. The mobile intelligence framework can’t exist separately from or independent of the organization’s business or technology strategy.