Data is everywhere and available to everyone, but it’s those who actively explore data who find new insights and a competitive edge. Passive consumption of data (looking at reports and dashboards other people made) will only provide predictable outcomes. The actual value of data comes to those who actively engage with the data and dig into it for valuable insights, creating value from enterprise data analytics.
Let’s look outside typical enterprise data applications and explore more unusual environments, the ones where IT professionals don’t normally lurk, and see how analytics has had an impact. Here are three examples from the worlds of TV gameshows, country music and Hollywood that illustrate how there are data insights that are up for the taking if you’re willing to get a little creative.
Putting data analytics into jeopardy
Ask most people how to win Jeopardy, the classic US TV gameshow, they’d answer that excellent general knowledge is key. That will get you so far, but finding the edge requires data. Jeopardy, it turns out, is a series of strategic manoeuvres that can be cracked through the use of insightful data analytics and the evaluation of probability.
James Holzhauer better known as “Uncle Jamie” applied data-driven approaches to his time on Jeopardy, leading him to one of the greatest Jeopardy streaks of all time. His unparalleled 32-episode stint netted him over $2 million in winnings.
What did he do? While other contestants were just memorising the answers to random trivia questions, James was using data and data analytics to learn how to target the Daily Doubles and create a successful cash-accumulation strategy. Quite simply, James actively used data – and a lot of practice with a fake buzzer – to give himself a competitive edge. Find out more in this excellent NPR podcast broadcast during his winning streak.
Luck? No. Logic? Yes. James’ winning streak was ended by information scientist, Emma Boettcher, who wrote her master’s thesis on Jeopardy: the data geek beat the other data geek!
Balance sheets and staff remuneration — the value of data is rocketing
The best organisations, or so Greg Hanson from Informatica recently told Information Age, remunerate people based upon their ability to demonstrate good culture and good activity around managing data. Is it time then to give more thought to the value of data, how it is managed, and how it sits on balance sheets?
Data lifts the lid on the good ole boys
How might you go about studying and applying a critical eye to the changing nature of a genre of music? Well, you could listen to a lot of it. Or you could turn the lyrics into data and analyse it. The insights can be profound as one sociologist recently found.
Applying data analysis to chart-topping country songs from the 1980s through the 2010s, Mississippi State University sociologist Braden Leap pinpointed trends of objectifying women and glorifying male whiteness.
Leap crunched through 800 weeks of the Billboard chart of country music hits to reveal how notions of masculinity were being shaken up. For example, his data analysis revealed how the image of white men shifted from being the chief breadwinner to providers of alcohol, transportation and a place to hook up. Leap cites several songs that exemplify the change in how working white American men define their masculinity, for example Dierks Bentley’s “Drunk on a Plane”
For those of us who aren’t country music fans, this may sound amusing. But, as Leap has argued, his analysis shows how popular culture might be reinforcing a male self-image that is becoming narrower and more hostile to women. In this case, analysing music lyrics has uncovered the popular culture messages that are affecting peoples’ lives.
A look at data analytics trends for 2019
As our understanding of data analytics has developed, data analytics is being used in wave of innovative and exciting new ways. From IoT analytics and augmented analytics and DataOps we look at the top data analytics trends for 2019
The best picture oscar goes to smarter data analytics
Finding the next blockbuster movie is a challenging game that destroys more careers than it creates in Hollywood.
For decades Hollywood execs have used test screenings to collect data about what an audience likes or hates about a movie. Whether these insights are helpful has been challenged by many directors, writers and stars who have said so much depends on how representative the audience actually is. A famous example of how test screening data got it wrong is when preview audiences hated the ending of the modern Hollywood classic and box office success Seven. That same scene is now regarded as one of the most dramatic end sequences in movie history.
Given how test screening and market research have limits, there’s been considerable interest in how technology could be used to improve the understanding how audiences might engage with a movie. This has ranged from the creation of neurocinematics, which looks at how audiences’ brains react to movies, to the use of wearable bio-sensors to track and measure the physical excitement of watching a blockbuster.
A new concept that could supplement older techniques and help identify a hit even before cameras have rolled is using AI and data analytics tools to analyse historical data, specifically correlating trends between performance, movie genres and types of talent.
Cinelytics and ScriptBook are some of the firms offering to use AI-powered data analytics to identify blockbusters and avoid flops. These companies make big claims for their software but the extent to which they are used by Hollywood executives isn’t disclosed. Nonetheless, you have to feel the use of AI will rise as the studios look at how Netflix harnesses AI and data analytics to commission and deliver content that’s more compelling for viewers.
In all of our jobs we spend our time finding ways to make decisions that make us more efficient. This might be to ease workloads, save costs, or discover new opportunities. As each of the examples above shows, success goes to those who actively consume and explore their data. Look at your organisation: do people engage, converse, share data insights? Or does everyone passively consume static data reports? If it’s the latter, you need to move from passive to active consumption.
Andy Cotgreave is a Technical Evangelism Director at Tableau.