Operational Analytics - the statistics is the easy part
As someone who once preferred to bake cookies rather than study for statistics exams, the works easy and statistics don’t always go together. However today, at a presentation by IBM and Azendian, the math indeed seemed easy. There are three places analytics are commonly used - new business (above the line), greater efficiency (below the line) and managing risk, This session looked at the operational (below the line ) side, an area not often cited in the examples of analytics in action or practice.
Using an example of HR analytics in looking at attrition, our speakers covered the many non technical areas in an analytics project that are important for success Data governance - who should see what information. Process & policy - how does the use of an analytical model impact existing process and policy Buy in - many senior executives, the ones to sign off and sponsor projects, got where they are from experience. Datadriven decisions, particularly non intuitive ones, may not come naturally to them Communicate with your audience - don’t try to sell analytics with fancy math, sell easy to understand business solutions Business drive successful analytic projects, not IT
At almost no point did maths come into the equation to make analytic projects successful. Citing examples from BWM (reducing cost of brake light errors due to sunroofs), Rugby (keeping your star player free from practice injury https://community.spiceworks.com/topic/553729-novel-use-of-data-analytics-helps-new-zealand-rugby-team-pull-off-epic-upset ) and bike teams http://www.bbc.com/sport/olympics/19174302 that turn 1% improvement to huge success.
For me, few ideas formed to grab and use our data to answer some operational improvements. As with many analytic projects, there isn’t a silver bullet to fix all problems. But collaboration, opening your mind to new ideas and asking the right questions leads to doing it better. Thanks to David Hardoon and the team at Azendian.










