Your Analysts Are Leaving — pt 2
Here is why and how to address it
Natural disasters are only fun in the movie theatre. The real thing is anxiety inducing at best and life threatening at worst. They are also generally unexpected events that occur infrequently and often with limited warning.
In the first 6–12 weeks of an analyst’s employ at your company, your data environment is much like a natural disaster. It is anxiety inducing, career threatening, and sadly (as noted in part 1) unexpected. Worse still, unlike a tornado, hurricane, or even volcano — it isn’t going anywhere.
Despite endless promises, most companies do little more than toss a few maidens… I mean IT professionals in any attempt to solve the issue. At least for our volcanic islanders — volcanoes eventually go dormant. Your data problems will only fester and multiply.
I noted in the last article that, in my experience, many analyst last only 18 months. One might hypothesize that it takes roughly six months to fully grasp just how bad a companies data infrastructure truly is. Six more months of trying to fix it - would leave roughly six more months to get the hell out of here! I am just hypothesizing, but consider that model.
Think about that learning curve. Well really it is a tidal wave…
We all know the first six months on the job is far less productive than most. But our hypothetical analysts then spend the next six months fighting for a cause they ultimately don’t win. Their final six months is looking for a new job. They are likely not focused and will most likely carry all learning and progress away when they leave. Is it any wonder that so many question the value of analytics?
Studies on employee replacement are sparse and fleeting, but a general estimate claims that it cost $30K to replace an employee and another $50K may be lost in productivity. For a data analyst or other analytic professional, that seems light. You could easily argue that it is closer to a year and a halves salary! That could easily be $100-$200K.
Financially speaking, that is a lot of money that a company could throw at this problem and feel like they were getting ahead. Unfortunately, this most often takes the form of a $10 million dollar database re-platforming project. That number is eerily consistent. I have witnessed more than two dozen in my career. Not one successful…
As an exciting and provocative benchmark, that is like tossing these Victoria Secret’s ladies in a volcano… twice! And while clearly tongue-in-cheek, I am only valuing the million dollar bras!
Again, I have never seen a $10 million dollar database re-platforming improve anything in most corporate data structures. I have witnessed a $100 million dollar fiasco that had some mild improvements and a few $2 million dollar tries that were somewhat helpful, if woefully ROI negative.
So how do you fix this?
Invest in understanding you data environment, your analytic needs, the nature of your data-driven (or data-averse) culture, and the size of the gaps that your analysts will need to bridge. Get an outside eye. Remember, most analytic teams are starved for resources. Even if that multi-million dollar data migration was guaranteed to succeed — they don’t have the time! You are only increasing their anxiety levels. And most of them already know the success rate of these projects..
If that paragraph is causing some deja vu, you likely read yesterday’s article. It is amazing how often the right answers for analytics repeat. More stunning how rarely they are employed. Once you have mapped the issues, education is going to be a truly important solution — for everyone. You need to educate the business, any IT folks not yet tossed in the crater, and the analysts. Educated consensus should lead to a meaningful data initiative with a lower price point and a positive ROI (be sure to count a decrease in voluntary turnover!).
Part 2 will end here. I don’t want you to get bored. That is a hint for part 3 — read it here.
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