Lessons in Analytics from MacGyver

Or 4 Mistakes That Will Kill Any Reboot

Full disclosure — I loved the original MacGyver series that aired in the late 80’s. I had the hair — gone now. I had the Swiss Army knife — still do despite having to surrender several to overzealous airport and government security. And I appreciated the level of cool that Richard Dean Anderson (and Henry Winkler) created in a chemist / trouble shooter.

So when MacGyver rebooted, I had to give it a chance. And for those first few minutes, it almost had me. It was part nostalgia. It was partially that great early quote about “I hack everything else”. It was part noble optimism and part naivety. It all faded quickly. Worse yet, I realized that I had seen it all before.

Old MacGyver — The Analyst

The old MacGyver was titled a trouble shooter. He was an innovator, a problem solver, and a detective. He synthesized solutions, modeled problems, and did improvised testing and experimentation. He was effectively an analyst. An analyst more skilled in ad hoc and forensic style analysis for sure. One who was hard to replicate but great to have when the chips were down. In many ways, he paralleled the analytic groups found in start-ups and young companies today.

The Reboot — Reinvestment Gone Wrong

CBS, like many corporations, recently found itself in possession of the Old MacGyver franchise. Acquisitions have a habit of turning up interesting assets that feel a bit under-leveraged. They immediately recognized its value and sought to leverage and enhance it. So far, so good. They promptly made every rookie mistake in the book for rebooting a winning analytic concept.

Mistake #1 — Casting Immature Talent

This is happening all over silicon valley so why not Hollywood, too? The old guard has departed in frustration, so identify a talented young 20-something and dump them in the lead role. Lucas Till is just not experienced enough for the job. MacGyver drew on experience and Richard Dean Anderson looked just old enough to have some. Credibility is essential. So is energy and charisma — just don’t trade one for the other.

Mistake #2 — Emphasizing the Obvious and Generic

Every analytic revitalization seems to start with the same boiler plate nonsense. The company is going to make better use of data… blah blah blah create value… blah blah blah technology… you’ve heard it, too. Why do they feel it is necessary to tell everyone what they already know? Why does new MacGyver feel the need to label Electronics!?! If you haven’t seen the reboot, they are in love with infographic style labels. A few may be helpful — say learning the black powder is soot. Far more are just silly — of course they are electronics… maybe you could tell us what kind?

Mistake #3 — Making It Sound So Easy

This mistake always comes from executives who have no experience in their space. I can hear the executive writers of the pilot episode now.

Question — guys, how is he going to find the bad guys again? Response — he’ll just use a computer and some cameras.

On the show, we watch the ‘senior analyst’ type on a computer for 5 seconds and mysteriously locate a bad guy who could have been anywhere in the Pacific Timezone. Better yet, they are able to look over his shoulder for several minutes to learn everything they need to know. TV executives just force their nonsense into the story. The actors play along. Only the audience says “that wouldn’t work”. New analytic leaders occasionally get their analytic teams to play along — but their inane ideas are ultimately no more successful.

At the core of this problem is inexperience. These writers don’t know computers half as well as their audience does. Worse still, they themselves seem blissfully unaware. News flash — someone on that production set knew better. Why aren’t you soliciting feedback rather than issuing naive edicts? At a minimum — get a consultant. Maybe Anderson could use some extra work? Though you should be sure it is one with relevant experience.

Mistake #4 — It Has To Sound Bigger And More Important

This article is quickly getting bigger — so let’s close with one final gaff. Inevitably the new guys want to sound more important than the old ones. It is human nature. People really loved the Old MacGyver. The new guys start jealous and thus proclaim their grand plans (and often grand budgets). You have heard this in analytics, too.

Big is sexy. It is easy to sell. Of course, the bigger they are — the harder they fall. Try selling more effective… more efficient… more insightful? I suppose that never works either. Only when Henry Winkler and his colleagues pitched MacGyver in the 80’s — were toothpicks, duct tape, and Swiss Army knives considered big? Why not copy the approach as well as the theme?

In episode one of the reboot — MacGyver saves the world from a bio-weapon. I hope by the end of season one — he can save his series from cancellation. It is a little late for CBS to avoid these mistakes, but if you are looking to reboot your analytics program — there is hopefully still time.