A little while back there was a fascinating article on the lack of productivity growth in the U.S. in the past 4-5 years. I’ll try to summarize the key points below (and then tell you why I think they’re important) – but the full article is very much worth the read.
Let’s start with the facts. In the last year, the total number of hours worked in the U.S. rose by 1.9%. GDP growth in the last quarter exactly matched that rate – 1.9%. So we added hours and we got an exact match in output. That might sound okay, but it means that there was zero productivity growth. We didn’t get one whit more efficient in producing stuff. Nor is this just a short term blip. In the last four years, we’ve recorded .4% annual growth in productivity. That’s not very good. Take a look at the chart above (from the New York Times article and originally from the Labor Department) – it looks bad. We’re in late ‘70s and early ‘80s territory. Those weren’t good years.
The Times article advances three theories about why productivity growth has been so tepid. They classify them as the “Depressing” theory, the “Neutral” theory and the “Happy” theory. Here’s a quick description of each.
The trend is real and will be sustained. Capex is down. The digital revolution is largely complete. People aren’t getting significantly more productive and the people returning to the work-force post-recession are the least productive segment of our workforce. On this view, we’re not getting richer anytime soon.
There’s a lot of imprecision in measuring productivity. With fundamental changes in the economy it may be that the imprecision is increasing – and we’re undercounting true productivity. As measurement professionals, we all know this one needs to be reckoned with.
We’re in an “investment” period where companies are hiring and investing – resulting in a period of lower-productivity before that investment begins to show returns and productivity accelerates. Interestingly, this story played out in the late ‘90s when productivity slowed and then accelerated sharply in the 2000s.
Which theory is right? The Times article doesn’t really draw any firm conclusions – and that’s probably reasonable. When it comes to macro-economic trends, the answers are rarely simple and obvious. From my perspective, though, this lack of productivity is troubling. We live in a profession (analytics) that’s supposed to be the next great driver or productivity. Computers, internet, now analytics. We’re on the hook for the next great advance in productivity. From a macro-economic perspective, no one’s thinking about analytics. But out here in the field, analytics is THE thing companies are investing in to drive productivity.
And the bad news? We’re clearly not delivering.
Now I don’t take it as all bad news. There’s a pretty good chance that the Happy theory is dead-on. Analytics is a difficult transformation and one that many companies struggle with. And while they’re struggling with big data systems and advanced analytics, you have a lot of money getting poured into rather unproductive holes. Word processing was almost certainly more immediately productive than analytics (anybody out there remember Wang?) – but every sea change in how we do things is going to take time, effort and money. Analytics takes more than most.
Here’s the flip side, though. It’s easy to see how all that investment in analytics might turn out to be as unproductive as building nuclear missiles and parking them into the ground. If they were ever used, those missiles would produce a pretty big bang for the buck. In the case of ICBM’s, we’re all happiest when they don’t get used. That’s not what we hope for from analytics.
Of course, I’ve been doing this extended series on the challenges of digital transformation – most of which revolves around why we aren’t more productive with analytics. Those challenges are not, in my opinion, the exception. They’re the rule. The vast majority of enterprises aren’t doing analytics well and aren’t boosting their productivity with it. That doesn’t mean I don’t believe in the power of analytics to drive real productivity. I do. But before those productivity gains start to appear, we have to do better.
Doing better isn’t about one single thing. Heaven knows it’s not just about having the newest technologies. We have those aplenty. It’s about finding highly repeatable methods in analytics so that we can drive improvement without rock-stars. It’s very much about re-thinking the way the organization is setup so that analytics is embedded and operationalized. It’s even more about finding ways to re-tool our thinking so that agile concepts and controlled experimentation are everywhere.
Most companies still need a blueprint for how to turn analytics into increased productivity. That’s what this series on digital transformation is all about.
If you haven’t yet had the opportunity to spin through my 20min presentation on transforming the organization with analytics – check it out.
After all, productivity is our business.