In our last post, we noted how great it was to see this recent article in the Harvard Business Review on how Good Data Won’t Guarantee Good Decisions because investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making and only 38% of employees and 50% of senior managers, on average, are equipped to make good decisions given good data. As pointed out by the authors, there are too many “unquestioning empiricists” and “visceral decision makers” and not enough “informed skeptics” who can effectively balance judgment and analysis with strong analytic skills and a willingness to listen to others’ opinions, but dissent if necessary.
As a result, organizations need to do whatever they can to increase the number of informed skeptics within their four walls. So what can they do? According to the authors, they can:
- Train workers to increase data literacy
and more efficiently incorporate information into decision making so they can make better decisions and
- Give the workers the right tools
to turn the data into information.
With respect to training, the authors recommend workshops and coaching. Workshops can teach them that they must understand the factors and calculations behind the numbers and learn to think critically about the accuracy, sample sizes, biases, and quality of their data. Even people who took statistics in college could probably use a refresher to help them apply what they learned then to their current jobs … especially since most people, analysts included, don’t understand statistics. (Remember that there are lies, damn lies, and statistics.) Coaching by people-oriented data experts can provide informal, ongoing training to employees that can gradually improve their skills. Given that surveys indicate that only 25% of all knowledge workers receive effective training in information analysis and use, this is a good start.
With respect to tools, there is a vital need to interpret data displays in a manner that allows them to deduce the information the data contains. Just because most executives choose to go with good-enough data now vs. perfect data later doesn’t mean it’s the right thing, not because perfect data is always a useful goal (as sometimes good enough is good enough), but because, without the right tools and understanding, it’s not always clear if good enough is good enough.
But is this enough?
Three factors are always required for success: technology (tools), talent (training), and transition (change management of the process). Overlooking how the training is to be applied, the technology is to be used, how the results are going to be interpreted, and how the change from dumb data to intelligent information is going to be implemented so that it sticks, the training takes hold, the technology gets used, and the results get repeated is very important. Otherwise, a few moderate wins will be made, but as pressure mounts to get things done, the talent will revert to the old ways and the tools and training will be for nought.