Data is invading every nook and cranny of every team, department, and company in every industry, everywhere. Developing the talent needed to take full advantage must be a high priority. Indeed, everyone must be able to contribute to improving data quality, interpreting analyses, and conducting their own experiments. It will take decades for the public education systems to churn out enough people with the needed skills — far too long for companies to wait. Fortunately, managers, aided by a senior data scientist engaged for a few hours a week can introduce five powerful “tools” that will help their teams start to use analytics to solve important business problems.
I’ve spent my career helping companies address their data and data quality opportunities. Overall, I rate progress as “slower than hoped.” While there are many contributing factors, one of the most important is the sheer lack of analytic talent, up and down the organization chart. In turn, this lack of talent makes it harder for companies to leverage their data, to take full advantage of their data scientists, and to get in front of data quality issues. Lack of talent breeds fear, exacerbating difficulties in adopting a data-driven culture. And so forth, in a vicious cycle.
Still, progress in the data space is inexorable and smart companies know they must address their talent gaps. It will take decades for the public education systems to churn out enough people with the needed skills — far too long for companies to wait. Fortunately managers, aided by a senior data scientist engaged for a few hours a week, can introduce five powerful “tools” that will help their existing teams start to use analytics more powerfully to solve important business problems. To be sure, these are not the only tools you’ll need — for example, I haven’t included A/B testing, understanding variation, or visualization here. Nor is my intent to make people experts. Rather, based on my experiences working with companies on their data strategy, these five concepts offer the biggest near-term bang for the buck.
The first is learning to think like a data scientist. We don’t speak about this often enough, but it is really hard to acquire good data, analyze it properly, follow the clues those analyses offer, explore the implications, and present results in a fair, compelling way. This is the essence of data science. You can’t read about this in a book — you simply have to experience the work to appreciate it. To give your team some hands-on practice, charge them with selecting a topic of their own interest (such as “whether meetings start on time”) and then have them complete the exercise described in this article. The first step will lead to a picture similar to the one below, and the rest of the exercise involves exploring the implications of that picture.
Charge that senior scientist you’ve engaged with helping people in completing the exercise, teaching them how to interpret some basic statistics, tables, and graphics, such as a time-series plot and Pareto chart. As they gain experience, encourage your team to apply what they’ve learned in their work everyday. Be sure to make time for people to show others what they’re learning, say by devoting fifteen minutes to the topic in each staff meeting. Most critically, lead by example — do this work yourself, present your results, and freely discuss the challenges you faced in doing the work.
As you and your team dive into data, you’ll certainly encounter quality issues, which is why pro-actively managing data quality is the next important skill to learn. Poor data is the norm — fouling operations, adding cost, and breeding mistrust in analytics. Fortunately, virtually everyone can make a positive impact here. The first step is to make a simple measurement using the Friday Afternoon Measurement method (the technique acquired this name because so many teams end up using it on Friday afternoon).
