For those of us that have been involved in studying business processes for a long time, we’ve seen the pendulum swing back and forth between abstract data driven decision making to a humanistic approach where opinions are valued, people are validated, and warm fuzzy hugs matter.
Study after study shows each of these as successful. Or a failure. Really depending only on which one you’re attempting at the moment. Why the confusion? The study authors (or book sellers) will tell you about tremendous numbers of variables or the changing culture of the millennials. Things that are accepted as truth and very difficult to disprove.
My experience as a business leader and data analyst tells me that they’re both right — and both wrong. Data matters. It matters very much -but ONLY when it is related to people.
Let’s look at an example. Churchgoers. Depending on your choice of denomination you might argue the warm hugs/cold data side of the equation, but still a very important data element tracked by churches is attendance. Another is weekly donations. Most churches have a sign visible and are proud showing these numbers.
You might try to draw a correlation between the number of attendees and the donations. For short periods of time, there will probably be a correlation. Taken as a whole, it is NOT a good indication of the health of the church. It doesn’t address the people. The data alone is not enough. Which people attend matters. How they FEEL about attending matters. For churchgoers in the industrialized world a significant increase in their individual donation would have a negligible impact on their personal finances. The National Center for Charitable Statistics shows that for people earning under $200,000 per year, they give from 2.6% to 4.0% of their income . . . in descending order. That is — the higher earners give less. Why does this matter to us? It shows room for growth in giving. Another interesting item noted by the The Center on Philanthropy (COP) at Indiana University was that for about half the population, giving increased around the holidays. So when people have more of their own expenses — they also give more. Financial pressure clearly not the root cause. Finding the sweet spot is a combination of people and data. Why don’t people give more?
Anecdotal data has been given a bad name by analysts. I think it is key to good judgement. Taking the church example — I could track and trend every possible item like weather, sermon topic, regional economy, and try to correlate to attendance and donations. Not a great use of time. A better approach is to look for the variations in the data that you do track then apply it to people by talking to them. Be direct. Attendance was down for two weeks — start calling people and ask how they felt about the sermon three weeks ago. Use the data to ask the people. This does multiple positive things: it gains engagement in a solution – lets them know you care; it is much cheaper than additional data gathering; and it creates a spotlight effect that causes some improvement WITHOUT additional action.
Most of us don’t run churches, which makes this a great abstraction. Now let’s apply it to our own businesses. Say you run a call center and average time per call has increased (usually not a positive trend). Option 1: berate employees. Option 2: Gather and analyze multiple variables and look for correlation/causation — extremely difficult with any degree of reliability as I’ll discuss another time. Option 3: Engage key team members (not managers) and ask them for input on causes and solutions. No process mapping activities or death by PowerPoint justification for existence — but direct questions of people.
The same approach can apply to your customers. Sales are down. You have some data as a starting point. Ask customers. Don’t try to be cagey or shrewd. Direct, honest questions with a sincere interest will get you the additional data that you need to make good decisions about people.
Cold data should only be a pointer — a place to start when deciding what questions to ask. People are the key to a complete understanding.
Data is the beginning of a solution, not the end.