One of the first things introduced to us at The Data School was a simple but important idea: communication is key to a successful experience with a client. I thought I got it. The research exercise we did last week showed me I didn't.
We did a research exercise in pairs: ten minutes to look up a company and come back ready to talk about how we, as consultants, could help them. Two things caught my attention right away. They had a double digit gap in their customer satisfaction rate, and they offered 24/7 customer service. That got me thinking about AI, and whether leaning on it for round the clock service was closing that gap or widening it. I thought I had something.
The feedback was blunt: trying to close the satisfaction gap would be a waste of the client's time. The numbers were already strong enough that chasing the remaining gap would cost more than it was worth. The number was real. The problem wasn't. And that is exactly what the five whys are designed to catch.
The five whys was another idea introduced to us at The Data School last week. The principle is simple: keep asking why until you get past the surface and reach something actionable. Good research follows the same logic. I only looked at the company's website. In essence, I only asked the first why. I stopped too early. I saw a gap, assumed it was a problem worth solving, and skipped the step where I check whether the business actually cares about closing it. Annual reports, news, job postings, even their LinkedIn presence, these are just some of the ways a business tells you what it is actually prioritizing. I had thought of communication as something that happens in a room with a client. But research is communication too, albeit in a less direct manner.
What stuck with me is that a gap in the data is not automatically a gap worth closing. Whether it matters depends on where the business already is, what it costs to move the needle, and whether leadership actually sees it as a priority. I skipped all of that.
I am still getting used to being wrong out loud. But I would rather take that hit now than carry the habit into a real engagement.
What makes it easier is the people around me. Only a few days in and I can already tell this cohort is something special. They are curious, supportive, and genuinely excited to be here. It does not feel competitive. It feels collaborative, and in truth, the data school as a whole feels the same way. It is truly an amazing opportunity to have such an incredible community to rely on, and having people to learn and grow with makes it a lot easier to keep pushing.
Looking forward to the next four months of training!