Data can help us make better decisions, but blindly reacting to it can get us into trouble. A number may be accurate while the conclusion we draw from it is completely wrong. When we treat metrics as instructions, we risk making changes without understanding what we’re actually changing.
Say a company notices that customer support calls are taking longer and decides to push for shorter conversations. On paper, that might look like an efficiency improvement. However, experienced support staff may know that the longer calls involve complicated issues that would otherwise require several follow-ups. Cutting those conversations short could improve the metric while making the experience worse for everyone involved.
Understanding that distinction requires practical experience and human context. The people doing the work often recognize constraints, tradeoffs, and consequences that aren’t obvious in a report. Their expertise can help explain what the data means and whether a proposed response makes sense.
Strategy matters just as much. Before trying to improve individual metrics, we need to ask how doing so supports what we’re trying to accomplish. If our overall goal is to resolve customer problems, call length alone won’t tell us whether we’re succeeding.
This is where data needs to be paired with craft: the expertise developed through doing the work, understanding people’s needs, and learning what makes a solution effective. Strategy gives that work direction and helps us decide which outcomes deserve our attention.
Experience shouldn’t be an excuse to ignore data-driven evidence. But data-driven evidence doesn’t remove the need for expertise, either. Good decisions come from bringing them together and thinking carefully about what happens next.
