notes

Every organization claims to be data-driven. That makes sense, considering data is the new oil: It’s the resource every enterprise needs to control in order to keep the wheels of production turning.

Indeed, data is fundamental, especially when it comes to business strategy. Way too often, however, “data-driven decision-making” just means that data operations end up driving everything. Eventually, the data itself becomes more important than the actual decision-making. You and your team get caught up in methods, processes, and trivia; the destination is obscured by a never-ending series of ones and zeros.

For all the effort, the data simply doesn’t deliver any new or compelling insights, so you lean on the same old bromides to make big decisions.

However, your organization probably has all the right people and technology to avoid such futility. It’s the processes where organizations usually falter, and managers often contribute more to the problem than the solution.

Managers are typically not deep experts when it comes to data-driven decision-making, and leading these initiatives is not a comfortable place for them to be. As a result, they tend either to get stuck in the weeds or to apply a heavy hand in all the wrong places. In either case, they try too hard to add value or overcompensate for feeling ill at ease. Managers mean well, but end up interfering with their teams’ efforts.

As the saying goes, “It is easy to get bad data, hard to get usable data, and damn hard to get good data.” Your job as a manager is to make sure your team gets enough good data to make a good decision. The good news is, you don’t have to be an analytics expert, a statistics major, or even all that comfortable with numbers to do this right.

Here are three powerful ways to be a valuable contributor as a manager of a data-driven decision-making project:

1. Be a Good Data Navigator

As a manager, you are responsible for seeing company strategy through to execution. On a data project, your No. 1 task is to map the connection between strategy and data. You and your team need to select the right data and stay focused on it. The biggest mistake people make is selecting the data they think they need first, then trying to backfit it to some objective later. Instead, start with the end in mind so that you select only the data that is tied to that end.

Here is a powerful technique the pros use to do just that:

First, articulate the ultimate statement you’d like to make. Typically, this will be phrased as meeting a strategic objective or delivering a specific value proposition. Keep it simple and avoid using numbers at this point.

Then, make a list of the evidence you would need to make that ultimate statement. What are the phenomena that need to be present to build the strongest case for making that ultimate statement? Again, avoid using numbers until the next step.

Finally, select the data that would best support the evidence. What are the measurable behaviors or activities you would need to see to persuade you the phenomena is indeed occurring? That is the data you need to stay focused on.

2. Be a Good Decision Engineer

Data is ultimately used for making business decisions, and as a manager, you are the decision engineer. You need to frame all available options accurately, set up mechanisms for evaluating what the data says, and create the decision map.

Fundamental to all of this is being able to make comparisons. As a manager, you have the line of sight necessary to determine the kinds of comparisons that will best inform your decisions — for example, behavior in different accounting periods or according to different market segments or product offerings.

Whatever the case, you always want to begin with a hypothesis — that is, your expectation of what the data will show. This forces everyone to be deliberate in their analysis and is your best safeguard against a fishing expedition. The most powerful comparisons usually come when the data points to something different from your expectations. As a manager, your focus needs to be on coming up with expectations that will yield the most information.

3. Be a Good Data Consumer

You’re the manager, which means the buck stops with you as far as quality assurance is concerned. Where the data comes from, how it gets analyzed, and what it gets used for all need to be completely transparent.

The best way to be a good data consumer is to apply a healthy amount of skepticism and encourage the same among your team members. You want to avoid at all costs a situation where your data leads to a wrong conclusion or gives you a false sense of mastery.

That said, be extremely careful about letting healthy skepticism turn into toxic pedantry. If that happens, you’ll slip into those senseless games of needing to have the last word or arguing every point for its own sake.

Instead, focus your energy on understanding the source of the data and any biases that might be inherent in the source. More importantly, be diligent in understanding the limitations and strengths of the data. Know specifically what the data can and cannot address.

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