Finding the Signal in a Noisy World

One of the biggest challenges I have found in leading organizations and businesses today is finding the "signal" in an increasingly noisy world. In most situations, especially in the "age of information," there is usually an endless supply of data, ideas, and possible actions, but only a few of them truly matter. People are drowning in data, but starving for actional information and direction. So how do you filter the signal out of the noise as fast as possible so that you can act on it? There is no one silver bullet, but there are some techniques that can really make a difference:

1. Framing. Until you frame something, anything is possible, which is great, apart from the fact that when everything is possible, it is less like likely that something purposeful will happen in a timely manner. The key aspect to framing is to bound the problem. What are we trying to solve and accomplish? What are the key factors, what is the relevant timeline, scope, and so forth? Once you have your framing, you can use it as a 1st level filter to discard information that may be super interesting, but not relevant to the particular item you are trying to address.
2. Facts vs. opinions and assumptions. For important things, there are usually lots of views and assumptions. That is great, but the problem is that many times, these are not qualified explicitly as such, so they seep into what I call the "pseudo-factual domain"; which happens when people hear something enough and start to treat it as a fact, even though it is just an opinion. This confusion is compounded when people make assumptions based on these pseudo-facts. To avoid this, for every data that flows in, make sure you qualify it as either a fact or an assumption/opinion. These assumptions/opinions are still important, but you need to qualify them with some subjective probability and also define what additional info you need to confirm them. This simple act is incredibly helpful. I have had many situations where a team went for weeks in a direction without understanding that many of the "facts" driving them were actually assumptions which turned out to be false. Note that it is perfectly reasonable to move forward based on both facts and assumptions, (you rarely have the luxury to wait to get all the info you need; most of us live in a world where 80/20 is the way to go). Still, it is critical to explicitly qualify the facts vs. opinions and assumptions.
3. Get to the why. Information is a lot more useful (and credible), when you know what is driving it. Thus, always strive to get to a root understanding of the information. For example, you may hear "many customers do not like feature XYZ." This is interesting, but its a lot more interesting and useful to understand why they do not like the feature. Maybe they actually like it but just find it hard to use, or it is not documented well? Another benefit of focusing on the why is that it helps filter "pass-through noise." One of the challenges of our current fast paced world is that many folks just "pass through" information without actually understanding or verifying it. By the time it reaches you, it may have gone through 3+ levels, which in many cases did not verify its accuracy. By driving a culture of asking "why" on data at every level of the organization, you will likely filter much of the noise before it enters the system, thereby saving everyone time and energy chasing false inputs.
4. Qualify the source. This is interrelated to point (3), but also stands on its own. Not all data sources have the same weight, and it is important that everyone understand this, and qualifies the inputs. Many times you might hear "A customer said XYZ about the product." Not all customers are the same, and not everyone on the client's team has the same weighing. Maybe it was a small customer that you are not even targeting. Maybe it was someone who is tied to one of your competitors and just noise, or maybe it is the CIO of a key customer, and thus important. Back in the day when I was working in Military Intelligence, this was one of the most critical parts of the work. Every day there would be highly varied info flowing in, but 95% of it was just not from a trustworthy source. Ideally, in your business, you will have a much better batting average, but the rule still holds.
5. Look for patterns and outliers. After you have well-qualified data from trusted sources, you still have to figure out how to prioritize it for action. This is where pattern matching is so important. In general, most critical factors will develop and fit into a pattern. Develop a thesis based on this pattern and use it as an anchor point (albeit a flexible one) for testing new data coming in. For example, if you hear that "project x is running behind" from one person, that is a data point. However, if you hear it from 5 people in different places in the organization, now you have a pattern, which probably demands action. This anchor is also is very useful for avoiding the "last thing I heard fallacy," where the last person or customer you spoke to holds the most weight. Having said this, outliers are also important to understand. If you have a pattern where 8 of 10 data points say X, but two sources say Y (which is the polar opposite of X), you should try to dig into what is driving the Y response. Many times you will find that you can ignore it, but once in a while, it could point to "blind spot" in your thinking. Maybe it is an early adopter that is going to drive a future trend, so you need to understand it. For example, if you asked about Public Cloud 10 years ago, most people would have dismissed it, but there was an outlier of "new style" startups that loved it.
6. Understand your organization's natural biases. Biases skew both the flow and interpretation of information. This is natural and normal, but you need to be hyper-aware of it to avoid its impacts; such as the tendency to focus on data that confirms your assumptions and/or "good news." When I was at Cisco back in the day, after the bubble burst, I remember Chambers used to say: "we need to deal with the world the way it is, not the way we want it to be." Since that time, this has always rung true to me when dealing with "bad news." The key here is also walking the talk. If you tell people you want the truth, no matter how unpleasant it is, you cannot chew them out every time they bring bad news and "shoot the messenger." If you do this, you will not be getting the "Reality Channel," you need, but instead, your team will tune you to the "Good News Network."
7. Write things down. Seems obvious enough, but common sense is not always common practice. I am not talking about formal power point presentations, but if you want to successfully find the signal and be true to yourself (and your team) about the data over time, you gotta put stuff on paper. It has been proven that human memory is a very selective and imperfect instrument, so don't rely on it as your system of record.

Notes of caution: Stay flexible and plan for adjusting as you go.

1. In almost every case, speed matters and time is money, so don't turn finding the signal into a "paralysis by analysis" exercise. At the end of the day, at some point you will need to move forward based on ~80% of the info, acknowledging you will likely make adjustments as more info become available. Accept this as the mode of operation and plan for it.
2. It is hard to really be 100% sure which info will be the most important ahead of time. This means that even with all the filtering, you need to keep an open aperture and awareness for possible outliers (per point 5).