Sailboats and Big Data

Sailboats and Big Data

Big data is a big deal. No shock there. It promises insight in to every aspect of business and government. “Data Scientist” is now one of the hottest career paths to be on, and without big data, IoT (the Internet of Things) would hold far less promise.

By definition, big data is big. It involves collecting information at unprecedented scale. It has driven the creation of entirely new types of databases, and powerful cloud services. We now have the ability to measure almost anything, and it’s easy to think of these measurements as the endgame.

But we must remember that cold-hard-facts mean nothing in isolation. They do not drive action, they do not influence, and they do not make us more likely to succeed or fail. None of those things can happen until some level of communication takes place. To make big data do big things, we need to recognize the relationship between information and communication, and keep the perspective of our audience in mind.

For example, I used to spend a lot of time racing a sailboat with a group of friends. The skipper (who drove the boat) and the guy trimming the mainsail (positioned next to the skipper) were legitimate data scientists. They would look at the compass, GPS, apparent wind, and dozens of other instruments and perform amazing feats of mental math. Now, I was usually on the pointy end of the boat and about as far from them as you could get. When they would yell out “The current is pushing us south at 2 knots!” I would file it under “nice to know.” They quickly realized that shouting, “We need to tack!” was a much more effective way to get things moving. The first approach gave me information, but didn’t accomplish what they wanted.

When talking about the interaction between information and communication, we need to remember that whenever an audience is involved, so are opinion, expectation, context, bias, knowledge, and everything else humans can’t help but bring to the table. With data we have impersonal facts. Add people, and you can run into problems. This is as important to remember in business as it is when racing a sailboat.

Remember our blog post on Aristotle’s three modes of persuasion (logos, pathos, and ethos)? One of the most interesting aspects of logos (logical argument), is that true logical proof is not required. Only the simulation of logical appeal is necessary to be persuasive. Even when presented with information backed by the most rigorous data science, we all have a tendency to seek affirmation rather than information. We find things that support our existing perspectives more convincing.

Hypothetical situation. Let’s say you spot an important market trend, and it’s a change for the worse. The magnitude of your “uh-oh” moment drives you to copy the charts and tables into an email and fire it off to your virtual team with little context or commentary. Later, you are surprised to find that some of your colleagues don’t see the oncoming storm, instead they see support for existing strategy. Even more shocking, a few people glossed right over your email—not having the context or skill necessary to know what to do with the information.

To avoid this, we need to ensure we establish the right channels of communication ahead of time. We need to work to understand the needs, goals, perceptions, and skills of our audiences. If we want to inspire, engage, and lead them, we must provide the right information in the right way.

We need to remind ourselves that the tables and charts that led us to our great insight are not always the right way to tell that story to others. Very often the details get in the way of telling your story (See our post on charts, the hidden trap). If you connect abstract information to real world people and problems—you’ll make the story stick (for more on that check out our post on bringing data to life).

You might point out, accurately, that big data is not just about gaining insight, it’s about gaining insight fast. Following my advice adds steps and slows things down. That’s true. As with most things, you need to find a balance. Take a second and weight the risks and likelihood of misunderstanding against the benefits of insight. If you are talking to close colleagues, less context is needed. If you are speaking to thousands of customers, spending a LOT of time is warranted.

Because at the end of the day, the more we rely on data and insights, the more we have to be aware of how we communicate.



team Zum