Big Data
This phrase gets tossed around a lot today. Big data, at its core, describes large sets of data. What we hope to glean from this data is based on a simple idea, the more we know about a problem, the more likely we are to solve problems. This ultimately ends up looking like a great deal of complex analytics looking to better predict things, be it healthcare outcomes, purchasing habits, or even crime prevention. And while concerns exist about big data, those essentially revolve around concerns for privacy, discrimination, and security. But there is one more thing we should be concerned about.
I recently finished reading an interesting book called Sensemaking. This book’s tagline is “The Power of the Humanities in the Age of the Algorithm” so clearly, this book is not going to be the largest fan of big data. Instead, this book looks at the idea of thick data.
Just as we discussed there are a variety of leadership styles that can work in various situations, there are different kinds of reasoning and they each have their own place. Deductive or algorithmic thinking is a way of thinking that is very logical, much like a math problem. The steps to getting a solution are clear and well defined. Any logical thinker can see the problem, approach it in this clear manner and come up with the same solution.
Currently, we are trying to solve many great problems using big data but using only big data is like forcing us to only use algorithmic thinking. Deductive reasoning is far more complex and takes factors into account that algorithms struggle with. Factors include how things feel, sound, and smell, how something makes you emote, and how you react to things.
Algorithmic thinking constrains humans in thinking and reacting in the most logical ways but anybody who has ever been around humans knows that we are not always logical.
Sensemaking promotes the use of thick data. This is the data that gives us greater depth of insights. Where big data relies on machine learning and results in a loss of resolution, thick data relies more on human learning but is less scaleable. Thick data helps us see some of the social contexts between data points.
Thick data, however, is something I find that has its place. Using only thick data runs into similar issues that only using Big data. This is where I feel like the book struggles a bit in that it does point out the benefits of thick data but it doesn’t point out benefits of big data, only pointing out the negatives. Further, the book essentially says that you need to use thick data not so much how to do this.
I found that thick data is something I feel like it needs to be done in balance. I have had many managers who focus simply on the data. If you only do that, you can lose the forest for the trees. Data can help tell the story but the addition of deductive thinking can help give context to what is going on. If your team performance is improving, it is important to know why. What makes the machine work? Knowing this will help you diagnose problems when they come up in the future.
As a leader, it is important to remember that what matters to your customer/clients is what should matter to you. Disney’s resorts around the world are so successful because they are able to make people feel amazing things about their vacations.
What can you do to take advantage of thick data as a leader? How can big data and thick data complement` one another in your business? Let me know in the comments below!
Brandon's Pursuit of Better Leadership 