346. Great Leaders Navigate Data and Wisdom for Optimal Decision Making
Leaders must know when to how to use data and when to look beyond the numbers.
Learning how and when to leverage data for decision-making is an important business skill for leaders.
Data is a powerful tool for your business:
Data can assist in making decisions.
Data can help you spot trends early.
Data can confirm progress or proactively find issues.
Their point is that you don’t know what data you really need until you properly frame what it is you’re trying to decide. The data can only take you so far on this essential question because you are the one who has a view of the broader business landscape, including, of course, the complexity of your own business. “Don’t expect the data to provide both the questions and the answers,” the authors warn. “It is your responsibility to hone in on the essential question and then combine data with intuition to attempt to identify the answers.”
Christopher Frank, Paul Magnone, & Oded Netzer
Data can be dangerous for your business:
Data can be incomplete, out of date, or just plain wrong.
Data is backward-looking.
Data is limited in what can be captured.
Great leaders determine how and when to use data to their advantage and invest the time necessary to make it work for them.
Many of the hard problems now facing managers and companies require sophisticated techniques for analyzing vast amounts of information. It is tempting to think that, if you can just get the right information and use the right analytics, you can make the right decision. It can also be tempting to hide out from tough decisions or disguise the exercise of power by telling other people that the numbers tell the whole story and that there is no choice about what to think or do. But serious problems are usually gray. By themselves, tools and techniques won’t give you answers. You have to use your judgment and make hard choices.
Joseph L. Badaracco Jr., Managing in the Gray
Keep Learning:
This topic is a very large one to cover and with the rise of AI, will continue to grow in importance. Depending on your role, you may need to go deep to help drive the strategy. In other roles, you may need topical knowledge to leverage the output.
Either way, here are a few resources to help. Please share if you have others!
In a data-led world, intuition still matters
“Defining the problem first and then working backward toward the data can put you in some good company. The authors cite the example of Amazon. There, when people have an idea for a new product or service, they have to write up a press release and FAQs to help them and everyone else understand what it is, how it will work, and how various contingencies would be handled. That process helps all parties gain insight into what they really need to know to determine if the scheme is a good idea.”
Building a Winning Data Strategy
“Building a winning data strategy requires bold moves and new ideas. Creating a strong data foundation within the organization and putting a premium on nontechnical factors such as analytical agility and culture can help companies stay ahead.”
Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI
“Data is consumed by many. For many business executives, discussions revolving around data management may seem abstract or arcane. There is consensus that decision-makers want data that is credible, accurate, reliable, and hopefully predictive and insightful. What exactly does this mean, and how does it translate into a set of rules and processes for effectively managing data as an asset?”
This is an essential topic for where we are currently with data and technology. What's insane is the broad spectrum for various organizations and how quickly things become irrelevant or outdated as months pass, especially with something like generative AI. I was talking to a data security lawyer yesterday, and he said it's like the Wild West trying to work with some of these companies.
On the spectrum comment, our company works with community health organizations (many are local government). We're simply trying to get them to understand data strategy in the simplest terms - they're roughly ten years behind in the most extreme cases. Trying to talk about predictive analytics quickly gets us into uncharted territory, let alone trying to explain a large language model!
Thanks for the share and resources!