turning data into information

When Life Gives You Data, Make Information

For starters, yes the title is a terrible play on “when life gives you lemons, make lemonade”.

Bad jokes aside, I hear too frequently how organizations need more and more data. I’m a data guy – I’m all about data. But there is a subtle difference between having more data and more information.

Below is one of my favorite quotes about data:

Without data, you’re just another person with an opinion. – W. Edwards Deming

Opinions are the foundation of subjectivity, and subjectivity fundamentally is devoid of data as support. Decisions driven by opinions without data is counter to everything The Knowlton Group (and I personally) stand for. Our primary mission is to enable every organization to become data-driven.

But as much as I like Deming’s quote, I also love this quote from a 2015 Forbes article:

“Without an opinion, you’re just another person with data” – Milo Jones and Philippe Silberzahn

The converse of Deming’s quote is equally accurate. Having data without an opinion or interpretation of that data is as bad as forming opinions without any data to back you up. With all of this data out there, it is foolish to believe that data will tell us what we need to do. All data requires interpretation and opinions to be formed before it can be practically applied.

The Proper Way to Use Data

Though this section is a gross oversimplification, it boils down the proper way to use data into a few simple steps. Following this process will enable you to turn data into information.

1. Ask a Question

Every actionable use of data must start with a question. These questions can be relatively simple like “how many members do we have” – unless there is no consistent definition for a member! .

The questions you ask can also be more complicated like “do we need a new branch?”. Regardless of what the question is, properly leveraging data and analytics requires asking a question to which you hope to discover an answer.

2. Form a Hypothesis

Like the null hypothesis in statistics, I strongly believe that you must start with a hypothesis. This is where your opinion and subjectivity can come into play. Use this hypothesis as a method to which you test your analysis against. Be sure that confirmation bias doesn’t play into your analysis. If your hypothesis turns out to be incorrect, who cares!?.

“I have not failed. I’ve just found 10,000 ways that won’t work” – Thomas Edison

3. Test your Hypothesis

With a question asked and a hypothesis formed, now you can begin to discover an answer to your question and test your hypothesis. This is where we can start to dig into the data (and the fun really begins). Simpler questions may require you to gather data from a single source. More complicated questions can require analyzing data from multiple data sources. For the more complicated questions, a data warehouse really starts to prove its worth!

Avoid gathering more data than you need and overcomplicating the process. Paralysis by analysis is a real thing. Gather and analyze only the data you need to test your hypothesis. Here are some of my favorite quotes/sayings on avoiding unnecessary complexity to motivate you:

“KISS – Keep it Simple Stupid” – Kelly Johnson, Late Lead Engineer for Lockheed Skunk Works

“Simplicity is the ultimate sophistication” – Leonardo Da Vinci

“Make simple tasks simple!” – Bjarne Stroustrup

4. Analyze Findings and Provide an Explanation

This is one of the most overlooked steps of the process. Asking the right question of your data is crucial, but you must provide an explanation or recommendation based on your analysis. Too often I see fancy documents put together with many pages and loads of charts and spreadsheets. In these instances, you’ve written a lot but said little.

Be concise, be clear, and provide an answer (for simple business questions) or a recommendation (for more complicated business questions).

Wrapping Up

Properly using data and analytics is becoming invaluable and, ultimately, necessary for most organizations. To begin developing a data-driven culture, have your staff read this article to learn how to properly turn data into information.

There is A LOT of data out there. The most successful companies (and people) know how to turn that data into information.


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Posted in Banks, Credit Unions, DW/BI.

3 Comments

  1. Pingback: Are You Tracking the Right KPIs? - The Knowlton Group

  2. Pingback: A:360 Podcast #7 - How Can You Turn Data Into Information?

  3. Pingback: A:360 Podcast #17 - Starting with a Question - The Knowlton Group

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