Data vs Information

Do you know the distinction between “data” and “information”? Does data feel like an abstract concept? I sometimes wonder if perhaps a lack of definition to the term can cause some of these concerns and confusion we commonly hear:

“I just don’t know where to start, there’s too many options”

“I’m too busy for data analytics or data management”

“I’m not a data analyst, I’m just the branch manager”

What are we hearing there? An overwhelmed perspective – they see the big picture but are struggling to carve it down to a starting point. We also hear a misunderstanding of the objective. In many cases it’s not about data, it’s about being informed and gaining “grasp” in your area of work.

In my opinion, a lack of definition on the relationship between data and information can paralyze progress or push people into the weeds too fast when talking about data analytics. Out in the wild, the terms data and information are often used interchangeably. But they are not really the same thing in practice:

Data = Individual facts or values without context. For example, a street number. It exists as a static number in a silo with little meaning at this point. You don’t know which member’s street number it is, or even which street it’s a number on.

Information = A collection of data, shown or used in an organized and relational way. Information is the result of processing data. For example, the mailing list of all members in zip codes within 20 miles of your branch. Now you have the entire street address, name of the resident, etc.

Fair warning, this analogy is silly but I can’t help myself. Think of a delicious cookie. Each ingredient is data, the recipe represents the rules and instructions (data governance and data mining) for processing the ingredients, and the cookie you get at the end is the information – an edible (consumable), repeatable, result.

My colleague Thomas writes about this concept in a slightly different way as he compares data to oil – oil is elemental (and valuable), and after refinement it powers our world. Check it out if you have a few extra moments – Refining Insight from Data.

It’s true – Data by itself is practically powerless. Why? Because it’s just values. This is before you introduce programs to calculate and update the data – Before an interface to display and present the data – Before generating visual charts off collections of data – Before all that, data is elemental.

Information on the other hand….is a meaningful. It is cohesive and organized. It is a solution to a problem, the answer to a question, insight into a situation, and potentially even the predictor of the future.

But where to actually start?

If you’re in the position of feeling overwhelmed about where to begin, do not start by trying to learn the most difficult tool in your toolset right away (custom Query). As attractive as it may sound to hand-craft a report, or completely customize your analysis, it’s not the place to begin.

Start with some of the data decisions already made for you.

So many options have already chosen the ingredients and have baked it per the recipe’s instructions. You just need to consume. Then maybe tweak the recipe by adding cinnamon instead.

What do you have in your toolset that can do this for you? You probably could pick just about any dashboard, but if you’d like a name…how about Relationship Analysis? Better yet, maybe login to your Analytics Booth subscription for the first time in awhile and focus on the Trends section.

Perhaps another day we can get into my take on the top 1 or 2 “can’t miss” dashboards for various roles at the credit union. One that falls into the lifesaver category for me is the Loan Risk Score Analysis. If you have a favorite report or strong opinion on any that you use, please feel free to share.

But we really could go on for awhile at this point. I’ll leave you with these for today and save the rest for later. 🙂

Share your thoughts

3 Replies to “Data vs Information”

  1. Great Post Ana- this is definitely why we have to be more than just processors. Our job is to take the INFORMATION and present it in a way that does not overwhelm, but makes their work life easier. What I’m finding, and I wonder if others are as well, is that I actually have a lot of people pumped up and ready for data and ideas for exactly what they want to analyze. Not exaggerating, I bet I could keep myself busy for the next year just helping people pull/sort/synthesize data just to help them get the information they’ve been wanting for a long time. My current debate is how to prioritize, and when to stop these specific tasks that may require a custom query, and focus on the big picture with pre-built dashboards and graphs. The conundrum is that (in my opinion) the credit union will benefit more from helping these people get the analyses that they so passionately want. That passion will help drive positive changes, as opposed to starting a high level analysis in a pre-existing query and then having to create the passion for people to realize the need for the change. I also want to help these data-passionate people realize the benefit of having a data analyst, and I think the best way to do that is to give them something tangible that they’ve been longing for, instead of starting new analysis that although I may see the benefit of, I’d have to prove the benefit of the analysis since it was not previously requested.

    1. Yes, yes, and more yes. In my opinion you absolutely have the right perspective here for long-term success – Deliver the easy wins for as many people as possible in order to prove tangible benefits and encourage their passion for information. Better and faster answers. Culturing those data-curious perspectives and delivering self-sufficiency will trickle over to more people, then a few more, and eventually the entire standard of operations and discussion is raised to a higher level across the entire credit union. Informed people are proactive rather than reactive.

      And although your work is never done in sharing and educating internally, at a point the tide will turn, trust has been earned (both of the data itself, and of it’s value), and time will open up for some of the new analysis projects that are more specialized or at a deeper level than an average individual might take on. Good luck and I love hearing about stuff like this! Let us know if we can help at any point.

  2. My mantra for years!!! Data is just noise. Until we piece it together, harmonize it, understand what it should be, do we get information. And then there’s the willpower that comes next to make sure we read the data consistently. Otherwise it all comes down to interpretation and at some point data needs to lead to facts. I like the cookie analogy!!

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