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. 🙂