By Annalyn Hawkes
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 terms can cause some of these concerns and confusion we commonly hear:
- “I just don’t know where to start, there are 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 we’re hearing is an overwhelmed perspective – credit unions 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.
Think of a 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 – a consumable (in this case edible), repeatable, result.
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 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.
Where to Start
Start with some of the data decisions already made for you.
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. Rather than starting by mastering the art of baking, start with a roll of premade dough and get to consuming faster.
What do you have in your toolset that can do this for you? Don’t turn your nose at “simple” tools—start simple and mastery will come with time.
Annalyn Hawkes is Business Intelligence Analyst with Asterisk Intelligence.