There is a saying amongst the tech community that was coined by the statistician William E. Deming: “In God we trust, all others must bring data.” This was all well and good back in the 60’s, when an entire person’s life could be summarized on a few sheets of paper – however, today the average human creates roughly 500MB of data every single day. That’s roughly 1,200 emails, 3 hours of music streaming, 300 Facebook posts with photos, 25 app downloads and 30 minutes of YouTube streaming combined, per person, per day – and that’s just the self-generated data. That’s not including phone GPS data, security camera recordings or interactions with machines (Like your ATM) that collect data as well!
That’s insanity. And through this insanity, you’re expected to somehow discern what your clients and members are interested in, what will attract Millennials, how to respond to marketing initiatives, social media interactions, product and feature requests – the list just goes on.
At face value, it’s an impossible task: No human can sift through that much raw data. Luckily, your goal is not to just memorize data points, your goal is to turn the data into information, and the information into actionable intelligence.
This blog is going to show you the difference between those three categories, and how to move through each step in order to achieve some sense of direction.
Understanding Your Data
Data alone is always useless. Data is 100% raw, unorganized facts that have received no processing whatsoever. It’s the equivalent of overheard conversations at the mall, or the TV left on overnight – yes, you’re getting facts, but on their own it does nothing for you.
For example, each one of your members’ checking account balances is “data.” The quantity and size of business clients you have is “data”. Where you are geographically located is “data”. Telling me this information by itself means absolutely nothing to me, and should mean nothing to you as well, because on its own, data is always useless. We want to refine our data into Information.
Information is Data given context. If you have an IT department that can handle it, it is pushed through Hadoop, Rapidminer or Splunk – specialized programs made to crunch data into Information. Information is where 95% of all companies and people in every industry in every country on this planet stop; it’s “good enough”. It’s been analyzed and processed into a human-readable format, one can feel good that they know this “important” information. Let me give you a table to help visualize the differences:
|Does it have Meaning?||Raw, random facts, so no.||Facts refined by processing; yes.|
|What is it?||Text and numbers||Human-readable information|
|What is it based on?||Records, Observations||Analysis|
Again, at face value this seems like a good place to stop. Pulling my examples from above, after turning my data into information I know that my average member’s checking account balance is $5,000. I know no branch or ATM we own is more than 15 miles from each other. My average business client has a credit line of $55,000. That’s nice for me to know; it’s a little bite-sized popcorn of information that I can take to my management team, but you’ll notice it adds no value on its own.
What does knowing that my average member’s balance is $5,000 do for me?
How to Turn Your Data into Actional Insights
Actionable Intelligence is what most management teams ask for when they receive Information. It’s the point and purpose of internal and external efforts to understand what’s going on in your business and in your markets. Actionable Intelligence is Information with a purpose, and here’s where the big and tough questions are asked, because to get to this point you have to give even more context to your data, and it may not be context that you alone can provide.
Actionable Intelligence is Information that creates an impetus to action – basically, exactly what it says on the tin.
|Does it have Meaning?||Raw facts; no||Refined facts; yes||Yes|
|What is it?||Text and numbers||Human-readable||Strategic|
|What is it based on?||Records, Observations||Analysis||Context|
For instance, knowing that my average member’s balance is $5,000 does nothing for me unless I compare that to my market, thereby giving it more context. Let’s pretend that after further research, now I know that my competitors’ customers only hold $3,000 in their checking accounts. Bringing it together, adding more context, it seems that the relationship you need with my institution for a rewards checking account is much harder to achieve than with my competitor – now I have Actionable Intelligence.
Knowing this information, do I now need to make changes to my rewards program? If so, what does that look like? Who do I need to bring into a conversation in order to find out what’s possible with our core system – etc.
As you can see, gathering data is easy. Compiling that data into information can be done even in Excel – and I bet everyone reading this has done that at one point in their lives – but without the vigorous application of additional information and context, it’s simply a waste of everyone’s time.
When you’re asking someone to build a report for you, or if you’re curious as to what a specific metric would be, don’t leave it alone in a vacuum. Ask yourself, “What problem am I trying to solve? What am I trying to learn from this data?” You’ll be able to move your data past information and into the realm of Actionable Intelligence if you keep these frameworks in mind, and that will be the greatest asset you can bring to your management team discussions in the future.