Data <> Information
For Many Companies This Is More Than A Symbolic Failure
The world over, corporations (in general) and executives (specifically) are repeating a tragic (analytically speaking) failure. They mistake data for information.
“Our company has all this data…”
Yes, good for you. News flash — so do all of your competitors and their data is just as big as yours. Now settle down, the size and volume of your data doesn’t really matter all that much.
What you need is information.
Great companies have more information available than their competitors. More information does matter, but data is NOT equal to information. It requires transformation. It requires a connection.
Many people mistake data for information. It is an easy mistake. Information is composed of data. But information requires something more, it requires structure. The word itself comes from the Latin — into form. For your data to matter, it needs structure, it needs shape, it needs to be organized. Without that, your data isn’t impressive — it is just a big mess.
What does structure mean?
There are three keys to structure that we can touch on here.
- Connection captures context
- Organization drives insight
- Identity & Definition create value
These are important elements for your business. They must reflect it. Too often companies borrow their structure from their software. Their data structures are cookie cutter versions of their off-the-shelf software solutions or mirror the structures taught in Database 101. The latter may be perfect for the production warehouse — but not for creating analytic insights.
Let’s consider an example. Software companies often sell their product via a free trial process. But often, their data warehouse reflects a trial as if it were a singular downloading event. Does that actually reflect the business or the customer experience? A trial should be a series of events over a window of 30–60 days (depending on the offer). A simple table with a timestamp, channel, and email address only reflects the log file it was generated from.
Next up — I described this as a customer experience, but how should this business define customer? Does a free trial count? Perhaps if the company collects advertising revenue it should? But if the trial is truly free, are they a customer before they actually buy? The answer needs to reflect the business, but often it reflects a default label in a software package.
Finally, all data needs to be aggregated. While both personalization and broad portfolio metrics have there place, most value comes from a thoughtful segmentation. Data should be aggregated by channel, by population, by product, and by a variety of other means that, once again, reflect the business. These divisions won’t natural flow from your software and other platforms. They also weren’t likely a major part of Database 101.
Connection, definition, and organization will transform your data into needed and valuable information. Having the most information is a huge advantage, if and only if it is well structured. Is your organization data-rich or information-rich? Who is responsible for creating the structure? Perhaps it is time to invest in one, or just a better one?
Thanks for reading! For more on creating true value from your data consider:
