Data Versus Knowledge
Our view of data often doesn't extend further than numbers. Admit it. When you think about it, data means a percentage, a total, or something to which those numbers are attached. Furthermore, we want to act on those numbers with familiar math. But that's only one kind of data—quantitative.
Red hair is a datum and so is size 12, first place, a letter grade in a course, a delivery date, time of day, and a part number, but most of those things are not amenable to mathematical operations. A well-designed database might tell you that you have a certain quantity of size 12 shoes in inventory, but that quantity might only be a calculated field that's not even stored since it can change so often.
While red hair divided by two is meaningless, it's still data, which brings up an important point. We don't act on data, we act on information, and we only act on information when it creates knowledge in our minds that enables us to make informed decisions. Some people call this customer insight, but the standard nomenclature recognizes a small hierarchy—data, information, and knowledge.
Getting all the way to knowledge is something not often discussed, having been drowned out in the long discussion about big data, but it's important. Too often in the big data discussion, we lose sight of the need to add together multiple data points to arrive at information that creates useful knowledge. That's too bad, because it leads us to think that managing data is an end goal, when the primary objective should be more like asking how we can make something valuable out of it.
Sometimes a combination of metadata or data about data can result in information, such as knowing what phone number someone called, how long he was on the line, and whether the person at the number called made several other calls immediately after the first call ended. All that metadata might describe a network of related numbers or people, for instance. That might be knowledge in the mind of a security expert.
Knowledge is a property of a human mind, so you might consider it information in motion. At any rate, knowledge is what drives people to complete deals. A person's name is data, and information might include additional data like job title and company, while knowledge is information extended by understanding the person's objectives for the year ahead.
We are sometimes haphazard in the way we interchange these terms. If we can't get these terms right, we can't correctly model the ideas we wish to pursue. If you have a program or policy for managing data and it is not delivering the results you planned for, it might not be a technology or process problem. It might simply be that you didn't think far enough from the data you thought you needed to manage to the knowledge you need to run your business.
You might have a program in which you capture data and cleanse it laboriously. That's a good first step. You might augment that data with more data to flesh out your profiles, and that might give you information about possible leads. But it's only when you further cultivate that information into knowledge that you have actionable knowledge to work on.
So where is your data program in the march to knowledge? Is it mired in the data management effort, or are you somewhere along the road to knowledge? An easy test is answering this question: What do you do with old data? Do you hang on to it or ditch it? Data that has served its purpose and rendered some amount of knowledge is as spent as the tailings from an old mine.
Denis Pombriant is the founder and managing principal of Beagle Research Group and the Bullpen Group. He is a widely published CRM analyst in the U.S. and Europe, and his latest research spans all areas of social CRM, cloud, and mobile computing. His latest book, The Subscription Economy, is available on Amazon.
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