Author Topic: Data, Information, and Knowledge  (Read 313 times)

Offline doha

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Data, Information, and Knowledge
« on: September 17, 2014, 09:01:15 AM »
Data, Information, and Knowledge

by Denis Pombriant of Beagle Research Group. Denis is a top CRM market researcher who advises end users in CRM selection, deployment and use while also publishing a steady stream of analysis on many of the industry’s most popular topics and emerging trends. ( InsideView | Customer Success, Data, Marketing, Sales 2.0, Sales Data, Sales Intelligence, Social CRM)

Augmenting what we know with curated data produces new insights


There is still a lot of confusion in the market about data, information, and knowledge. Too often we use the terms interchangeably and while that might sound like a small issue, these words frame our mindsets and color our understanding of the marketing and sales process.

This trio is really a hierarchy of increasingly refined concepts and along with the refinement comes real power to affect sales situations. Data is the lowest level because it lacks context. While it is important, and the whole Big Data industry swoons over it, data without context is almost meaningless. In marketing, the name of the game is turning data into actionable knowledge.

First off, context turns data into information. So while a phone number is just a phone number, knowing another bit of data, like who owns the number, turns the common phone number into a key piece of information about a customer.
In the marketing and sales process we are always trying to capture context, to add data to data or data to information to build up what we know about prospects so that we can figure out how to spend our precious time and resources. And knowing is a far from arbitrary word because it represents the level of understanding that decision-makers routinely need to go forward with confidence.

It wasn’t always that way. Before the Internet and Big Data revolution, having actionable knowledge in business was relatively rare. You could develop knowledge long after it would have been useful. In the moment, the best we had was a handful of information and gut instinct so decisions were made by HIPPOs. Ever hear of a HIPPO? It stands for the Highest Paid Person’s Opinion. Unfortunately, opinions can often be wrong but too often that’s all there was. And HIPPOs? Lacking credible knowledge, we often deferred to the highest paid person who also had the most grey hair, a marker for experience, and that is what often substituted for knowledge.

Today, it’s all different or at least it can be. Big Data and analytics deliver information by the boatload but that has its problems too. Analytics is so efficient that anyone with a computer and a bit of software can sift the Internet stream and come up with more or less the same information. That means going into a sales process, every rep is more or less equal and, ironically, we might all still be searching for that bit of difference making knowledge.

Knowledge is available but it is also in the mind of the beholder. For example, a sales person armed with a base level of information about a prospect plus a fact as benign as a company’s quarterly filings or the CEO’s statement of direction might be able to combine all of it with his or her specific product knowledge to discover an opportunity. Interestingly, that knowledge might only be in the mind of the sales person so it actually rises to the level of intellectual property. It can be as valuable as your product plans, patents, processes, and procedures.

This kind of knowledge is what the most competitive businesses use today to outfox their competition. But notice that the information that drives knowledge is only partly represented by numerical data that you can process through an analytics engine. The contextual information like the CEO’s statement of direction is not quantifiable and a person, not an algorithm, has to unpack it to derive meaning.

All this suggests that we need to expand our data analysis options from just dealing with quantitative data to including the qualitative data that, so far, only humans are good at deciphering. Combining the two provides greater insight than either one alone and that’s how information turns into knowledge.