May 2011 Archives

In my last post, I suggested that intelligence and analytics are two angles on the same challenge: developing the information value in available data. You're probably already looking—sorry, listening—for useful information online. Rather than thinking of intelligence and analytics as separate specialties, let's approach them as two lenses that might help us find information in data.

I'm going to risk a small definition here; if I'm going to write about intelligence and analytics, it would help if I assert that these aren't two words for the same thing. Proposing a formal definition isn't my point, so let's think about it this way: We do a lot of quantitative analysis these days. We care about the results because they present trends or aggregate data points in some way. For the purposes of this discussion, that's analytics. Other times we care about individual facts, regardless of the quantitative view. That's intelligence (cue James Bond theme).

For example, you might be interested in the most popular adjectives used to describe your product or brand. You care about the results because they represent mass opinion. That's analytics. Conversely, if you discover a death caused by your product, that fact is important regardless of how many people are talking about it. That's intelligence.

Yes, it's a little messy. The point is to notice what we've been missing, not to perfect the language.

What do people say?
Let's apply this to the familiar topic of listening in social media. People say all sorts of things online, but when we start analyzing their meaningful statements, they fall into two categories: statements of fact (which may be false) and statements of opinion.

We spend a lot of time on the notion of analyzing opinions. Most of the usual metrics help us understand trends in the opinions expressed in a large collection of comments. But what about facts? What do we do about them? They don't really fit into a market research paradigm, but some of them may be important to the business. We need to use a different lens.

It must be serious; he has a matrix
In proper consultant fashion, I decided to see what happens when we put these two ideas in a matrix. We use our intelligence and analytics lenses to look at statements of fact and statements of opinion online. Remember, analytics (in this discussion, at least) is about aggregate data, while the intelligence lens can pick up isolated signals. The examples in the boxes are illustrative; I'm sure you can think of more.

Intel analytics grid

Think about the usual discussion of listening in social media. How much of it focuses on measuring customer opinion and brand image (including every discussion of the accuracy of sentiment analysis)? How much more value could we uncover if we asked more questions of the same data? Are you looking for the important signals that don't show up in a Top 10 chart?

This is another piece of the Omniscience framework I'm working on. It starts with four simple thoughts, and it all comes together eventually—I hope.

House on silosIn a finite world, individuals specialize, but organizations don't have the same limitations. Given enough specialists, you can do it all. The challenge is in managing them. Somebody has to get on top of all these silos.

In my ten-minute pretend-keynote at last year's Defrag conference, I asked people to look beyond the existing silos of data and analytics to consider what more we could do. I challenged them with this simple idea:

Analytics + Intelligence –> Strategic Value of Information

What I'm doing is applying and not or to analytics and intelligence. Applying math when that works and finding facts when that works. Around here, the starting point for data is social media, but that's another boundary that turns out to be arbitrary. The same reasoning applies to other data sources.

We use labels like intelligence and analytics to divide the analysis of social media data into closely related specialties. In the process, we risk losing sight of the bigger goal, which all of these specialties support:

Uncover the information in the available data in order to develop insights that support the business.

We're all looking for useful information in data. In the social media realm, some of the data is unstructured content, and some of it is structured data generated by our activities. That distinction is driving some segmentation among the vendors, but it's worth remembering that intelligence vs. analytics isn't an or question; it's an and question—you need to consider both.

In the next post, I'll show you the model that applies intelligence and analytics to expand what we might find in what people say online. There's more to it than the usual summary of opinions.

Photo by Pablo David Flores.

About Nathan Gilliatt

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  • Voracious learner and explorer. Analyst tracking technologies and markets in intelligence, analytics and social media. Studying complexity and futures.
  • Principal, Social Target

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