The Net-Savvy Executive

Talking Social Media Analytics at the Summit

2012-02-01T02:33:34Z

I like blogs for developing and sharing ideas, but if you really want to see progress, you need to spend time with people, face to face. Notice which problems get them animated, and which topics bore them. Look in their eyes to see which ideas are working and which are not. Considering the unresolved questions of measurement and analytics in social media, spending some time together sounds like a great idea.

That's why I'm excited to be a part of the Social Media Analytics Summit, taking place April 17–18 in San Francisco. As conference chair, I get to present a couple of sessions, moderate a couple of panels, and generally stay in the middle of things throughout the event. Offstage, I plan to spend a lot of time listening to what people are doing and seeing how they respond to the other ideas in the room.

I also plan to have a very pleasant time with the people who would go to a conference dedicated to social media analytics. These conferences with very specific topics are always good for meeting interesting people. And, you know, business opportunities have been known to emerge in these gatherings, too.

The program includes some very sharp folks (I would know, I invited some of them), talking about the burning questions, effective strategies, and practical applications of social media analytics. It's a safe bet that everyone will learn something from this group, starting with the pre-summit interview series. As always, the conversations after the sessions will probably be even better.

Psst. You want a discount?
If you read this blog—and I think you do—the Social Media Analytics Summit is worth a look. This isn't social media in the context of a larger conference; it's all ours. If you decide to attend, use the discount code NATHAN300 to save $300 on your registration. Super Early Bird pricing is good until February 17, so you have a couple of weeks to think about it before the price goes up.

See you in San Francisco.

Photo by Abhishek Chhetri.

Why You Can't Measure Influence

2012-01-10T14:30:20Z

I think I've figured out the source of the difficulty—and controversy—in some of the measurement discussions around social media. It all starts when we talk about measuring things that can't really be measured, because they can't be observed. If we called it what it is—modeling—we'd see that differences in opinion are unavoidable.

Take influence. As a concept, it's not all that hard to define, and I don't think there's a lot of disagreement on what it means. But have you ever seen a unit of influence?

What did it look like? A lot like persuasion? What does that look like?

How about reputation? Have you seen a good one lately?

How about engagement? That's all about attention, and interest, and emotion, and focus, and—well, nothing that you can actually see, even with the best instruments.

Measurement requires observation
We don't argue about the definitions of all online metrics. Many of the basics—page views, unique visitors, even hits—have precise definitions, so the discussion moved on to their relevance and the reliability of available data. The shared characteristic is that they're based on observable events. A web browser requests a set of files from a server, and the computers exchange information that can be tracked.

In survey research, the survey itself provides an observable moment. You might question the validity of the questions or sample, and interpretation is open to—um—interpretation, but you do math on people's responses.

We have discrete events in social media, too. People connect to each other on social networks, they like, tag, or share things, and they publish their opinions. These are all actions that can be observed, though what they mean can be the start of a heated discussion. The frequently misleading labels can confuse the interpretation of the data, but the starting point is a set of observations.

Enter the model
With influence, reputation, and engagement, we're dealing with the abstract. None is particularly hard to define, but none can be observed directly. When you can't measure directly what you need, you look for something you can measure that relates to it somehow. You need proxy data, and that's where disagreement begins. What's the right proxy?

Models can be simple or complex, but they all have this in common: each represents the modeler's estimate of how measured characteristics relate to the desired property. Models are abstractions—equations that use measurements to derive values for characteristics which can't be observed or measured.

A model might be based on someone's intuition or extensive research, it may be strong or weak. But here's something else they have in common: the model is not the thing.

The map is not the territory.
—Alfred Korzybski

The reason we don't have standard metrics for such desirable commodities as influence, engagement, and reputation is simple. We can standardize measurement, because we define what is being observed. Modeling defies standardization because it seeks to measure that which cannot be observed, and in the process of defining a model, we incorporate elements that do not apply to every situation.

Modeling for a reason
Models reflect the opinion of the modeler and the objectives they support. Because apparently simple concepts might be used for different purposes by different specialists, we end up with diverse models using the same labels. In essence, we talk about the labels, because they represent familiar ideas (influence, et al), but the models represent what we really care about (such as positive word of mouth, leads, and sales).

If you understand that the label is just a convenient shorthand for a model that takes too many words to describe in conversation, it's not a problem. If the model generates useful information, it's doing its job. Just don't assume that any one usage of the label is the correct usage. Modeling requires judgment, interpretation, and prioritization in context, which are incompatible with standardization.

Photo by gilhooly studio.

Revisiting 2011

2012-01-03T16:30:22Z

When I started looking at the year's most-read posts a couple of years ago, I noticed that the list always includes a lot of older posts. So, I started a new post last year: a list of the past year's posts that didn't make the 2011 Top 10, but that I think are worth another look.

Like Means What People Think It Means and The Rise of the "Influence" Peddlers
The labels attached to the metrics we get from social networks are a big part of the challenge of interpreting the data. Do you "like" your "friends" on Facebook, or are those just arbitrary labels for something else? I'll have more on this topic soon.
Top-Level Numbers Are Candy
I've always thought that one of the analyst's most important characteristics is curiosity. Being satisfied with the top-level metrics your tool (any tool) spits out isn't sufficient.
Twitter Best in Events and Emergencies
Emergency management pros started paying serious attention to social media in 2011—tsunamis, tornadoes, and popular uprisings command attention, after all. While they sort out how to add social media to their plans (hint: there's more to it than communicating with the public), regular people are already making effective use of the new channels.
Four Simple Thoughts, What If Intelligence and Analytics Are the Same Thing, Applying Intelligence and Analytics to Online Statements and Driving Intelligence and Analytics with Omniscience
2011 was the year that I started telling people about my Omniscience Framework, a model for identifying information needs and driving requirements into analytics and intelligence systems. This goes well beyond social media and customer interactions, and I plan to explore its implications with clients in 2012. I know of at least one company who's already thinking about products based on one of those posts.

Previous years' lists
2010: Top 10 posts, Thinking through 2010
2009: Top 10 posts

The Rise of the "Influence" Peddlers

2011-11-29T00:48:58Z

As the measurement clubs start to work out their competing standardization efforts for measuring social media, the battle to define influence is flaring up in all the usual places. And while I won't attempt to settle the debate over how to measure influence, I want to point out that the topic is more interesting than whether Klout scores mean anything. A growing group of companies is experimenting with different approaches. Influence, apparently, is the new gold rush.

At Defrag this year, I saw several new companies with new variations on analyzing influence and profiling people. One startup founder described an entirely new—and promising—approach that he's about to take into alpha testing. To his credit, he preferred that I not use the influence buzzword to describe his business.

We call it influence, because that's what it's not

Dance like no one's watching. Sing like no one's listening. Tweet like no algorithm is coldly deciding your social worth.
—Chris Sacca (@sacca)

I'm not comfortable with the influence label, because it's not really what anyone measures. Influence—the real thing, not the black-box metric—isn't hard to define, but it's practically impossible to measure. So everyone uses proxy data, and the proxies vary by company.

A few years ago, I heard Barak Libai speak about the use of agent-based modeling to calculate the value of word of mouth, and I suspect that influence is essentially the same question. But I haven't heard anybody going down that path in the commercial market. It's probably too hard for practical use. Instead, everyone uses some combination of network connections, topic analysis, and audience reaction, which—obviously—equals influence when combined with pixie dust in the correct proportions.

As I started this post, I reached the chapter on influence in Duncan Watts's recent book, Everything Is Obvious: *Once You Know the Answer, and he fairly demolishes the whole idea of measuring influence. In all but the most trivial, contrived scenario, influence is just too complex. It seems the influence controversy isn't limited to the social media discussion. Even in the sociology lab, they use proxies.

If people want "influence," let's sell it to them
If we dial back the expectation that metrics represent precisely what the label says, we might find some use in the growing crop of "influence" tools. We have a selection of single-purpose tools, of course, but it's also common for these companies to provide hooks to connect into other programs. They provide a filter for finding people who have more followers, or whose words seem to lead to more action online, and so one or more of the influence proxies frequently shows up in social media tools.

Here's what I've seen so far. Where available, I've linked to useful information about APIs, FAQs, and how the scores are generated for each company. As always, once you start looking for more companies, you find that they're different in interesting ways.

Appinions
Find and profile influencers relevant to topics defined by Boolean queries. Uses text analytics to understand statements by, and about, influencers and specific topics. (api, faq)
Connect.Me (beta)
A reputation-scoring system based on individuals recommending each other. Tags link recommendations to specific topics. Connect.Me promises not to mine or sell user data, so it's not an option for developers looking for influence scores.
Identified
A career-oriented marketability score based on how well Facebook profiles match what employers search for on social network sites. (how)
Klout
A single-score influence metric based on social network activity. "The standard for influence," at least in the sense that it's the one everyone's arguing about. (api, faq, how)
Kred (beta)
PeopleBrowsr's single-metric scoring system based on online influence and outreach. (api, how, intro)
PeekYou
A search engine for people with a single-score influence metric based on online activity. (api, faq, how)
PeerIndex
Influence analysis with scores broken out by topic and activity, audience, and authority subscores. (api, faq, how)
PROskore
Business-oriented reputation and experience score based on social network activity, career profiles entered on the site, and on-site engagement. (faq, how)
Spot Influence
Contextual influencer identification and analysis based on reach, topicality, and impact. (api, faq, how)
Traackr
Influencer search and profiling based on reach, resonance, and relevance. Traackr can also monitor and measure online activity by influencers for campaign management.

In addition to the specialists, influencer analysis and profiles are a common feature in social media analysis platforms. Have you seen my directory of companies in that business?

Lack of a standard never stopped companies from selling their stuff. If we're going to argue about the value of "influence," let's at least consider more of the options.

More posts in the "Build or Buy?" series:

Exactly

2011-11-14T21:29:39Z

My wife is on a mailing list for daily quotes, which are sometimes almost suitable for framing. I particularly enjoyed this combination:

The only really valuable thing is intuition.
—Albert Einstein

Never use intuition.
—Omar Bradley

Apply a little conversational algebra, and we end up with this:

Never use the only really valuable thing.
—Albert Bradley (or was it Omar Einstein?)

That explains so much.

Sharing My Database of Listening Vendors

2011-11-03T14:34:56Z

In late 2006, I decided it would be interesting to find every company in the world that offered social media listening tools or services. I thought I'd find a few dozen companies. Five years on, I've found hundreds of companies, and I'm still finding more. Today, I'm sharing the database that I've been using to track them: Companies in Social Media Analysis.

The directory is a bit more than a list. Each company gets its own page, which includes:

A link to the company's main website
A link to their Twitter account
The location of the company's main office
A description of the company and its social media analysis products and services, provided by the company itself
Links to recent news items on Social Media Analysis that mention the company
A Twitter widget showing the company's most recent tweets

Not every company has responded to my request for a description, but I expect more to figure it out soon. :-)

You could browse the list of companies, but it would take you a while—the directory is launching with 292 entries. Instead, I recommend the search function, which has a few tricks up its sleeve. In addition to searching the company descriptions, it can find keywords that don't show up on the company pages, such as states and provinces (spelled out), names that have changed, and companies that have been acquired.

But wait, there's more
Building the directory gave me the push I needed to redesign SMA. It still has the industry news it's always had, and I've made it easier to find the acquisitions scorecard, where I keep track of M&A activity, and the roundup of third-party product reviews. There's a new roundup of investments in social media analysis, and I've added a job board (yes, it accepts international listings). Finally, I added the social network icons that are a required part of social media web sites in 2011.

I always intended for SMA to be a sort of online trade journal, the best source of information about what's going on in the market. The new sections are a step in that direction, and I hope you like them.

The full database just passed 400 companies, which includes many that are no longer active in the market. Listening companies, are you on the wrong list?

An Easy Request for Listening Vendors

2011-10-27T14:33:15Z

Is your company in the listening business? Monitoring, measuring, analyzing social media? Using your own technology (not third party tools)? I have a simple request for you. It involves very little effort on your part, and there's free marketing in it for you.

Ready?

I'm in the process of turning my database of listening companies, which I've compiled over the last five years, into an online reference for everybody. Since the killer part of my earlier research projects was writing descriptions of every company, I'm letting you write your own, this time.

If you're on my vendor mailing list, you should already have an invitation. If you don't have it, or you're not on the mailing list, send me an email. The directory goes live next week. It's up to you to fill in the blank on your page.

Tick tock…

Make Your Company Look Alive

2011-10-18T18:46:16Z

When I talk with people in the social media analysis business, it's common to speculate about a coming reduction in the number of competitors. Having just finished a long-overdue review of my vendor database, I'm here to report that it's already happening. A number of companies have already gone away; they're just not the ones you've heard of.

At the beginning of the review, I had roughly 350 companies in the database. I've always been generous in my definitions, so these aren't all direct competitors, but they all did something in the area of monitoring and measuring social media, with their own technology.

Thinning the herd
As I went through the list, I found 19 companies that appear to have gone out of business entirely and another 76 that don't appear to be active in SMA this year. A few more have been merged into other services by their parent companies.

Some of the reduction is the result of my getting more strict with the definitions, but a lot of it is companies that have changed focus or deemphasized their listening businesses. Notice, for example, the companies that have repositioned themselves into the advertising space.

Acquisitions are always interesting; will the acquired product remain separate or be integrated with the acquiring company's platform? We've seen some of both in this market.

Show signs of life
A review of 350 companies is necessarily web-based, and I generally gave companies the benefit of the doubt. Still, some hints are pretty strong. If you're still in business, you might consider checking the vitality of your own company's presence:

Have a web site.
First, you need a company web site. If www.yourcompany.com doesn't respond, that's a strong, negative signal. If your domain name has expired, that's an even stronger signal that you're out of business.
Check your redirects.
If yourcompany.com doesn't respond and doesn't redirect to www.yourcompany.com, I might jump to the wrong conclusion. Ask your SEO if you don't know how to fix it, but both addresses should get visitors to the right site.
Check your copyright notice.
It's not definitive, but when the copyright notice still says 2007, somebody's not minding the store.
Post to your blog.
Yes, we all fall behind, but after a year with no posts, we start to wonder if anyone's home.
Update your press page.
If a company posts releases on its site and then stops, we wonder what else has stopped. If you never had a press page, at least it's not saying that nothing has happened lately.
Link to your Twitter account.
Almost everybody in SMA now has a link to the company Twitter account on their home pages. Having no Twitter account is not a positive indicator in a social media business.
Check your LinkedIn profile.
One of the more reliable indicators that a company is truly dead is a founder's profile that puts his involvement with the company in the past. If key people have moved on, it's even more important that the rest of company's presence shows signs of life.

Almost time to share
I've been sitting on this industry database for a long time, but it's almost time to share it. If I can figure out how to build it, I think you're going to like the industry directory I'm working on. And if your company is active in the space, look for an email soon.

Photo by Gregg Sloan.

Not Actually Hiding

2011-10-13T02:18:27Z

I didn't mean to take three months off from blogging. I just put it off, one day at a time. Next thing you know, the leaves are changing colors, and it's cool enough to play outside on a sunny day. Now I'm back, although I never really went away.

Let me 'splain.
No, there is too much. Let me sum up.
—"Inigo Montoya" in The Princess Bride

This summer, I started a job, which ended along with the summer. No hard feelings, it just wasn't the fit we hoped for. Now I'm putting more energy into a startup idea I've been kicking around, something that's different from almost everything I've seen. I won't be doing any more syndicated reports, but I am available for consulting projects, and I still cover industry news at Social Media Analysis (see? no summer break there, and we've had investment and acquisition activity to keep up with).

I'm still behind on my reading (some things don't change).

Staring at the draft folder
Blogging returns. There's more to the Omniscience framework, some of the ideas it's led to, and—who knows?—maybe some social media stuff. Defrag is just around the corner, and we're making progress on doing AnalyticsCamp in more cities. I need to write about some of the social media management and metrics books on my pile, too.

Mostly, I'd like to get inertia back on my side on the writing front.

In other words
Nothing much. What's new with you?

Photo by lokidude99.

Defining a Silo Buster

2011-07-06T03:33:50Z

I recently saw a job description that tells me I'm not the only one looking for the value that's lost when analytical methodologies keep to themselves. Change a few key words, and it becomes something that a lot more organizations could use. Maybe yours?

Cross-pollinating analytics
I really like the idea of learning from other fields, such as the physicians who used lessons from Formula One pit stops to improve patient transfers. Most of us aren't working on anything that is truly different; you just have to find the relevant lessons from unrelated fields. It sounds hard, but I think that opening your mind to the possibility is the step most people miss.

I use the metaphor of cross-pollination a lot when I talk with people about intelligence and analytics (cue a silo rant if you missed it). The short version is, I think the various analytics specialties are missing value when they reinvent each others' solutions and fail to learn from each other.

You can get a broader application of the concept from Matt Ridley: When ideas have sex. We work better when we don't try to do everything ourselves.

Hiring a silo-busting analyst
Breaking down some of those barriers is the idea behind AnalyticsCamp, so I was really pleased when I found this great job description at the CIA a few months ago (emphasis added):

As an Analytic Methodologist, you will have the opportunity to develop and apply analytic methods to add rigor and precision to intelligence analysis and collection. You will provide statistical, operations research, econometric, mathematical, or geospatial modeling support to Agency analysis, and you will incorporate your findings into a broad range of intelligence products. Agency analysts are encouraged to maintain and broaden their professional ties through academic study, contacts and attendance at professional meetings. They may also choose to pursue additional studies in fields relevant to their areas of responsibility.

Maybe I'm seeing what I want to see, but that looks like And not Or thinking to me (though I would like to see a longer list of methods). Notice the continuing development aspects, too. What would you think if we adapted it to business, changing the specific types of analysis to the specialties at work in business and added a few that could be at work?

Your company might not offer some of the specific perks of government work, but what are you doing to encourage your analysts to develop beyond the confines of their current specialties? Are you taking the opportunities to learn from other fields, both near and far?

Photo by curimedia.

Driving Intelligence and Analytics with Omniscience

2011-06-17T03:28:20Z

It started with a simple challenge: if I were to draw a big circle around the things I find interesting enough to follow and declare them to be one thing, how would I label it? To avoid flying completely off into pointless musing, assume that it's relevant professionally. Considering that the circle included social media, analytics, intelligence, geopolitics, and natural disasters—to pick a few—the label wasn't obvious. By declaring them to be one thing, though, it soon became clear that the theme was the importance—the value—of knowledge.

The label was Omniscience.

"That's pretty ambitious."
Yes, I'm aware of the definition of omniscience, and no, I'm not suggesting that I know everything or ever will. But among the unattainable goals, it's a good one. I mean, what could you do if you knew everything? You can't, but what if you knew a lot more about things that matter to your business?

What if you knew something that was there to be discovered, and your competitor didn't? Is it starting to sound reasonable yet? Maybe even something you'd want to do?

The framework
I've talked through the Omniscience framework with several folks for early reactions, mostly in person. It involved some handwaving, so I knew it wasn't ready to post. Some people suggested related books, but nobody really shot it down. Now, it's your turn (click for a larger view). I'm not sure I need a lot more assigned reading at the moment, but I'm definitely interested in your reaction.

A framework, not a recipe
This is the top-level view, and each section has a story, a purpose, and examples. But this is the gist of it: starting with a few simple observations on the nature of things, Omniscience is a challenge to expect more of your intelligence and analytics, drawing on a broader range of techniques to track and anticipate a wider range of things that matter.

Omniscience provides a thread. It links things you know with things you do—and with things you don’t do. It links the very large and the very small, the short-term and the long-term. The way you think and plan and the way you measure and evaluate. It provides a structure to identify missed opportunities and to evaluate new ideas. And although it looks highly theoretical, it's already suggested a practical application that I haven't seen on the market.

Naturally, I think it's a big deal. Does it make sense to you, so far?

Why I Haven't Read Your Book (Yet)

2011-06-03T01:19:16Z

I have a pile of books on social media, measurement, and management that I'd like to get to. It sits next to another pile of books, and that's become a bit of a problem. While the social media books look potentially useful, my other exploration keeps adding books to the second pile. It's amazing stuff, so I thought I'd share them with you.

Well, that's random
Sometime last year, I decided I needed to start learning about complexity science. I knew that complexity and uncertainty were unavoidable, so I wanted to learn more about how to work through them. I thought that the emerging discipline might be helpful, and Melanie Mitchell's Complexity: A Guided Tour (2009) was the introduction that suggested I was on the right path.

You might not think you're interested in complexity (if you've heard of chaos theory, this is the next generation). It's still fairly obscure, and even its definition isn't completely settled. Because it's inherently interdisciplinary, you'll probably find parts you don't care about. If you're interested in social networks, though, guess what.

Connect this
If you're up for a challenge, read two books by Albert-László Barabási, back to back. Start with Linked: How Everything Is Connected to Everything Else and What It Means (2003), which fills in the background on ideas you know, such as social networks, network analysis, and power-law distributions (the math behind the long tail). Eight years after its initial publication, it's still an excellent source for anyone who has ever used the term social graph. You're using this stuff, so you may as well understand it.

Barabási's newer book, Bursts: The Hidden Patterns Behind Everything We Do (2010), digs into patterns in human activity and how much of what we do can be predicted. I'm not quite sure what this one means, but I suspect it's important. Plus, where else are you going to find a book about predictions that uses the story of a 16th century peasant uprising in Transylvania to make its point? Yes, it's full of math and theory, but this is a fun one to read, too.

One point I'm going to take to heart is based on the discovery of power-law distributions in communications patterns. It only emerges when people are overwhelmed and start to prioritize, but university emails and Einstein's correspondence show the same pattern. What it means is that bloggers don't need to apologize for quiet periods when they're busy behind the scenes.

Catching up, adding more
Walking people through the framework I'm working on turns out to be bad for clearing the reading backlog. The first person who saw it suggested Nassim Nicholas Taleb's The Black Swan (2007), which was sort of like The Long Tail (2006): one of those concepts that's been discussed so much that you feel like you've read it, even if you haven't.

The basic idea, if you're not familiar with it, is that improbable, high-impact events will occur, and they can't be predicted. Rather than trying to predict them, then, we should structure our environments (not just business) to minimize our exposure to the bad ones and maximize our benefit from the good ones. I'm not ready to give up on attempts to analyze the future, but it's a good reminder to incorporate a healthy dose of uncertainty into the process.

Which leads us to scenarios
The second person I told about the Omniscience framework heard the bit about planning methodologies and pointed me toward GBN and Peter Schwartz's The Art of the Long View (1991). It turns out that I had read the important parts on scenario planning when the book was new, which was reassuring. At some point, though, I need a more detailed source on that topic. Plus, the 20-year-old predictions of trends that are now obvious give the book a distinctly vintage feel.

Fortunately, scenario planning was already part of the mix. So far, it's holding up.

About that framework
I know, I've mentioned the Omniscience framework too many times, and I need to show it to you already. I will. But while that's in draft, I thought you might like some of these. If you feel stuck in the social media bubble, they're are a good antidote.

Update: The post introducing the Omniscience framework is now up.

Attacking the backlog of social media management/measurement books in 3… 2… 1…

Applying Intelligence and Analytics to Online Statements

2011-05-27T14:33:18Z

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.

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.

What If Intelligence and Analytics Are the Same Thing?

2011-05-26T21:09:45Z

In 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.