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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.

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.

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?

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.

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.

Have you noticed a lot going on lately? Several Arab countries are renegotiating their governance; storms, floods and earthquakes are making life hard in the Pacific; and pirates are expanding their reach in the Indian Ocean. There may be other things going on, too. How do you keep up? Where do you find meaningful analysis? You're not still waiting for the evening news, I hope.

Business Insider shared this map by Citi's Tina Fordham this morning. It's similar to something I started drawing to explain the context to my son, except Tina kept going and finished the map. I like the idea of summarizing the protests and political developments on a map, because it invites the viewer to think about cross-border effects. The Arab Spring uprisings have spread throughout the region, so looking at the entire region is useful.

What would make it more useful would be to expand its scope, make the map interactive, and update it in near-real time. In short, make it a dashboard for political unrest. So, I started looking for one. What I learned is that real-time incident maps and intelligent summaries may be mutually exclusive.

Update: The Economist made an interactive map of the region that presents political and economic indicators, but no current awareness.

Trying out global situation maps
The RSOE Emergency and Disaster Information Service is a dashboard for the world that comes close to what I'm looking for. It pulls information on natural disasters and a few other categories into an impressive application that combines maps, a table of incidents, and incident details. What it doesn't do is cover political unrest or offer broader summaries—but it's free, and it does cover events that don't make the news.

Global Incident Map is another Google Map mashup of incident reports. Incident details and current updates are limited to subscribers, but there's a free trial. The developer also offers other maps of specific topics of interest. The design—especially the flashing icons—has kept me from the trial so far, but it might be interesting to compare to the RSOE map.

Maplecroft reports on risks and risk indicators globally. You'll have to pay for their maps and analysis, but it might be a good investment if your interest is more than personal. The map at right is a top-level summary from their Global Risks Atlas 2011.

ReliefWeb generates maps of countries and regions experiencing emergencies of all types. I'd like to see them create the global situation map, but the maps they do provide can be quite informative. Today, for example, they have a map that reports on humanitarian agencies in, and refugees leaving, Libya.

The map equivalent of a Twitter search is Ushahidi, a crowdsourced crisis monitoring platform that maps reports sent in my email, Twitter, SMS, and probably semaphore in the next version. This example is tracking recovery efforts after the Christchurch earthquake. I haven't found a directory of Ushahidi deployments, but it's easy enough to Google Ushahidi Egypt or look through the Twitter account (@ushahidi) to find the maps. Update: The new Ushahidi Community site has a map of current deployments. The field reports are about as far from high-level analysis as it gets, but if you want details…

My new secret weapon
STRATFOR is an online publisher of political, economic, and military intelligence that has provided excellent coverage of the Arab Spring events. In theory, traditional media do much of the same work, but I've found that STRATFOR regularly picks up angles that aren't mentioned in the media, and they don't lose track of the rest of the world when the media focus on the topic of the week. It's a paid service, but they offer a free version to test the waters.

As we've seen in other domains, software doesn't replace analysts; it gives them new tools and data to work with. So I'm not surprised that the best sources I've found so far require subscriptions. It beats trying to process the firehose, and I do like being informed.

Related:

• Europe Media Monitor - Global News and Alerts

Today's Wall Street Journal had Twitter abuzz about social media monitoring and privacy in closed communities ('Scrapers' Dig Deep for Data on Web). Specifically, a health discussion board and a social media analysis vendor using individual accounts to access personally identifiable health information. It's obviously an ethical question, but whose ethics apply? As far as I can tell? Nobody's (yet).

People are sharing personal stuff online, sometimes sharing more than they realize. We need to be careful about how we handle this information, but from what I can see, the ethical standards are just as siloed as the measurement standards. People brought along whatever ethics they subscribed to before they started dealing with social media, but the existing standards don't really cover the new activities.

Think about the different functional roles where you might find companies using social media data:

• Market research
  Market researchers have strong ethical standards that come from social science research. They get into things like informed consent, but does that really apply to data mining of publicly available data? Do they apply if the data is aggregated, and no personally identifiable information is preserved? What ethical standards apply to desk research?

  Jeffrey Henning wrote about the etiquette of eavesdropping and presented a webinar on consumer attitudes towards social media market research. The short version is that people persist in expecting privacy in their online conversations, despite the public nature of the forums they use. But does their expectation of privacy online translate into an ethical obligation for researchers?

  Update: IMRO and CASRO guidelines may apply to social media research.

• Public relations
  PR ethics say a lot of being honest and transparent in public statements, representing the client and the profession well… but what about the ethics of monitoring and measurement? A recent discussion of ethics in PR measurement suggests that that conversation has only just begun.

• Marketing
  WOMMA takes strong positions on its members' marketing activities, but the closest it comes to mentioning monitoring or research is when it commits to "promote an environment of trust between the consumer and marketer." Other marketing codes I found had a similar emphasis on outbound marketing over inbound information collection.

  Update: WOMMA also calls for members to "respect the rights of any online or offline communications venue (such as a web site, blog, discussion forum, traditional media, and live setting) to create and enforce its own rules as it sees fit."

• Customer service
  Is customer service sufficiently organized as a discipline to have its own code of ethics, or does it simply inherit the company's overall standards? I'll bet you that any existing ethics deal with one-on-one interactions with customers.

• Human resources
  HR ethics related to personal information are based on information that companies aren't supposed to use in hiring decisions. danah boyd shared some thoughts on regulating the use of social media data in hiring.

• Strategy/intelligence
  SCIP's code of ethics doesn't commit to much more than obeying the law. Other types of intelligence organizations get some leeway even on that. If you don't want competitors spying on you, your only real defense is to learn about INFOSEC.

• The source of the data?
• What you do with it?
• The job title/professional affiliation of the user? What if the labels themselves lack agreed definitions?
• No ethics, just laws?
• Nothing—there are no rules?

Update: Is it time for Ethical Standards for Listening Vendors?

Related:

• The Ethics of Listening
  
• Ethics, Open Sources and CI

Bruce Schneier's taxonomy of social networking data (via Tim Finin) provides a helpful starting point for thinking about the various ways that personal information finds its way online.

Social network analysis has been a part of social media analysis (not the same thing) for a long time, but it hasn't been central to the social media discussion lately. Mostly, SNA shows up in the form of link analysis, which is used to identify online communities and influencers. A recent conversation on intelligence applications of social media data got me thinking about how much more could be done with the many expressions of connections online.

Looking for less obvious connections
Link analysis is relatively easy work, since the data you're looking for is helpfully encoded in HTML. Follow the link, map the connection, and continue. But think about all of the other connection data that is being generated, and how it could be used to map social networks or model influence in the real world:

• Explicit social graph data
  Sometimes we make it easy, by making our connections on sites like Facebook and LinkedIn visible to the world.

• Follower/following
  Twitter follow connections are probably weaker than other social network connections, but these connections are mostly public. Asymmetrical follow tells you something different about the relationship.

• @replies
  Probably weaker than a social network connection, but stronger than a follow. @replies indicate some level of active connection (which may be one-way).

• References in text
  A mention of an article or book may not include a link that a crawler could follow, but it's still a citation.

• Mentions in text
  References to people, organizations, and topics within the text of a post. The text might even describe the nature of the connection (e.g., "my friend Bob," "Bob, my former boss").

• Sharing
  Bookmarks, likes, and other sharing services provide another source of links from identifiable parties.

• Book reviews
  What do you read? Which authors? Who comments on your reviews? Are your reviews voted up or down?

• Community membership
  Besides direct connections with individuals, we're joining discussion forums and online communities, which connect us to other members.

• Forum posts
  Active engagement in a community is a signal. Comments on a common thread suggest a connection, or at least common interests.

• Blog comments
  Commenting on a blog indicates that you read it (unless you're a spambot).

• Check-ins
  Check-ins reveal where people go. Who else checks in at the same place? At the same time? What about accidental check-ins?
  

As social becomes a feature of seemingly everything online, the potential to use SNA to build richer analysis only grows. Social media are giving us many opportunities in indicate our connections, both explicitly and implicitly, constantly adding to the public data pool. Whether this is more of an opportunity for analysis or a threat to privacy depends on your point of view.

Image by Marc Smith.

This is one of those posts where the probability that you'll comment is inversely proportional to the probability that this idea is useful to your work.

What can you learn online? How about where someone is? Or where they live, where they work, where they hang out… One of the interesting ways to segment social media data is by the contributor's location, but it's a rare feature in social media analysis platforms because of the difficulty of doing it well. More than they realize, though, people are publishing their locations.

I've heard of two main methods to assign locations to social media sources. The easier method, which initially sounds more accurate, tracks down the IP network address of the associated computer. Every computer on the Internet has one, and in principle, the address corresponds somewhat to location. But the goal isn't to find a server, it's to segment online contributors by geography.

If addresses matched locations in some mythical past, they're useless for location now. Facebook is Facebook, wherever an individual user is. Blogs are hosted by a few big players; even with private domains, there's no guarantee that the web host is anywhere near the user. This blog, for example, is hosted on a machine in Pennsylvania—a long way from where I'm sitting. I have accounts on lots of social media sites, none of which are here.

So IP addresses might help you locate a computer server, but they're not a reliable indicator of where an individual user of that system may be.

Revealed demographics
The more interesting process, which I've heard from a handful of SMA companies, is to extract information revealed by the user, linking profiles across services to develop a profile of the person. If someone links a blog to accounts on services like Twitter, Facebook, and LinkedIn, then the combined profiles can build a better picture of the person. Location is one of the major components of that picture.

People have lots of opportunities to announce their location in social media, especially in all those member profiles we fill out. The location field in Twitter might be misleading (remember all the people who changed their location to Tehran in a show of support last year?), but if it agrees with Facebook, Linkedin, or the About Me page on the blog, you have a location.

That's without getting into location-based services like Foursquare. Everyone using those is building a personal tracking database on purpose.

Are you uncomfortable yet? At least this is all based on information that people shared intentionally—so far.

Oops, too much information
The New York Times has an article today, Web Photos That Reveal Secrets, Like Where You Live, which discusses the location metadata attached to digital photographs now. Sarah Perez wrote on the same topic a few weeks ago (Researchers Warn of Geotagging Dangers - Are You Concerned?). Cyberstalking, meet cybercasing: how to reveal your home address on Craigslist.

Both articles emphasize the privacy concerns, as they should. In aggregate, the data creates what Marshall Kirkpatrick calls your new superpower; applied to individuals, it's just creepy.

So, how much location information do you want? Where's the line between constructive location and demographics data and creepy/dangerous? Finally, whose ethics apply to analyzing this data?

Photo by Silver Smith.

Think of your favorite model or metrics for measuring social media activity. Flip through Olivier Blanchard's presentation on social media ROI. Now, with that in your head, read Tom Davenport's 2007 book, Competing on Analytics. How far do you get before realizing that the enterprise analytics crowd is asking some of the same questions as the social media crowd, but looking for answers in different data? What if the two groups met?

When we asked CIOs to identify their visionary plans for enhancing their enterprises' competitiveness, business intelligence and analytics was the top answer, selected by 83 percent of our sample... "Facts drive decisions," said an Insurance CIO. "Plans for imbedded analytics need to enable data capture at the customer touch point."
— IBM's 2009 Global CIO Study (via KDnuggets)

Are you doing this now? I'm looking for companies to interview for my research.

I'm working on a theme that's all about expanding our idea of listening—it's so much more than defensive monitoring, but we need to get beyond first steps. After the last post, Sam Flemming commented on the importance of distinct terms for communicating outside of the bubble, and he's right. After we expand the concept of listening, we need to break it into manageable pieces. Fortunately, the pieces will look familiar.

As a set of activities, listening breaks down into these five modes:

• Searching
  Search is so familiar that we don't always think about it, but look at the advice on getting started in social media. That first step: find out what people are saying, where they meet—you know, the 5 Ws—when you do that as a snapshot, that's search. Don't neglect the value of familiar methods.

• Monitoring
  The usual starting point for a discussion of listening. Through automated methods (typically a dashboard or RSS reader), find and read new posts, comments, tweets, etc. that are relevant to your business. Focus on individual items for action.

• Alerting
  Similar to monitoring, but the system notifies you through email, instant messaging or text when a new item is discovered. Alerts can also be based on measurement thresholds, such as a sudden increase in negative commentary. No requirement to revisit the platform to receive alerts.

• Measuring
  Add a quantitative element to monitoring. Whatever your choice of metrics or measurement silo, measurement is about aggregation and numbers. For the purposes of this list, I use measurement to refer to the generation of regularly updated metrics.

• Mining
  Add a quantitative element to search, and you have data mining, which looks for meaningful patterns in archival data. Although it has a lot in common with measurement (as used above), I'm seeing different practices and benefits that justify separating the two.

For example, if you're doing routine measurement—you're looking at meaningful metrics on a regular basis—is there an opportunity to find different value by taking a mining approach, looking for insight in a snapshot of historical data? A slim distinction, but the point is to step back, walk around a bit, and look at the data from another angle.

Actually, lots of other angles, but more on that later.

Photo by bdu