Recently in Strategy Category

Another sketch from the whiteboard

A couple of years ago, a suggestion that I develop a maturity model for social media analysis led to a different kind of model. My approach to this space has always been to explore its edges, looking for what might be next. One effect I've noticed is that change circulates through the ecosystem of companies, their customers, and their suppliers. Where change keeps coming, everyone's learning together.

A linear maturity model defines development stages toward a known destination, but in a system where everyone is learning, the destination is still unknown. We react to others, and others react to us. Change reverberates through the system, and we don't yet know what maturity looks like.

What this means in social media analysis
If social media analysis were good for one thing, we could have a simple maturity model. The products would progress toward a theoretical ideal, and clients would mature toward efficient, effective business practices. But the technology stack is built on areas of active research, new platforms are driving new consumer behaviors, more business functions are showing interest in how to use social media to do their jobs, and vendors are trying new ways to distinguish themselves.

Virtually every piece of the puzzle is moving.

Let's go to the whiteboard to see if we can visualize it.

There's a lot going on here, and this is the oversimplified version. Here's the basic dynamic: on the right, new capabilities become practices; on the left, new expectations become requirements. In the overall system, we expect more from our suppliers as we adapt to new capabilities and adopt new practices.

Think about what is being learned in each loop.

• Tool vendors combine their own R&D with new capabilities from research labs and partner companies to expand their products' capabilities, enabling new tactics for their clients, who provide feedback and new requirements based on real-world use of the products.

• Consumer-facing companies experiment with new tools and capabilities, and they learn from both operational results and customer reactions.

• Customers react to companies' online tactics, adjusting their behavior to maximize their own benefit. When they find a practice they like, they may expect other companies to mimic it.

The catch is that this is all happening at the same time, and the companies, at least, are trying to predict how their customers will want next.

Who's learning fastest?
We know of some unintended lessons, such as teaching customers to complain publicly for a quicker response, and redefining like. But where do we look if we want to get ahead of the market? Try these key areas:

• Outside innovation - New research and inventions may provide answers to questions you've wanted to answer.

• Product capabilities - What's possible keeps changing, but don't look only at existing suppliers. Look at adjacent markets for capabilities worth adapting to new applications.

• Client requirements - It's always worthwhile to pay attention to what companies say they'll pay for.

• Client capabilities - Watch what companies are actually using, too.

• Competitor actions - Watch early adopters for practices that may become standard. Is there a better way to do it?

• Customer expectations - How are people reacting to new business practices? What issues are being raised? What new expectations?

Like any model, this one raises more questions than it answers. That's the point. What will it help you discover?

More ideas from the whiteboard:

• Computer attention
  
• Omniscience

I'm sharing some of the frameworks that have been hiding on my whiteboard. Want to apply them in your business? Email me.

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.

This XKCD cartoon places many predictions on the same timeline (click through for the full version—it's much too long to include here). This is what I mean by only one future: all predictions have to happen (or not) in the same future. If they're mutually inconsistent, then somebody's wrong.

More and more, I find myself applying this filter when I hear or read some breathless prediction of fantastic technological breakthroughs that are supposed to be just ahead. We have a lot of predictions floating around, and I'm pretty sure some of them are wrong.

Since 2006, I've been learning about social media analysis—as a business, a set of technologies, and a set of business practices. If you read the blog, you've seen some of what I've figured out. Along the way, my professional interest in the information value of social media activity collided with some of my other interests, which has led to a rough draft of a strategy that I'm modestly calling Omniscience.

It's too early to publish the whole framework, but I want to share a few foundational thoughts that are shaping the way I look at things. I find myself referring back to these every day, whether the topic is business, current events, or long-term futures. As you read through the individual elements, think about how they interact.

1. Everything is connected.
   A drought in China, floods in Australia, turmoil in the Middle East—which could affect economies in the US and Europe? Right. All of them. Cause-and-effect relationships circle the globe, and they don't respect the arbitrary domains of knowledge that we create. Energy, climate, economy, politics—they're all connected, and so is everything else.

2. Everything is uncertain.
   Any useful prediction has an element of uncertainty that we like to ignore. It's easier that way, and anyway, uncertainty is interpreted as weakness. A better approach is to embrace the uncertainty—evaluate it, and consider the possibility of unexpected outcomes. Do you bet everything that you're right?

3. Think and, not or.
   I see so many topics framed as false choices when the right answer is probably all of the above. Explore with and, focus with or, and never stop exploring.

4. Only one future.
   We like focus, but focus projected into the future is tunnel vision. We have only the one planet. Everyone's predictions—on technology innovation, business growth, sovereign debt, energy supply, climate, demographics—have to play out in the same world. Everything is connected and uncertain, so predictions interact, even (especially?) when we would prefer to deal with one topic at a time.
   

Yesterday, I hinted at some of the other stuff I'm working on. This was the starting point. Wait 'til you see where it leads.

Update: the rest of the framework is up.

One of the first people to see the Omniscience framework suggested that I read The Black Swan, just in time for the Arab Spring uprisings that threaten so much of what had been described as "stability." Perfect timing.

First, listen. Listen first. First, listen. Getting redundant yet? Is that also the way you've set up your own listening activities?

I recently talked with someone whose company has social media responsibilities divided among several executives. Each has a listening arrangement in place: one has an internal team, and the others have outside agencies doing the work. The kicker? All of them may be using the same platform.

I'm thinking there are some opportunities to rationalize costs there.

So here's a deceptively simple question for you:

How much are you paying to maintain multiple, independent listening posts?

• How many times do you pay for the same software?
  
• How many times do you pay for the same data?
  
• How many times do you pay for the same analysis?
  
• How many people are handling the same posts?
  

Getting started is great, but the pilot project approach has costs. Are you ready to manage yours?

Photo by Andrew Magill.

Focus is a good thing. To know what you're doing, you need to know what you're not doing. So companies define their space, focus their attention, and get to work. But.

What if customers frame their needs differently from you? What if a competitor looks at the world from another angle? What if something that is off-target in your frame turns out to be on-target for another way of looking at things?

When do you notice that last year's off-target product from an adjacent market turns out to be this year's competitor?

What if the customer you've focused all of your attention on realizes that he's been working in an analytics silo and decides to see what else is out there?

No answers today
This isn't one of those questions with a general answer. The question is the point. Focus to get things done, but don't stop wondering what's just outside that might become relevant.

That's my target from the virtual firing range at the National Museum of the Marine Corps. Turns out I'm quite the marksman when the rifle's firing a laser, there's no wind, recoil, noise, or other distractions—and most especially when nobody's shooting back. If you're near Quantico, Virginia, the museum is well worth your visit.

National governments represent a special category of large organizations: they're far larger than any company, and they're in a funny kind of business. But their talent for generating documents occasionally leads to something of value in the business world. Would you believe a strategy document that frames the relationship between social media and Enterprise 2.0 in a sidebar?

Though it's not what most people will be looking for, the new 2009 [US] National Intelligence Strategy (PDF) neatly categorizes two types of objectives for the intelligence community (IC). If you squint a little, I think you'll see how these categories could be repurposed for the 2.0 crowd:

Mission Objectives

• MO1: Combat Violent Extremism
  
• MO2: Counter WMD Proliferation
  
• MO3: Provide Strategic Intelligence and Warning
  
• MO4: Integrate Counterintelligence
  
• MO5: Enhance Cybersecurity
  
• MO6: Support Current Operations
  

Enterprise Objectives

• EO1: Enhance Community Mission Management
  
• EO2: Strengthen Partnerships
  
• EO3: Streamline Business Processes
  
• EO4: Improve Information Integration & Sharing
  
• EO5: Advance S&T/R&D
  
• EO6: Develop the Workforce
  
• EO7: Improve Acquisition

Identifying external and internal objectives
Obviously, I'm not suggesting that national security and social technologies are the same thing. If you're not in the national security business, then "combat violent extremism" isn't your first objective. Instead, look at the framework. I think that the distinction between mission objectives and enterprise objectives might just clarify the relationship between externally-focused social media and internally-focused Enterprise 2.0 initiatives.

• Social Media for Mission Objectives
  Mission objectives are closely linked to the overall objectives of an organization. At the enterprise level, these are measured in terms of financial success; in marketing, they're the familiar product- and customer-oriented objectives that lead to financial success. These are the kinds of objectives we see in social media discussions (especially when social media for business is interpreted as social media marketing):
  
  • Combat negative impressions of the company
    
  • Improve customer communication and responsiveness
    
  • Increase brand visibility
    
  • Enhance customer loyalty
    
  • Integrate market intelligence
    
  ...add your favorite social media objective here. The social media focus on connecting with the worldwide conversation in support of the business reflects an emphasis on mission objectives.

• Enterprise 2.0 for Enterprise Objectives
  The E 2.0 case is even easier to make—look at E04 on the NIS list (improve information integration & sharing). Look at the list through your new technologies lens, and you'll hardly need to edit to start applying it. Enterprise objectives are about making the operation work better, so the prescriptions are generic, not specific to the organization's mission. Despite the idealistic rhetoric, improving the operation is the argument for E 2.0.

Aligning social media and Enterprise 2.0
I don't think the internal/external view of social media and E 2.0 is all that new, but I do think it's instructive to see the two types of objectives neatly linked in one document. If the evangelists of social business strategy succeed, I think we'll see more explicit alignment of these high-level categories.

Thanks to Andrew McAfee for pointing out the new document.

If we haven't talked lately, you might not have heard about the theme I've been developing. Last year, I was calling it "the management layer of social media," but I knew that was too geeky. So I refined it to "managing social media." While so much of the talk is about the big ideas and cultural changes surrounding social media adoption, this is all about how to make it work in large organizations. I expect to spend a major piece of the next few years on managing social media.

You can't manage social media!
If this were another post on how companies should listen and participate in social media, I'd be prepared to agree with the standard objection about loss of control. But I'm not talking about control. This is about what companies do internally to manage their social media activities. Just Do It is a great ad campaign, but it's not how things happen in medium or large companies.

Today, I'm sharing the general framework. It doesn't spell anything, but it can be fun to pronounce—especially if you pronounce both Ps...

Organization
Policy
Process
Technology

For the Groundswell fans, this doesn't replace POST—it addresses a separate set of issues that come up inside the company. Issues that we'll get into in the coming months.

Connecting listening to business processes
The first topic is something from the process column, which I've heard as an issue from multiple sources: how to connect social media listening and engagement to business processes. I've encountered at least four different organizational approaches so far, all of which seem to work. I'm lining up the case studies for a report; if you know of a good example of a company that's doing it well, I'd like to know about it. (Yes, I know about Dell. We all know about Dell.)