Recently in Innovation Category

I'm going to do something old-school and blog about a couple of blog posts today. Consider it a break from the latest outragefest on the 'book. Instead, let's share bright ideas about large-impact innovation and how we've been looking for it in the wrong places. It's what happens when two posts, posted months apart, cross my desktop in the same morning.

First up: Jerzy Gangi's post from August, Why Silicon Valley Funds Instagrams, not Hyperloops, runs down the reasons that venture-funded startups keep launching relatively easy web-based software applications. It's worth a read. The short version is, that's what the investment system is looking for, and [insert Willie Sutton quote here].

Next is "Killer Apps" Evolve, Vinnie Mirchandani previewing Chunka Mui and Paul Carroll’s new book, The New Killer Apps: How Large Companies Can Out-Innovate Start-Ups. Google's self-driving cars are one example (built with investment from both corporate and government sources).

We shouldn't be surprised that startups and investors play by the rules of the game. Innovation and addressing the big issues of our time, however, are not the game they're playing.

The M&A market can be characterized as a giant distributed R&D department for major corporations.
— Jerzy Gangi

Remember corporate R&D? Bell Labs, PARC, Lockheed's Skunk Works? Big companies exist to take on projects and markets that are too big for small companies, and part of what they do is large-scale innovation. Whether they invent in their own labs or build from acquired startups, big changes that take place in the physical world will happen only when somebody puts serious capital behind them.

It's interesting that the old-school sources of innovation—university, government and corporate labs—are still out there, and despite long-term reductions, they're still at work. If we're looking for the world-changing innovations, maybe we just need to put more effort into learning about them and their projects.

Story starterDo you have any of those mix-and-match books that let you remix parts of their pages? (It's ok; you can claim they're for your kids.) We once bought a story starter for my son (see, like that) that combines an opening quote, a character, and a situation. Put together a random grouping, and you have the beginning of a story.

That's sort of how I look at data and analytical methods.

Here's how it works: First, remember the basic building blocks of social media analysis: data, analytics, and application. Now, let's generalize from the social media example, because this isn't just about social media data.

We get three basic pieces:

  1. Data
    Internal and external sources, open (freely accessible) and proprietary (paid). There's a lot more here than most discussions get into.

  2. Analytic methods
    Sentiment analysis, topic clustering, source profiling, statistical analysis, geospatial analysis—the list goes on and on. This is a good area for And not Or thinking.

  3. Applications
    In a software business, this usually refers to the product, its features and their benefits. Here, though, think about the work that can be enabled through the application of data and analytics. Think about functional roles and what they need to do, and then you may get ideas about what a software application should do.
Put the three together, and you get data that can be combined with analytic methods to generate value in a particular application, or functional role.

We tend to get stuck in familiar modes of operation, thinking that a certain type of data implies a certain type of analysis, which is useful for a certain application. We fall back on social media + sentiment analysis + marketing. You might even think of it as a chemical reaction: social media + sentiment analysis -> value for marketing.

It's comfortable. It's familiar. It's not wrong. But there's more.

Time to mix it up
To find more value in the data and analytics, we need to start flipping the pages in the book. Which analytic methods could make this source of data useful for that function? I know what I know. What have I not yet found?

You can start with any piece first, and switching the order aids discovery. You might start with a functional role and ask what information would help them. You might start with a data source and think about how it might be useful. Or you could ask how an analytic method might turn data into something meaningful.

The secret is that each category has more options than you're probably using. More sources of data, inside and outside your organization. More analytic methods—some still being invented. More functional roles than the ones you're used to supporting.

Combine them, and you put familar data through unfamiliar analytics. New data through existing analytics. And you find ways to create value beyond the marketing, public relations, and customer service roles we associate with social media.

Do I have specifics? Sure, but not all in one blog post.

The mix-and-match book is similar to the Omniscience framework I proposed, which is all about understanding how intelligence and analytics can be useful at all levels in the organization.

I sometimes summarize the opportunity of social media analysis as using computers to "read the Internet." It's not an original idea, but it is one we still haven't mastered. I've seen many tools that find relevant content and apply some level of automated analysis, but we're not about to replace the analyst. One simple question I've started to think about is, "then what happens?"

The SocialSpook 9000 reads millions of blog posts, Facebook updates, and tweets every second. It finds every relevant mention in your space, extracting the facts, opinions, and needs that you're looking for. Its sentiment analysis engine provides 120% accuracy in 38 languages, and its graphics are so well designed that whole new awards contests have been created for it to win.

In 2007, I pointed out the need to link social media monitoring to customer service, because most of the problems that people were seeing as PR problems started with unhappy customers. Since 2010, I've been thinking about another application: blending social media data with other publicly available sources to create an automated view of what's happening in the world. It turns out to be a big challenge.

My own private news channel… or command center?
We can take this in several directions. At the low end, applications such as Flipboard generate personalized media based on activity in the user's social media accounts and selected topics or sources. In the middle, we might have a more dynamic version of the social media dashboard running in the conference room or reception area. It's the web-powered news channel that always shows something you might care about.

At the high end, we're looking at a valuable—but noisy and sometimes misleading—source of crowdsourced information about events in near-real time. The obvious applications are in government: national security, law enforcement, emergency management, and disaster response agencies are looking for fast and accurate information from social media sources. I see value in corporate applications, too, for functions like security, risk management, logistics, and business continuity that need information when things happen. Preferably without hiring an army of analysts to look at dashboards on the quiet days.

Now what happens?
The challenge in using social media for real-time awareness is that the volume level becomes overwhelming just when the information becomes most valuable. Forget looking for the needle in a haystack; this is the needle in the needlestack. Faster than you can read them, more messages arrive, and they're all relevant.

Existing tools generally emphasize either handling messages individually (think customer service or community engagement) or analyzing them in aggregate (think sentiment and leading topics). For this application, we want the system to help analysts deal with the volume without losing the detail, and that's where I started asking about what happens next.

For all the systems that can notice something happening and put it on a screen, I wanted a system that can notice and pay attention. So what would that look like?

Here's an idea (click to enlarge):

Computer Attention in Situational Awareness Applications

The inputs to this system can go beyond social media content; depending on your application, it might pick up data about natural disasters, weather, or market data. It might incorporate traditional news media, commercial intelligence services, or internal data. Its models will reflect the needs of its users, so a system that looks for, say, transportation-related incidents could be quite different from one looking for damage reports in weather emergencies.

This has a lot of moving parts, and it builds on what others have already built. The central idea is to go beyond the dashboard and think about how the system can relieve analysts of some of the burden of reading the alert queue. Step one is to consider what an analyst does with that information and how a computer could mimic that.

I'm sharing some of the frameworks that have been hiding on my whiteboard. Want the long version? Email me.

About Nathan Gilliatt

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  • Voracious learner and explorer. Analyst tracking technologies and markets in intelligence, analytics and social media. Advisor to buyers, sellers and investors. Writing my next book.
  • Principal, Social Target
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