Discover PerformanceHP Software's community for IT leaders // June 2014
Augmenting humans: Better business through analytics
Looking ahead, analytics won’t be something you go to for insight. It’ll come to you with answers before you’ve asked the questions.
By Mike Shaw, HP Software Strategic Marketing
The last 100 years have seen massive physical augmentation of humans. I watched a farmer trim the hedges in the fields opposite my home office the other day, using an attachment to his tractor: one field took him about 30 minutes. Two hundred years ago, the same task would have taken over a week. That’s a massive augmentation of human capability.
Let’s jump to 2020 and see such a system in action: Working in a large enterprise, Anton and Aziza are trying to create a marketing plan for memristors. The emails and IM dialog are flowing back and forth. The "human augmentation system" is analyzing this dialog. It figures out what they are trying to do and, like a combination of the world’s most attentive personal assistant and the world’s most diligent information researcher, it is offering assistance:
- "These people within your [large] organization are also discussing this topic."
- "Here are internal documents related to achieving the goal you’re trying to achieve—I’ve summarized them for Anton, but kept in lots of detail for Aziza, because I know that will match your personal working styles."
- "Here are the latest best practices on e-marketing for technology—I know how you like to work, Anton, so I’ve chosen only those that are short and sweet."
As I research the upcoming Big Data 2020 chapter for HP’s Enterprise 20/20 project, the theme that comes through time and time again is "augmenting humans"—using data analysis to empower knowledge workers. This augmentation is a combination of three sets of data analysis:
1. Your current state and intentions. In the Anton and Aziza scenario, the email and IM conversations are monitored and the intent of the dialog is determined. Down the road, sensors on smartphones will help in the determination of intention. We know that Apple is working on this; universities are doing likewise, calling it affective computing. So, if I’m angry (facial expression, voice tone, skin resistance, brain waves) and moving fast (motion sensors, GPS sensors), I need information to be focused and brief.
2. Your personal avatar. Imagine an intelligence that knows all about you. Think of it as the best personal assistant you ever had, a personal assistant who has been with you for years. It knows how you like to travel, how you like to arrange and attend meetings, how you like to shop, how you like to receive information—everything. It’s configured on a per-application basis, inferred through observation of your life, and it runs across all applications, both business and personal. (Here’s an example of a speech recognition system from Cambridge University that learns as you talk to it, and if it doesn’t understand, you can explain things to it.)
3. The augmentation engine. Today, when you are shopping on Amazon, the Amazon program takes your current state (what you are trying to buy) and your personal preferences (how you have shopped in the past), and it uses these and the information about what is available to buy right now to make recommendations—to "augment" your shopping experience.
The Amazon system is very specific to Amazon. Imagine that you could point such a system at your corporate and related business resources. It could then augment knowledge workers like Anton and Aziza.
Making it real
HP Enterprise Services has already taken a step in this direction with an offering that monitors Microsoft Lync conversations, passing the content to the Autonomy engine to derive human meaning from the interaction. It then uses this meaning to proactively find information to help you in your dialog. They call this offering "HP In Context Analytics." (Read a short PDF overview here.)
I believe that "augmenting humans" is the most important concept in the Big Data 2020 chapter—it’s really the theme of the Information Age. There are many facets to it, and we’ll go into greater detail when we release the chapter, as well as in blog posts surrounding the chapter’s release.
Future issues of Discover Performance will continue to preview the ideas behind the upcoming Big Data chapter of the Enterprise 20/20 project. For more about making the most of your data today, visit HP.com/HAVEn.
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