I recently posted an article, Metadata, Connection, and the Big Data Story, covering the big-data analysis process as applied to “human data” that is communicated in intentionally expressive sources such as text, video, and social likes and shares and in implicit expressions of sentiment.
I recently posted an article, Metadata, Connection, and the Big Data Story, covering the big-data analysis process as applied to “human data” that is communicated in intentionally expressive sources such as text, video, and social likes and shares and in implicit expressions of sentiment.
The article is spun out from Q&A interview of four industry figures: Fernando Lucini (HP Autonomy), Marie Wallace (IBM), Elliot Turner (AlchemyAPI), and Stephen Pulman (University of Oxford and TheySay). (IBM sponsored my recent, New York Sentiment Analysis Symposium.) Read each interview by clicking or tapping on the name of the interviewee.
This interview is with Marie Wallace, and as a bonus, check out Ms Wallace’s March 6, 2014 Sentiment Analysis Symposium presentation, Engagement: The Unspoken Connection. This is —
1) What’s the really interesting information content that we’re not really getting at yet, and what’s interesting about it?
Having spent the last several years focused on social analytics, its not going to be surprising when I suggest that the biggest piece of missing information isn’t the content itself but the meta-data that connects various pieces of content into a cohesive story. We are still, for the most part, taking content snippets in isolation and making decisions about individuals based on this incomplete picture. It’s like listening into a telephone call, without understanding the background of the call, and then only catching every second sentence.
2) How well are we doing with Natural Language Processing, noting that formally, “processing” includes both understanding and generation, two parts of a conversation?
This depends totally on the type of analysis and the source of the content. If I look at areas like healthcare, I believe we are doing a phenomenal job where technologies like Watson are deriving some amazing insights from analyzing text. However, if we look to lower quality content (like social media) with inherently ambiguous analysis (like sentiment, opinion, or intent) then it’s still a bit of a crapshoot.
3) And how well are we able to mine and automate understanding of affective states, of mood, emotion, attitudes, and intent, in the spectrum of sources available to us?
I don’t believe these can be reliably extracted unless we do a better job at filling in the content & meta-data gaps. If I don’t know the individual and the background behind their current communication, how can I really decide what their mood or intent is, and most importantly take effective action. Maybe I’m complaining about my phone after having sent two e-mails to the unresponsive helpdesk, or maybe its because I’m away from home at Christmas and frustrated that I can’t get a good signal at my current location. The customer would expect a completely different response to each scenario.
4) Deep learning, active learning, or maybe some form of machine learning that’s being cooked up in a research lab: What business benefits are delivered by these technologies, and what are the limits to their usefulness, technical or other?
We’ve only just scratched the surface in terms of the insights that can be derived from these new advanced learning techniques and until such time as they are broadly adopted in large scale operational systems, it will be difficult to predict the limits of their usefulness.
5) Mobile’s growth is only accelerating, complicating the data picture, accompanied by a desire for faster, more accurate, and more useful, situational insights delivery. How is your company keeping up?
Mobile is at the heart of everything we do both in terms of the solutions we build for customers and those we deploy within our own business. Mobile is the mother load of contextual meta-data that will allow us to provide the type of situational insights the contextual enterprise requires.
6) Where does the greatest opportunity reside, for your company as a solution provider? Internationalization? Algorithms, visualization, or other technical advances? In data integration and synthesis and expansion to new data sources? In monetizing data, that is, yourselves, or via partners, or assisting your customers? In untapped business domains or in greater uptake in the domains you already serve?
Coming from a company with technologies and solutions spanning such a broad range of businesses, this is impossible to answer in the absolute. However from my own personal perspective as someone working in the Social Business Analytics space, I believe that there is a significant opportunity to be realized in providing integration and analysis (at scale) of social and business data. Social analytics without business data is incomplete at best or completely misleading at worst, however once we combine interactional information (Marie spoke to John) with the business action (to close a sales deal) we can derive insights that will truly transform the social business.
7) Anything to add, regarding the 2014 outlook for analytical and semantic and sensemaking technologies?
2014 is going to be the year of the Graph, at a minimum in the area of social and collaboration, although more likely we will see it applied broadly across the business; allowing new insights to be derived by understanding not just what is being said, but how people are interacting with each other and with the business. Privacy, ethics, and governance frameworks are going to be increasingly important as we look to apply more analytics on people information and derive new insights that need to be tempered by privacy and ethical considerations.
Thank you to Marie!
Click on the links that follow to read other Analytics, Semantics & Sense Q&A responses: Fernando Lucini, Elliot Turner, and Stephen Pulman. And click here for the article I spun out from them, Metadata, Connection, and the Big Data Story.