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Twitter chat recap: Innovations in analytics with Seth Grimes

March 4, 2014

Last Wednesday, February 26, the Big Data & Analytics Hub, which is where you're reading this post right now, and Business Analytics software at IBM ran a Twitter chat entitled, “Innovations in Analytics" with special guest, Seth Grimes (@sethgrimes). Seth is a leading industry analyst covering text analytics, sentiment analysis and analysis on the confluence of structured and unstructured data sources.

Given the broad nature of this topic there was the potential for us to be led into peripheral, but relevant, topics, which we briefly touched on, without ever leaving the core of the conversation: innovations in (big data and) analytics. We focused the coversation around seven questions that Seth commented on while leaving ample time for others to share their prespectives—and share they did. It was a balanced discussion providing insight into these recent innovations in analytics and how they’re changing the way we live our lives and how we run our businesses. We had many participants who made significant contributions that we want to thank:

Below is the Twitter chat recap with questions, Seth's responses and some of the great input we heard from other Twitter chat participants, including those mentioned earlier:

  • @sethgrimes A1: I see 3 innovation aspects: 1) Improve existing; 2) Extend to new; 3) Disrupt. Now, #BigData itself embodies an innovative attitude, that you can *do more with more*, via #analytics of course. But those #analytics approaches to #BigData have to be different, because conventional methods may not scale and new methods, tailored to #BigData, may be able to extract insights that conventional #analytics methods miss.
  • @thesocialpitt A1: #bigdata innovation helps answer the big "what if?" questions that have been elusive.
  • @sethgrimes A1: Ah, but big challenge is finding/framing those Qs.
  • @tracewall A1: Big data and analytics need to be actionable -- do that and it's innovative. I don't want to have to use a data scientist.
  • @mt_marko A1:..How to extract this info from diverse data sources ingeniously and tirelessly? New innovations can help us in this
  • @thesocialpitt A1: Using more data and being more strategic in that use
  • @tcrawford A1: Innovation for data comes from the ability to leverage data in ways not previously possible to gain greater insights
  • @sethgrimes A1: So I'd sum, that defining innovation involves defining the questions you can ask & elements that contribute to answers
  • @sethgrimes A2: I'd start with machine learning, in particular semi-supervised and unsupervised learning and also deep learning.
  • @sethgrimes A2: There's other IBM tech such as I2 Analyst Workbench, which does synthesis & visualization, also more here-and-now.
  • @stevemassi A2: watson, etc are making it easier to get-to-insight faster, but need to continue making it easier for biz users
  • @sethgrimes A2: Also data integration/fusion (a la Watson, certainly!), of personal (profile), geospatial, transactional, attitudes
  • @sethgrimes A2: Interesting that few here have picked up on my citing Machine Learning. Maybe if I cite #Google, that'll get you riled up
  • @sethgrimes A2: Concluding on Watson: The real innovation is the assembly of disparate tech and operation at scale and Q&A capability.
  • @thesocialpitt A2: stream computing like InfoSphere Streams does is innovating many uses, from online gaming to fraud detection
  • @sethgrimes A3: Here's another app'l area for you: Computational advertising.
  • @sethgrimes A3: Really any area where traditional methods don't scale or adapt well to new data may be a candidate.
  • @stevemassi A3: google's use of search related to flu prediction always interesting
  • @dianemcwade A3: On this side of the pond not many businesses understand the concept yet and hard 2 find analytic products affordable 2 SMEs
  • @sethgrimes A3: Language understanding and image recognition are 2 great examples of tech functions that can be applied to many purposes
  • @sethgrimes A4: I'm big on experimenting & exploring, with different data sets, analytical methods, visualizations, etc
  • @sethgrimes A4: I'm curious what others see by way of risk, in the pursuit of business decisioning driven by #BigData #analytics
  • @bobehayes A4: Risks? Downsides? With so much new data involved, it could take you down a rabbit hole
  • @sethgrimes A4: I don't see huge risk, until you put yourself in a bet-the-store position. The real risk is in standing still.
  • @tcrawford A4: Orgs need to start w/ a strategy that drives toward specific outcomes. ‘Data stewardship’ also comes into play
  • @sethgrimes A5: Anything expensive/slow. Hate to say this, but areas often handled by people, whom automation could make redundant
  • @sethgrimes A5: Think customer service, logistics functions. Are truckers looking forward to self-driving rigs?
  • @stevemassi better use of real-time traffic and routing MT @SethGrimes: A5: logistics functions
  • @sethgrimes A6: Need starts w/ability to decide where to place effort & resources, how to operationalize, productize & monetize insights.
  • @stevemassi A6: Major $, especially in pple/time so strategic commitment driven by need for getting smarter faster
  • @sethgrimes A6: Talent is the biggest and most difficult investment, and I don't mean just hiring data scientist types.
  • @marie_wallace A6: As well as data scientists we need biz folks that can understand & internalize analytics & integrate into how they work
  • @sethgrimes A6: You can outsource, or buy as-a-service, software, platform, even R&D elements, that aren't core to your business.
Seth Grimes
  • @sethgrimes A7: I don't have a crystal ball. so I'll fall back on the old (Bayesian?) standard of projecting from past experience…
  • @sethgrimes A7: How do you stay nimble? I'll go back to my response to an earlier question: Experiment, explore. Stay aware and stay open
  • @sethgrimes A7: Admittedly, that answer felt kind of obvious, platitudinous.
  • @sethgrimes A7: We're heading toward more data and better retrieval & analysis, so faster & more effective & more pervasive automation
  • @stevemassi  A7 nimbleness driven by open knowledge base.narrow base and lack of access to data pts leads to rigid decision making
  • @mt_marko A7:I repeat my self but by utilizing cloud services for building apps based on SoLoMo, data analytics & cog computing
  • @tracewall A7: Big data is a necessity now. The guys who use it right will prevail. 2 stay nimble, u need big data and ppl who can use it

Let us know if you enjoyed this Twitter chat brought to you by the Big Data & Analytics Hub (#bigdatamgmt).  These chats take place every Wednesday at 12pm Eastern Time. Join us as these are a fun, informal and informative way to keep up with big data and analytics in real-time!