Society is standing on the edge of significant breakthroughs. Collaboration and data collection will take us there.

This post is part of a series – 14 Business and Technology Trends to Look for in 2014. 

The books Abundance: The Future is Better Than You Think by Diamandis and Kotler and Physics of the Future by Kaku share many of the same themes. Both books describe society as standing at the edge of significant breakthroughs across a spectrum of industries such as finance, healthcare and manufacturing. What is behind these breakthroughs, however, is fantastic and deceptively subtle – it’s all about collaboration and mashing disparate data sources together.

Consider that one ingredient for a breakthrough is the synthesis of information from seemingly disparate sources. Synthesized information is used to build novel visualizations, infographics and pictograms. Synthesized information is also used to build models, refine algorithms or test concepts. We’re seeing these visualizations, infographics and tools emerge every day. IBM’s Watson won Jeopardy but is now used to help solve complex cancer care (1). Infographics are helping interpret node traffic on the Internet and complexities of nerve transmissions in the mouse brain (2). Fraud models have been in place for years to predict patterns associated with credit misuse.

A second ingredient for a breakthrough is woven into our society – collaboration. To create breakthroughs we must work together in “real-time” to interpret, refine, challenge and ultimately make sense of synthesized information. Some industries and groups are already doing this quite well. Laboratories around the world, for example, work with genetic information and have taken advantage of portals, rich online analytic tools and real-time collaboration to build and interpret complex data sets of genetic information. Tools like Ensembl and NIH’s portal tools, as well as those provided by OMIM, give individual contributors access to each other, to infographics and to annotations of each others’ work.

In terms of 2014 trends, what does this mean?  Consider this:

We’ll see many more sophisticated tools for information synthesis. Data scientists will be part of the picture and they’ll help define and develop tools. 2014 will be a year filled with one-off online analytic tools built from Python, R and a combination of these. Python will become a powerhouse for entry to mid-level information synthesis, but R will still reign as the ‘free’ statistics tool. We’ll also see more analytics as SaaS and API models appear, based on one-off needs (like those provided by Nutonian software). You won’t need to understand the math behind the model but have a persistent need to analyze and interpret your data.

We’ll see a rise and adoption of real-time (non-email) collaboration between groups and organizations. Email will still exist, but collaboration will occur via “tagging,” “liking” and “following.” A rich collaboration landscape will be productive for those who adopt it. Dr. Eddie Obeng provides context for this transition and software companies such as Jive offer the product(s) to make it happen.

We’ll see increased use of predictive information models down at the individual level embedded in more processes. Organizations will leverage current models to build and improve their predictive capabilities. Use of these models will free up staff to focus on more “what if” development and enhance collaboration.

In keeping with Diamandis, Kotler and Kaku, what kinds of breakthrough capabilities will emerge this next year?  Here are a few of my predictions:

In healthcare and biosciences, we could see new treatments to permanently stop cancer growth through use of Histone Deacetylase Inhibitors (HDAC). The biology behind these compounds was predicted by individual laboratory information models, then discovered experimentally by global research collaboration and finally put into human tissue research treatment of HIV and other cancers.

In retail, we could see customer purchasing through predictive modeling incorporating more disparate information. This information includes tracking individual customers’ physical location in stores and shopping centers, sophisticated customer pattern recognition (head and arm positions) within stores and social media data integration (3).  Predictive models will be cross-pollinated between different customer-facing companies – from clothing to food stuffs.  Finally, predictive models developed by retail will be increasingly used for non-retail purposes.

References:

  1. Watson is helping doctors fight cancer
  2. The Connectome Project aims to map 1,200 brains
  3. Tracking Technology Sheds Light on Shopper Habits

Other Business and Technology Trends of 2014:

  1. Beginnings of a Gigantic Innovation Cycle
  2. IT Shops Will Leverage Their Knowledge of Legos® to Build Enterprise Systems
  3. The Growth of DIY Healthcare
  4. Data is the New Currency – Mining for Gold in the Internet of Things 
  5. The Emergence of the Professional DIY Data Scientist 
  6. Marketing and IT Sitting in the Tree
  7. Cloud Breaks Out of Infrastructure Groups and Into Strategic Imperatives
  8. Financial Companies Prepare to Advise Multi-Generational Homes
  9. The Re-emerging Importance of Tech Careers
  10. Responsive Web Design Falls Victim to the Hype Cycle 
  11. Data Scientist Sightings Will (Mostly) Be Proven a Hoax
  12. Non-techies Grasp the Cloud
  13. Sensors Invade – Big Data Goes Mainstreamdata