Trendspotting 2013

Dec 2012 HARO Tech Query Word Cloud by BloomThink2012 is gone and there are plenty of entertaining and profound retrospectives from Google’s Zeitgeist 2012 to your very own Facebook year in review. But it is much more interesting to look ahead and anticipate where the market will go based on the trajectory established in late 2012.  Therefore we have performed some deep textual analytics, data mining and business intelligence operations on forward looking data sets to arrive at the following conclusions.

  1. 2013 is the year business adoption of social media starts in earnest.  For many years, technology and innovation have far outpaced business adoption.  As niche players grew into success over the last 2 years, big business interest in the revenue of thos spaces grew as well.  2012 was a year of many notable social acquisitions by big firms. Facebook bought Instagram, Microsoft bought Yammer, LinkedIn bought SlideShare, Salesforce bought BuddyMedia, Google bought Wildfire, Oracle bought Vitrue, and IBM bought tealeaf to name a few.  Undoubtedly you could name others.  Such acquisitions are leading, rather than trailing, market indicators.  As the big companies buy innovation, expect them to monetize their purchases by driving those social technologies deeper into their customer lists and solution stacks.
  2. 2013 is the year organizations move from infants to juveniles along the maturity spectrum.  While social technologies enjoyed the limelight among marketers and consultants, the rest of the organization yawned and continued with business as usual.  That meant email.  Despite pockets of enlightenment, most businesses are just now dipping their toes into the ocean of social business.  Like toddlers at the beach running towards then away from the surf, they are curious about what is out there and convinced that it is amazing.  Yet, as the 2012 IBM Tech Trend Report demonstrates, they are cautious and fearful as well.  Combine a maturing business user with available technology and 2012 – the year of introduction – completed and the stage is set for businesses to do some growing up.
  3. 2013 is the year big data meets big social content and has a social intelligence baby. The match has already been made.  The sheer power of distributed compute systems like Hadoop when brought to bear on the sheer magnitude of social data produce amazing insights. But outside of some basic ERP optimization or network bandwidth allocation, most businesses have largely been left scratching their heads with what to do with all this new information.  Big data has yet to regularly produce actionable insight from all that information.  2013 is the year that unstructured content is brought into the mix.  The combination will produce a small but promising technology trend – social intelligence.  It will undoubtedly be named something else. And what we mean by “social intelligence” is not the best time to post your tweets.  Rather it is the synthesis of big data intelligence, social CRM, CXM, enterprise knowledge and unstructured data that produces the contextual lens through which business decisions are made.  The burgeoning cloud backup market is getting a handle on all the unstructured content in the business.  As they add indexing, search and sharing to their offerings  – as pioneers such as Digitiliti have already done – the availability of enterprise knowledge will become independent from the snags and barriers known today as “check in pages”.  As APIs and integrations become productized the combination of these centralized knowledgebases with big data warehouses will be tapped by enterprising reporting engines and genius data scientists.  There will be many small niche players in this area in 2013.  But those will be snapped up in 2014 and we’ll see a growth in maturity in social intelligence – or whatever it is called – in 2015.

In closing, the image above is a word cloud that highlights terms from forward looking technology queries posed by journalists looking for help writing their stories.  It covers December, 2012 and more than 7600 words.  If the news is to be believed, 2013 will be a year where business has a deep need for expertise, data, security, people who can execute (make) and information is at the heart of it all.  There is also a proliferation of smaller topics that form a milieu rather than remain on the periphery.  Taken all together, 2013 will be a year of learning to use what we have to drive insight we have always suspected was there.  Cheers!

The Accidental History of Hadoop

Creative Commons: Attribution by Flickr User Efecto; Negativo

There are two very different types of collaboration; Intentional and Accidental.  Intentional collaboration is focused by a defined team with a shared purpose.  Interactions are marked by introductions, updates, “take a look at this”, “please review…” etc.  Most “collaboration” technology fits this category.  It is boring, line cook kitchen model collaboration.  Read the recipe. Gather the ingredients. Cook, plate and serve.  There is efficient repetition but little or no innovation.

So where do new recipes come from?

The answer is Accidental Collaboration.  Accidental collaboration is time and context shifted. It subverts or ignores original intent (of authors, findings, content or audience).  It finds new uses and applications for old information.  It is disruptive, innovative and amazing.  Examples in technology include re-blgging (tumblr, pinterest), reporting, content curation, re-use, re-purposing, re-search. When information is available and accessible new insights can occur.  This is because each new re-combination of content allows different features to emerge.  A collection of events in a city becomes a holiday schedule.  A collection of medical journal articles reveals a new drug delivery pathway.

A thread of an idea that started in 1676 with mathematician Leibniz can be traced through history to David Hilbert (1882), Alonzo Church (1936), John McCarthy (1958), Dean & Ghemawat (2004), and finally Doug Cutting (2006) who stands on the shoulders of these giants to create Hadoop.  Hadoop is at the center of the “Big Data” buzz.  Big data is all about deriving insight from huge amounts of disparate data.  It is accidental collaboration.

The original intent of the data is largely irrelevant.  It’s the data, and the availability of that data that is important. Leibniz wanted to create a language that could prove or disprove any proposition.  Hilbert came along to challenge that idea.  Church created Lambda calculus to prove that Hilbert’s challenge was actually unsolvable.  McCarthy used Lambda calculus to create LISP.  Dean and Ghemawat used LISP programming ideas to create MapReduce.  Cutting read their research and combined MapReduce with Lucene to create Hadoop.

Just as McCarthy never worked on a project team with Church to create LISP, the content Church created for Lambda Calculus was indispensible in helping McCarthy create the programming language.  Similarly, the ways in which LISP was created directly influenced Dean and Ghemawat at Google to create the map & reduce capabilities that allow massive distributed problem solving.  From that inspiration, a lot of hard work, and some help from Yahoo!, Hadoop was born.

The men involved, the content and approach were all in different eras, but they came together to create something special, innovative and impactful.  If that information was not available or accessible, Hadoop (and all the applications that rely upon it) would never have happened.

Accidental collaboration throughout history has been incredibly slow.  Modern information management technology like hadoop or active content archives can speed it up and deliver to us amazing insight in incredibly short periods of time.

This post originally appeared on on July 16