2014: The Year of Data Disruption

542192_61276739Linguist Geoff Nunberg’s annual “Word of the Year” posts offer an instructive peek into the American psyche. In 2012, he chose “Big Data”. In 2013, his pick was (no, not “twerk”) “selfie.” Nunberg makes his selections based on dominant news stories, or words that he believes tell us something important about the culture at a particular point in time. What appeals to me about the 2012 and 2013 choices is that they illustrate the increasing tension between our fascination with data, and our profound unease at its implications.

This plays out from pop culture to organizational culture, from The Economist to TMZ. In 2014, I’ll be looking at the increasing tension in several areas, as technology continues to overtax our ability to understand it (sentiment, video and image analysis) assimilate it (filter failure), act on it (business disruption) and define rules and ethics around it (security and privacy). Here’s what I’ll be thinking about throughout the year:

1. Data Diversity Requires Diversity of Expertise

The biggest “Big Data” challenge will continue to be the sheer variety of data types. Large brands want to know when their products or logos are used on the social web. Sentiment analysis, image recognition in both still and moving images, as well as text-to-speech and speech-to-text will continue to confound technologists, until and unless they more aggressively include linguists, social scientists, even neuroscientists, in their R&D processes. That isn’t to say that will solve everything, but as we bring technology and human communication closer together, it stands to reason that we need a far more multidisciplinary approach to understanding signals.

2. Clean Data is Happy Data 

With multiple data types comes increased demand for consistent interpretive standards, particularly as the need to view disparate data sets in tandem increases. We’ve seen the challenges of this with text-based social data but have not even scratched the surface for other data types, or the impact when they are viewed in conjunction with other data sets. Consistent sourcing, transparent methodology and interpretive standards will become a must-have for 2014. It may not be sexy, but it’s mission-critical.

3. Machine Learning is Table Stakes

The ability to deliver ever-more massive and heterogeneous data streams from devices, enterprise and social apps and other sources–often in real time–will place increasing pressure on organizations. Rather than continuing to segregate analysts, hand-code posts and manually interpret these data sets, machine learning will need to become an expectation rather than an exotic and costly addition to data analysis tools. We’re not talking Scarlett Johanssen in “Her,” (sorry, folks) but rather the ability to infuse learning into data processing technologies to reduce filter failure, improve relevance and move to higher-order analysis–at scale.

4. Data is the New Disruption

As data makes its way around increasingly permeable organizations, we’ll see  waves of disruption follow in its wake. While corporate initiatives can spark quite a bit of controversy over “who owns it” and “who funds it,” data is so elemental to organizational culture and operations that these questions will predominate. The next wave, “who gets to see it, interpret it and administer it” will only increase the need for direct, timely and clear agreements and governance as these data streams become business critical.

5. Contextual Privacy: the Useful/Creepy Conundrum

There has been too much of an inclination to treat privacy as a one-size fits all proposition, but what we are learning is that the complexity of data gathering and data sharing means that privacy can be a very situational concept. Thinkers like Danah Boyd deeply understand the contextual nature of privacy, and how one small adjustment can erode or even build trust. I’ll be focusing on this in 2014, with an emphasis on helping organizations and technology developers deliver relevant experiences without undermining the social contract between individual and organization.

This is one in a series of posts on Altimeter Group’s 2014 research focus. For more from my colleagues on what they’re planning for the year, please click here.

About susanetlinger

Industry Analyst at Altimeter Group
This entry was posted in Altimeter, Big Data, Predictive Analytics, Quantified Self, Real-Time Enterprise, Research, Social Analytics, Social media measurement, Uncategorized and tagged , , , , , . Bookmark the permalink.

9 Responses to 2014: The Year of Data Disruption

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  9. Kent Langley says:

    Excellent post Susan! I particularly find interest in the concept of “increasingly permeable organizations” and how they will deal with the not so black and white world of inside or outside the firewall. There is now a significant space in-between that is hybrid not unlike the hybrid cloud in some ways I expect. Of course, you know I’m bullish on the concept of “the impact when they are viewed in conjunction with other data sets.” So many possibilities! 2014 should be a very exciting year all around.

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