Quick! What’s the definition of Big Data?
The answer isn’t that easy. There has been a lot of buzz over “Big Data” this past year, but it tends to adopt the abstract tone that marketers love and IT managers loathe. In order to tackle Big Data for business value, you need to shape the approach for the task at hand. One size does not fit all. For example, the approach for harnessing Big Data will change radically on the unstructured side.
Moving forward into 2013, we will see more tactical focus on how to get the desired results from Big Data, as approaches towards extracting value become better defined.
In a sense, Big Data will get smaller. Not literally, of course. But 2013 may be the year that we really separate Big Data into actionable categories. Concomitantly, there will be clearer user requirements which Big Data vendors will need to address.
A few predictions:
Greater Distinction for Structured + Unstructured Big Data
Data can be either “structured” or “unstructured,” yet this is often lost in the buzz. Most Big Data analytics described are for the structured type, i.e., machine-generated data such as transactions. Unfortunately, that’s only a small part of the data universe. Indeed, the vast majority of data created is now unstructured, reflecting human communication in email, IM, social media and the like. Much more attention needs to be given to this data that is most prevalent, has the most potential, but is, to date, the most ignored.
Better Ways to Leverage Enterprise Unstructured Data
With these distinctions about the different types of Big Data in hand, there should be ways to effectively use the unstructured data that currently accumulates in the enterprise. Compliance and legal rules already mandate that emails and related information be captured and saved. Leveraging this existing data for business use is the logical next step. Soon, this trove of human information will provide valuable intelligence in order to better understand and manage the human dynamics of the organization.
Discovering + Realizing Value of Unstructured, Big Data Within
While the value of unstructured Big Data is readily understood in the context of managing it for necessary purposes such as e-discovery, compliance and records management, the market is only just awakening to the true power and value of such information for business advantage. The beginning glimmers are apparent in the discussions about sentiment analytics, knowledge management and the like, which glean valuable information from human communications, but it’s still just the tip of the iceberg of what we call Corporate eMemory™. We see these initial sparks in unstructured Big Data analytics ignite into a full flame through the course of this year.
The Next Stage of Unstructured Data Analytics
Unstructured data currently makes up the majority of the data universe, and yet nearly all analytics are still focused on the more number-friendly structured data. However, there have been major advancements in recent years, including textual and semantic analysis, cluster analysis, similarity matching, probabilistic latent semantic indexing, natural language processing, and other powerful tools, which promise to leverage human data in ways that were once thought to be impractical or impossible. This year, the trend of rapid advancement of these technologies will move them from cutting edge into the mainstream.
Could we say that 2013 is the year of Unstructured Big Data? Perhaps. But more aptly, 2013 will probably be the year that “Big Data” loses its buzz and really starts getting down to business.
About the Author
Kon is responsible for managing all aspects of the business, including strategy, finance, sales and marketing. Earlier, Kon was co-founder and president of GigaLabs, a vendor of high speed networking switches. Prior to that, Kon was First Vice President of Mergers and Acquisitions at Deutsche Bank He was at the General Motors Treasurer’s Office in New York City, where he managed GM’s venture capital investments in high tech. He also spent eight years in various IT engineering and management positions at Burroughs, Philips and Union Bank.
Kon earned an MBA with Distinction from the Wharton School and received an undergraduate degree in Computer Science from Concordia (Loyola) University, after completing a year at the Indian Institute of Technology.
Latest posts by Guest Author (see all)
- Guest post: 3 ways to avoid the data silo syndrome - September 22, 2016
- Guest Post: ‘Virtualization 2.0′ is your on-ramp to the cloud - July 27, 2016
- To unlock Big Data’s potential, learn to use fast data - August 17, 2015