When it comes to big data analytics, there is a vigorous discussion about how to extract value from unstructured data.
It is estimated that the majority of data makes up 80–90% is unstructured[i] and we continue to generate more. This includes content from social media sites such as Facebook posts, tweets, LinkedIn discussions, in addition to blogs and emails. You also have social networks within the enterprise, such as Jive, Yammer, Huddle and Salesforce Chatter. On top of that there is machine-to-machine data emerging from the Internet of Things.
It is accepted that better, timely access to the right information — structured or unstructured — can yield significant business benefits: greater productivity and increased revenue, reduced costs, getting more innovative products to market faster, and better customer relationships.
But what does it take to get at this information? Are we asking the right questions?
Extracting value from unstructured data is a classic big data challenge. Simply organizing information prior to using Hadoop or MapReduce can be a project in itself. Though semantic, contextual search and natural language processing (NLP) tools have made and continue to make progress, these approaches generally assume you know what question to pose. I’ll get to those questions in a moment. First, let’s look at how you can set yourself up to ask them.
When it comes to big data analytics, there are some fundamental first steps: gathering the information, converting documents and data formats to make them accessible/searchable, overcoming information silos (technological and organizational) and identifying and eliminating bottlenecks. But the real trick to extracting business value from big data analytics is bringing together the right processes, technology and people to make asking the right questions more probable. If you lay the right foundation, you’re more likely to ask the right questions.
Successful organizations — those with a high “knowledge quotient” — set themselves apart by bringing together four areas of information management: processes, technology, culture and socialization.
So, to set ourselves up for success, the questions we should be asking are:
– How can we change our processes and technology to eliminate or cut across silos of information?
– What technology might help us reduce bottlenecks to the conversion and gathering of information to make it more accessible?
– What analytical skill sets might we need, and what experiential knowledge do we already have? For example, can we better leverage our iWorkers’ experience to unlock the value in unstructured social media data?
– Do we have the understanding and organizational support to enact such a transformation?
We know that simply having more data is not the answer; we already work in a data-rich environment. Unlocking the value from unstructured data can allow us to make better data-driven decisions and realize the business benefits of big data analytics. Success depends on getting the best information at the right time to the right people. And that depends on asking the right questions.
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[1] Cas Purdy. “Infographic: Finding Big Benefits in Big Data” Trustwave.https://www.trustwave.com/trustednews/2013/07/infographic-finding-big-benefits-big-data/#sthash.ogif4DBG.dpbs