What started many years ago as a driver for a new generation of storage devices, the topic of Big Data is turning mainstream, having made nearly every list of “trends for 2012.” Continued media attention and buzz is making what Big Data actually is more clear. The challenge, is how get value from it.
What started many years ago as a driver for a new generation of storage devices, the topic of Big Data is turning mainstream, having made nearly every list of “trends for 2012.” Continued media attention and buzz is making what Big Data actually is more clear. The challenge, is how get value from it.
Compared with other trends that fizzled out over time or remained more academic phenomena than anything else (i.e. the Semantic Web), I believe that Big Data is for real: it really can give companies a tremendous opportunity to leverage their data for better business insight through analytics. The catch is knowing how to use it.
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More than structured information stored neatly in rows and columns, Big Data actually comes in complex, unstructured formats, everything from web sites, social media and email, to videos, presentations, etc. This is a critical distinction, because, in order to extract valuable business intelligence from Big Data, any organization will need to rely on technologies that enable a scalable, accurate, and powerful analysis of these formats.
On this point, the learning curve for most organizations is quite steep. Not only do they have to start considering unstructured information as part of their business intelligence process, but they also have to learn that extracting insight from unstructured data is a much more complex and qualitative process than traditional business intelligence.
While I think all enterprises can agree that unstructured information is important, when it comes down to the practical application of Big Data, everyone seems to revert to social media sentiment analysis. Certainly this approach has value in an overall business intelligence strategy, but I believe it’s overrated compared to other areas that can provide more strategic, big picture value. For example:
- Predictive Analytics: Using social media data, but more importantly, open source information, to go beyond sentiment analysis and identify patterns in customer behaviors, detect early product feedback and identify indicators to drive innovation.
- Brand Management: Being able to track trends between brands in similar sectors, or completely unrelated markets, in real time, is an extremely powerful tool to drive brand strategy and measure its effectiveness. At the same time, measuring how different brands relate to the intentions and feelings of its target customers is important information for designing a winning marketing strategy.
Integrating unstructured information in the business intelligence process cannot happen without a strong semantic technology, but here, the role of analysts is even more important than in traditional BI. To take advantage of Big Data, analysts have to use the elements and connections that emerge from analysis of millions of documents and be able to interpret them to distill what really matters for the enterprise.
Unstructured information is thus a significant part of the Big Data phenomena but automatic sentiment analysis is less than the tip of the iceberg on how, if effectively handled, this information can be strategic. In upcoming posts, I’ll present some real-world, concrete examples of what ‘effectively handling’, beyond sentiment, could mean for business intelligence in different sectors.