The Semantics of Big Data

I had the pleasure of attending the Big Analytics Road Show in Boston this week. The presenters and sponsors did an outstanding job of describing the “big data” ecosystem. They even offered clear descriptions of Hadoop and MapReduce for non-technies, which is quite an achievement.

The most rewarding aspect of the day’s program, however, was its emphasis on how the data can be used to add value to business decisions. Consequently, the focus wasn’t on acquiring massive quantities of data (although zettabytes and yottabytes were mentioned!)—or even on the value of organizing big data sets. Instead, the program provided many examples of how analysis of structured and unstructured data in tandem can lead to new insights that can improve business processes and marketing decisions.

Years ago, at InfoCommerce Group we coined the phrase “data that can do stuff” to describe the advantages of well-designed data products. In essence, a data product that is designed to meet a defined need of a target audience becomes a decision tool when analytics are applied. With the era of big data upon us, even textual data and real-time streams of behavioral data can be leveraged via semantic and pattern matching technologies to obtain data that can do stuff. Furthermore, the different types of data can be overlaid to achieve higher levels of insight into customer behavior or patient outcomes, for example.

The takeaway point: data analysis tools and techniques that used to be available only to big life-science companies and search engines are now entering a phase where the costs make the technologies more widely accessible. However, as someone mentioned at the Big Analytics event, Gartner Group places big data at the peak of inflated expectations on its hype cycle curve. I agree with Gartner because of the level of noise surrounding big data. Nonetheless, with proper alignment between the data, business goals, and execution, opportunities to benefit from big data—or should I say big analytics—exist today.

Comments

[Mike Reid]

Janice,
Excellent article. I really like the distinction you made about big analytics!
Mike