Whether its fine-tuning supply chains, monitoring shop floor operations, gauging consumer sentiment, or any number of other large-scale analytic challenges, big data is having a tremendous impact on the enterprise.
The amount of business data that is generated has risen steadily every year and more and more types of information are being stored in digital formats. One of the challenges entails learning how to deal with all of these new data types and determining which information can potentially provide value to your business.
It is not just access to new data sources, selected events or transactions or blog posts, but the patterns and inter-relationships among these elements that are of interest. Collecting lots of diverse types of data very quickly does not create value. You need analytics to uncover insights that will help your business.
That’s what I have tried to post over here. Big data doesn’t only bring new data types and storage mechanisms, but new types of analysis as well. I hope I have done enough justification in giving enough go-through on the various ways to analyze big data to find patterns and relationships, make informed predictions, deliver actionable intelligence, and gain business insight from this steady influx of information.
Big data analysis is a continuum, not an isolated set of activities. Thus, you need a cohesive set of solutions for big data analysis, from acquiring the data and discovering new insights to making repeatable decisions and scaling the associated information systems for ongoing analysis. Many organizations accomplish these tasks by coordinating the use of both commercial and open source components. Having an integrated architecture for big data analysis makes it easier to perform various types of activities and to move data among these components.
I am actually not feeling well… So I am gonna wrap it up for today, Maybe I’ll come over here later and edit maybe…