For my first post, I find the best place to start is by defining the subject matter we wish to talk about. So lets get started, what is analytics anyways? How is it different than traditional Business Intelligence? and why has it come back into focus after being dormant for so many year?
a Definition: Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry.
Historically, Analytics was heavily used in banking for portfolio assessment using social status, geographical location, net value, and many other factors. Today, Analytics is applied to a vast number of industries and is re-emerging due to the phenomenal explosion of data from our connected world.
With this explosion of data, we now see analytics re-emerging as a topic and instead called “Data Science.” In reality, analytics has been around a long while and this new breed of analysts are re-branding to garner higher salaries. With the advent of low cost and open source databases we’ll see analytics penetrate deeper into traditionally less analysis focused industries. Leading the pack is Apache Hadoop, primarily due to the aforementioned low cost and ease of scalability. Big Data consists of data sets that grow so large and complex that they become awkward to work with using on-hand database management tools. McKinsey Global Institute estimates that big data analysis could save the American health care system $300 billion per year and the European public sector €250 billion.
Many industries have already adopted or are in the process of adopting a Big Data platform in their organization and the time has come to start discussing some simple analysis to leverage this vast amount of information.
I humbly submit this blog to discuss Big Data Analytics tooling, a range of Analytics Topics (Customer Retention, Online Marketing, Behavioral Analysis, Customer Valuations, and more).