Big data analytics different from traditional BI

Tech industry investor Michael Miller recently wrote for PC Magazine that the field of business intelligence has changed with the rise of unstructured data. In Miller's opinion, the large quantities of hard-to-sort information call for an entirely new approach to intelligence, with data discovery taking the place of classic sorting methods.

Miller stated that classic business intelligence applications are useful when companies know what they are looking for in their data – when they already have a well-defined business question. Big data analytics is more important when an organization's data is not stored in a data warehouse.

The power of big data analytics, Miller stated, is to draw real-time insight from data. Sorting and storing data can be a slow process, meaning that companies hoping to draw high-speed insight can apply newly developed tools to raw information and boost their performance faster than competitors.

The difference between traditional BI and big data analytics is also present in the skill sets of users. The demand for big data talent could soon become urgent. Recruiter Greg Pankhurst told the Australian that in one or two years he expects the jobs to be in extremely short supply. He urged potential recruits to study Hadoop and other big data tech immediately.