INSIGHT: Why Big Data isn’t suited to analytics

Big Data is not intrinsically suited to analytics. I’m sorry, but it’s true.

Distributed storage and massive scale can make data hard to find. Lack of normalisation and structure means an analyst often has a lot of hamster-wheeling to do just to know what the data is — much less combine it with other sources.

Add to this the challenge of simultaneous batch and streaming processing and a legacy of marketing analysts who are whizzes with Adobe and Google Analytics but not SQL, much less statistical programming languages such as SAS and R.

Advanced analytics for marketing is not synonymous with Big Data, and vice versa. Adoption of pure Big Data analytics, such as MapReduce/NoSQL engines, remains below 5 percent across most industries and company sizes, according to Gartner’s 2014 survey of analytics spending intentions.

A recent Gartner survey found that 80 percent of analytics use cases still require a traditional data warehouse.

Early exceptions are seen in media, services and communications industries, which have been aggressive in building out marketing analytics teams and staffing up centres of excellence, according to Gartner’s Survey of Data-Driven Marketing, 2015.

Many analytics techniques can and do make use of Big Data stores, generally by transforming it into structured or semistructured formats first. These include: