Advanced analytics is a top business priority

"While advanced analytics have existed for over 20 years, big data has accelerated interest in the market and its position in the business," said Alexander Linden, research director at Gartner. "Rather than being the domain of a few select groups (for example, marketing, risk), many more business functions now have a legitimate interest in this capability to help foster better decision making and improved business outcomes."

IT and business leaders must expand their efforts to move their organisations from using only traditional BI that addresses descriptive analysis (what happened) to advanced analytics, which complements by answering the "why," the "what will happen," and "how we can address it".

"While basic analytics provide a general summary of data, advanced analytics deliver deeper data knowledge and granular data analysis," added Mr Linden. The rewards of data-driven decision making can be a powerful boost to business outcomes. For example, clinical diagnosis applications apply analytics across a range of structured and unstructured sources — including electronic health records data, drug information, study data, and others — to recommend optimal treatment plans for individual patients. This market has existed for over twenty years, and some have considered it to be relatively stable and mature; which, if it was ever true, is no longer so, as the concept of big data has not only accelerated interest in the market but also posed significant disruption to it. The following are key disruptive trends in this market:

However, creating value from data requires a range of talents, from data integration and preparation, to architecting specialised computing/database environments, to data mining and intelligent algorithms. "Extracting value out of data is not a trivial task," said Mr Linden. "One of the key elements of any such 'making sense out of data' programme is the people, who must have the right skills and capabilities."

Data scientists in the organisation support big data initiatives for which the No. 1 use case is enhancing customer experience. According to Gartner's latest global big data survey conducted in 2014*, 68 per cent of respondents said that they use big data to enhance their customer experience. This is the third year customer experience has been a top business problem to address.

"Data scientists are not business analytics," said Mr Linden. "They are professionals with the capability to derive mathematical models from data to reap clear and hard-hitting business benefits. They need to network well across different business units and work at the intersection of business goals, constraints, processes, available data and analytical possibilities."

Ultimately, data science is inevitable as it can help extract various kinds of knowledge from data, for example, how to acquire new customers (database marketing), how to do more cross-selling (via propensity-to-purchase modelling) and information on route optimisation, drug design and demand or failure prediction.
"Whether an organisation calls the role "data scientist" or something else, individuals (or teams of professionals) with these core skills and soft skills will prove essential in maximising the realised value of your information assets, and discovering opportunities for enhanced business performance and competitive advantage," said Mr Linden.