Sundeep Sanghavi succinctly points to the dearth of data scientists being a critical reason why one-off big data analytics projects often fail. Data must be re-usable across the organisation to deliver value, better decision-making and execution.
Cognitive computing, machine learning, AI & algorithms- these can help sort the data insight "wheat from the chaff" allowing human beings to do what they do best. That is:-
Make decisions with a combination of this logical insight presented by cognitive computing and analytics with contextual insight and intuition that only the human brain can process.
Competent operational people have an asset that no data scientist or planner has; they live in the operational front-line and see reality. They can add that intuition gained from having experience the real-life situations again and again- all the blood and guts of human behavior and business, healthcare, social-care, the law courts and so on.
Self-service analytics let line-of-business people use intuition backed by factual logic. When embedded in a scalable and secure platform in which data is constantly:-
- presented in timely and relevant insights
- to all the people who need them
- on whatever device they need at the time
- from a smartphone to a massive wallboard
Despite spending billions on big data, a 2015 study shows that 43 percent of companies "obtain little tangible benefit" from their data — and 23 percent "derive no benefit whatsoever." We are on the eve of a new era of big data, where the global shortage of data scientists, and high cost, low return-on-investment of traditional “stop/start” data projects will force enterprises to reassess the way that they deal with data. Enterprises need to follow in the footsteps of companies like Netflix, Uber and LinkedIn, which have integrated data into their business models. Otherwise, they risk falling behind the competition, which can gather vital insights quicker and more effectively than they can.