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.

The article misses out an additional critical success factor though- the lack of self-service data preparation and  self-service analytics

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:-

• gathered
• ingested
• analysed
• 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