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The four very human competencies that data analytics experts need.

Why do we listen to the weather forecast in the morning? To dress according to weather. When you leave home, the decision to take an umbrella or not is made easy with an accurate weather forecast: sunny, warm (no umbrella) or wet (umbrella).

Weather forecasts include the probability of rain. A 30% probability of rain sounds clear. The statistics give us a sense of certainty and control.

Sadly, most people don't know what "30% probability of rain" means. They think it means it will rain 30% of the day. The correct definition is: there is a 30% chance that a specific quantity of rain will fall in a defined area, over a defined time. That definition varies slightly from country to country.

Understanding that definition is important. Understanding it moves you along the data-information-knowledge-wisdom chain. You find true expertise and masterful decisions at the wisdom end of that chain. Experts have wisdom. They understand what the data is, know how to interpret it and know why it is important. Novices drown in data. Their only tools are speculation and guesswork.

Poor data visualisation doesn’t help anyone to gain knowledge or wisdom. A quick search on the Internet gives weather forecasts with colourful logos, interactive icons and rain radars. It all looks great. But it means very little if you don’t understand the fundamental definitions.

It's the same in business.

Many companies are jumping into big data. They hire data scientists and make huge investments in data analytics. The market for big data is overflowing with suppliers of data analytics software and systems. Visualized data is the normal end product: colourful diagrams, interactive graphs and detailed dashboards.

Data visualisation at its best connects analysis to business actions, or at least recommendations. At this level, copmanies need data scientists to be partners with executives and managers. This can be a real challenge. It requires data scientists to operate beyond their functional role and to widen their expertise. In other words, data scientists need to lead with their wisdom. The four competencies that big data experts must master are:

#1 Understand the real business problem to be solved, and the benefits to the company of solving it.

#2 Understand the main effects of the solutions on people and processes across the value chain.

#3 Know how to apply data analytics and produce high-quality results.

#4 Be expert partners – have the skills to work with managers and support higher quality decision-making.

It's easy for data scientists to do #3. This is their natural domain; it’s in their comfort zone. The real challenge is to show wisdom and become a business partner by mastering all four competencies, which includes understanding the human side of the business. The data scientists who operate at this level – the true experts – bring the real benefits to the companies they serve.

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