Predictive data analytics knowledge paired with business know-how paves the way to success in modern business, according to EX-analyst and founder of the Data Science Institute, Kevin McIsaac.
By mastering the ability to predict business outcomes and derive business acumen from data, partners can become equipped with notable value-added skills as McIsaac highlights that it is this insight that businesses truly desire.
McIsaac referenced Gartner, stating 70 per cent of companies do not generate the expected business value or savings from their big data infrastructure and as a result, the world of hardware and storage has become commoditised.
“If you really want to be relevant to a business, you have got to move up and start asking yourself what you can offer in terms of predictive applications. You should question how you help a business predict the outcome of an event,” he advised.
“If you can approach a business and stop talking about how many petabytes of storage a business may need and start talking about how you can help a business identify its most valuable customers and pick out which ones are most likely to respond to a cross-sell opportunity, then you will sell a lot of expensive storage and consulting services, but you have got to lead with a business opportunity.”
He added that the modern world of upcoming companies is around predictions and traditional enterprises need to understand lead scoring or churn prediction, which is very important particularly in the telco space.
“Spend more time thinking about the high level use cases and business problems and less time looking at technologies like Hadoop or Spark,” he added.
According to McIsaac, there are three skill sets that make up the criterion of a data scientist including a mathematics, science and programming background paired with business intelligence.
“You need to be able to sit down with a business person and speak in their language. You also have to be able to take what used to be a very complicated piece of implementation and come back in simple terms to describe it to the business,” he said.
McIsaac added that the commoditisation of technologies and infrastructures today that are required for data science practice has meant that there is a large scope of opportunity for smaller consultancy firms.
“The techniques have always only been available to larger companies but since around 2010 or 2011, the machine learning algorithms and the technologies and infrastructure needed to run it has become more mainstream.
“I use a commercial product, I write in python and I run it all on Amazon [AWS] servers and they only cost me 5c an hour so in total I might only spend 25c a day on my computing,” he said.
