The world is abuzz with the () hype. In fact, Gartner estimated that the global business value of will reach US$1.2 Trillion in 2018 – this is more than the GDP of most countries in the world.
With Gartner categorising the sources of business value as enhanced customer experience, new revenue, and cost reduction, one has to wonder why the world is not ‘falling head over heels’ for and using it to the business’s advantage to stay relevant as pressure grows in the competitive landscape.
However, given that is mostly about relinquishing control to an autonomous entity that acts and makes decisions without any human intervention, businesses remain hesitant to invest in , given that they are cautious of the ‘unknown’, or they simply just don’t know how to go about introducing practically.
With Machine-Learning () being one of ’s focal points, understanding data, for the practical and successful implementation of , has become more critical than ever. This needed focus on data in turn means that businesses are seeking to invest in the data science role and ironically the rise of data science is in fact leading the practical introduction of into the corporate world.
While data science is not itself, it is top of mind for every ‘data’ driven business because it is the data scientists that actually ‘teach the artificial engine to become intelligent’ – through statistical descriptive, predictive and prescriptive models.
Yet, the challenge in the local market is that the data scientist skill set is very rare and for many corporate businesses simply doesn’t exist. In fact, the ‘unicorn’ data scientist is rarely found, and if found, is generally unaffordable to most.
This reality is forcing businesses to employ graduate, or lesser experienced, data scientists straight out of universities. However, this affects a business’s ability to implement the data science needed in an operational environment for sustained benefit (often referred to as the ‘last mile’ in data science).
In fact, very few businesses succeed at deploying ‘sustained’ data science as while graduate data scientists are highly educated for their trade, they tend to not have the necessary experience to deploy the data science operationally.[…]