Opinion piece by Willem Conradie, Principal Consultant: Big Data at PBT Group.
The world is abuzz with the Artificial Intelligence (AI) hype. In fact, Gartner estimated that the global business value of AI 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 AI 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 AI and using it to the business’s advantage to stay relevant as pressure grows in the competitive landscape.
However, given that AI is mostly about relinquishing control to an autonomous entity that acts and makes decisions without any human intervention, businesses remain hesitant to invest in AI, given that they are cautious of the ‘unknown’, or they simply just don’t know how to go about introducing AI practically.
With Machine-Learning (ML) being one of AI’s focal points, understanding data, for the practical and successful implementation of AI, 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 AI into the corporate world.
While data science is not AI 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.
So how can businesses achieve sustained data science given these realities?
Sustained data science requires solid governance, architecture and data engineering processes, where the value of data science lies in the complete end-to-end life cycle, which includes the ‘last mile’.
This is all about good data science governance – linked to the data science process, business and technical architecture, model management, model performance monitoring, systems monitoring, business continuity strategy trust, which is often the most difficult element.
Considering this – and given the realities linked to data science in the local market – data science is for many an acceptable way of introducing AI into the corporate world, given that it is typically performed by a person and with that there remains an element of trust.
The other option in this regard would be to succumb to the data scientist challenges and as a result ignore AI completely and hope the hype dies down. However, the lost opportunity of this outlook could be substantial.
Businesses who introduce AI, despite the perceived risks and associated challenges and who succeed, will get the first bite at the US$1.2 Trillion pie. This is where good data science governance practices play a vital part. It’s difficult to eliminate all the risks of AI in corporate environments, but it is very possible to manage it.
