Will AI kill off the data analyst?
- Updated on: 2017-05-24
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The AI revolution is upon us and nowhere does it manifest more powerfully than in the realm of data analytics. Cutting edge developments and innovative new ways to use the immense amount of data generated daily, means that humans are no longer qualified to deal with the quantities of data produced.
The current trend for developers is to integrate AI and cognitive systems into each facet of enterprise, according to David Schubmehl of the International Data Corporation. The research director suggests that in light of the current venture capital boom in AI data analytics, it is pertinent to identify, understand and capitalise on the growth opportunities that AI will impact on the industry.
The broad advantages of AI in industry involve the ability of machine learning to recognise and appropriately respond to data flows using algorithms. This enables AI systems to automate a large portion of the analytics originally allocated to human analysts.
The traditional role of the data analyst is set to transform with this changing dynamic. No longer is it necessary to crunch numbers and sift through data. The paradigm is shifting and opportunities are emerging for those who are ready to grasp the tools.
Presently, the world is producing 2.5 quintillion bytes of data daily. Of all global data, 90 percent has been created in the last two years alone. This exponential escalation in the amount of data available has given rise to smarter tools to interpret and use the data to gain insights into business models and be able to leverage the information to promote the bottom line.
Computers don’t need to sleep or take lunch breaks, making them more efficient than their human counterparts by quite a margin. A data analyst working a 70-hour week doesn’t even begin to compare with the 168 hours a machine can accomplish. This is not even taking into account holidays and sick leave. Another consideration is that computers are cheaper than human resources.
With the advent of machine learning, computers are getting more efficient and smarter with more data available to them.
In their historic capacity, data analysts are obsolete. But with increasing digitalisation, there are emerging opportunities for them in other sectors of data analysis. The transformation in the way we process data is increasing the part machines and cognitive systems play in data analytics. The IDC states in its Worldwide Semi-annual Cognitive/Artificial Intelligence Systems Spending Guide, that the use of this technology will only increase based on statistics. Revenues are predicted to increase to $47 billion in 2020 from $8 billion in 2016 and the market for this widespread adoption of technology will grow by 55.1 percent in this time frame.
The implication for the data analyst is that they are no longer tasked with analysis as such, but rather interpretation and drawing meaningful inferences from data sets. There might be some merging of analyst and managerial responsibilities in the future.
Data analysts have the potential to become agents of change with regard to the skills they bring to the table. These include outlining possible business solutions as hypotheses for algorithmic testing, identifying outcome criteria and ensuring governance for data use. This ensures the role of the data analyst moving towards more engaging business functions.
Ultimately, there is still a place for the data analyst if they are willing to transform their way of thinking and embrace the tools available to facilitate the leveraging of data for profit.
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