Why your best choice for a data analyst is a machine

Why your best choice for a data analyst is a machine

It's a machine, rather than a human analyst, that might be your best choice to deliver deeper data analysis.

We live in a world with more information created and consumed than ever before. Driven by a need to capture and understand this data, artificial intelligence (AI) has emerged as a competitive advantage for enterprises to commoditise our increasingly tech-centric civilisation, delivering a better and more consistent customer experience (CX) than their competitors.

But as organisations adapt to this new age of information, deeper data analysis and faster and more accessible insights are needed, and it’s a machine, rather than a human analyst, that might be your best choice to deliver them.

Here’s why:

1. It solves a massive skills shortage

The shift from human to machine-led data insights hasn’t happened overnight. Historically there has been a globally accepted supply-side crisis in the insufficient numbers of data analysts and scientists - a problem that the increase of AI technology in the CX landscape brings to the forefront. While these developments in technology have helped simplify and speed up processes, it hasn’t solved the lack of human resources.

In parallel with the data industry’s supply-side issues, our expectations of what constitutes ‘meaningful data’ have exponentially developed in the past decade. We are moving beyond connecting big data with a behavioral or transactional understanding of customers to generating CX insights from both structured and unstructured data.

The opportunities unstructured data provides are only set to increase; a recent Gartner study reported over 80 per cent of business relevant information originates in unstructured forms, primarily text, and Gartner predicts this will grow up 800 per cent in the next five years.

Even without the predicted 60 per cent growth of the data analytics industry, there are simply not enough resources to manage the demand itself or the level of detail we can now expect in our data analysis. The time for disruption is here.

2. It can help reduce human error and bias

In addition to the obvious benefits of significantly reduced speed and cost in using a machine data analyst over a human analyst, the absence of human factors is also worth considering. Human bias can significantly distort data and its findings even if the preliminary steps and pretext have been given. Because of the long, often mundane process of preparing and coding data, the human analyst themselves may become automated and develop an individual human bias.

The opportunity for human bias can also occur in the analysis process. Think of a complex organisation faced with prioritising multiple key decisions, each involving business cases that factor CX.  How as a senior leader can you trust that the insights and underlying data behind each business case is impartial and can be objectively compared?

As AI handles increasingly complex data and draws insights from it, it can help reduce conscious or unconscious bias or expose it through the findings. Exposing a bias gives an organisation the opportunity to recognise and lessen the impact of a bias on business actions, make decisions driven by objective data rather than personal assumptions, and identify the cognitive blindspots that may lead to poor decision-making.

3. It presents the opportunity to elevate the role of a human analyst

It wasn’t so long ago the financial services industry stood on a similar precipice. When accounting software like Xero revolutionised the accountancy market, accountants were forced to start shifting their value proposition to their clients, going from being paid for traditional bookkeeping or tax services to a position more akin with the role for ‘director of finance’ with technology now performing their previous role.

Similarly, a product like Touchpoint Group’s Ipiphany leverages machine learning and AI to do the heavy lifting of customer related cognitive analytics from both structured and unstructured data, over any interaction or channel.

In response to the quicker, more in-depth data insights that a machine can now deliver, human data analysts should be adopting a similar approach to their accounting counterparts - augmenting the AI insights with broader organisational or functional experience that adds further value to the organisation.

In fact, the partnership between human analysts and AI is already underway - a 2016 PwC survey reported that ‘analysis used to inform executives’ next strategic decision’ will consist of 59 per cent human judgement and 41 percent  machine algorithms.

Choosing a machine as your next data analyst will help save time, resources and the increasingly deep and multifaceted data analysis required to generate CX insights, but it doesn’t render your human analyst in their current form unnecessary.

The continued democratisation of AI will enable technology to do the heavy lifting so analysts can (and in many cases will have to) focus on providing real answers and insights, and at a pace previously never seen. As AI-based technologies become increasingly embedded in new systems, humans will find it easier to collaborate with machines to provide intelligent, contextual and proactive solutions - the type that have the ability to deeply transform businesses.

Mark Thompson is chief operating officer with New Zealand based Touchpoint Group.

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Tags skills shortageGartnerdata analysiscxCX technologiesartificial intelligence (AI)

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