Driven by commoditization, the source of competitive advantage for many of today's organisations has been reduced solely to delivering better and more consistent customer experience than their competitors. But when designing products, services and processes and transforming your organisation to deliver a consistently superior customer experience relies on data analysis, and faster and more accessible insights – what happens when artificial intelligence (AI) is able to match, or even surpass, the human data analyst in your team?
Technology is already transforming data analysis at a rapid pace. Analyst firm IDC predicts the worldwide content analytics, discovery and cognitive systems software market will grow from $4.5 billion in 2014 to $9.2 billion in 2019 [source], while data-driven decision making roles are beginning to be supported by an AI. IBM’s cognitive technology Watson has come a long way since beating Gary Kasparov at chess, currently putting its analysis skills to broad outcomes from personalising cancer treatments for US hospitals to suggesting smog reduction strategies to China [source]. In Japan, a local insurance firm recently replaced staff with an AI system (based on Watson Explorer technology) to determine insurance payouts based on comprehensive data analysis of the patient’s medical history, records and past procedures [source].
WHAT CONSTITUTES MEANINGFUL DATA?
Our expectations of what constitutes meaningful data are also rapidly developing. With more data than ever before to draw from, we are moving beyond connecting big data with an understanding of customers at a transactional or behavioural level to generating experiential customer (CX) insights, in order to identify the root causes of and opportunities deep within products, interactions and processes. 80% of business-relevant information originates in unstructured forms, primarily text and Gartner predicts this will grow by 800% in the next five years. If you can’t lay your hands on subtle but critical drivers of both poor and excellent experiences across your customer groups, chances are your organisation will struggle to win and maintain their share of the market or generate satisfactory margins from your efforts.
While historically there has been a globally accepted supply-side crisis in the insufficient numbers of data analysts and scientists, the increase of AI technologies in the CX landscape whilst helping, also brings a greater problem to the forefront – that the role of the human analyst will be forced to adapt to a different organisational need.
IS AI TAKING OVER?
Currently, the role of the analyst is often disconnected from the day-to-day machinations of the business and market, because the career path for an analyst doesn’t generally include several years in the frontline sales, marketing, operations and finance roles that give people the experience, context and perspective to generate actionable insights from data. And while current human analysts may be sourcing, organising, grouping and mapping data to search for underlying trends and patterns, these are all tasks that can now be effectively undertaken by an AI.
To avoid their role disappearing to seasoned business professionals using AI…human analysts should be looking to acquire broader organisational or functional experience to add value to AI generated precursors to insight.
Many analytics professionals are ill-equipped to deliver value in the very near future – but that doesn’t spell doom for the profession itself. Instead, analysts should be looking to examples of similar disruption. Take accounting software firm Xero; while the technology has revolutionised the accountancy market, instead of being paid for traditional bookkeeping or tax services, many accountants have shifted their value proposition to their clients to that more akin with the role of ‘Director of Finance’ [source]. To remain relevant, accountants and accountancy practices are being forced to reposition themselves much closer to the intricacies of their clients and to being ‘trusted business advisors’. That aforementioned business understanding will also be an advantage – so taking on different responsibilities within an organisation or becoming an industry or subject matter specific analyst will be important to futureproof roles.
To avoid their role disappearing to seasoned business professionals using AI, as organisations expect quicker and more actionable insights and predictions from analysts and less ‘well-organised data and facts’, human analysts should be looking to acquire broader organisational or functional experience to add value to AI generated precursors to insight. Ai is shortening the supply chain between raw customer data, actual experiential customer insights and business decision making. Analysts should look to move past their current responsibilities in the data analysis process to help contextualise and humanise both the data and decisioning that an AI delivers.