Research shows the ups and downs of investments in analytics projects and the fixation on AI.
HBR article ( link at end of article) reports that " Virtually all of the respondents (97%) say they are investing in these types of projects.
Perhaps the best news in this survey is that companies continue to believe they are getting value from their big data and AI projects. 73% of respondents said they have already received measurable value from these initiatives. That number is half again higher than in the 2017 survey, which suggests that more value is being achieved as companies grow familiar with the technologies."
but back in November 2016 the Economist Intelligence Unit reported that "although 70% of business executives rated analytics as “very” or “extremely important”, just 2% are ready to say they have achieved “broad positive results”. See "Damning analysis of Analytics?"
Last week Mckinsey reported ""The biggest challenge in any organization’s analytics journey is turning insights into outcomes—what we call the last mile, which is where the value of analytics is ultimately extracted."
It went on to state "Despite huge investments in analytics "senior executives tell us that their companies are struggling to capture real value. The reason: while they’re eking out small gains from a few use cases, they’re failing to embed analytics into all areas of the organization."
It described the critical success factors to execute in order to avoid that fate. See "Scaling analytics across the business".
Whilst it is important to exploit the advantages of AI, most enterprises can only do so after they master the management of data inside their organisations. Far too much is unstructured, inaccessible and therefore not analysed. Yet- in that unstructured data lies immense value waiting to be mined and analysed. Not in a year's time with complex tools, large budgets and armies of data scientists. But today with agile cost-effective tools before your competitors old and new jump you to the gun.
AI needs massive data to recognise patterns, to enable analytical insights for better and automated decisions. Invariably for behaviour, processes and events that are predictable. Without surfacing and analysing the right data it is dangerous.
Some things never change; we are constantly urged to adopt and deploy new technologies but often by start-ups that have little knowledge of real-life, frontline business, financial services, healthcare and insurance.
It is easy to automate and apply AI to simple insurance use cases and you can see this in home-share for example. Yet, as soon as you add more complex and less predictable use cases- escape of water and storm damage for example, you see the need for human cognition to overcome the limitations of AI.
AI is not a panacea, it does not leapfrog the need for effective data management and applying it blindly leaves you blind to the true value of much of your data. The combination of artificial and human intelligence is a key to competitive advantage and anticipating disruption.
Holistic solutions rather than standalone technology is the path to success
While AI gets the headlines here and elsewhere in the world, the survey addresses both big data and AI. Terminology comes and goes, but the constant is a data explosion and the need to make sense of it. Big data and AI projects have become virtually indistinguishable, particularly given that machine learning is one of the most popular techniques for dealing with large volumes of fast-moving data. It’s also the case that statistical approaches to AI — deep learning, for example — are increasingly popular. Therefore, we view traditional data analytics, big data, and AI as being on a continuum. Virtually all of the respondents (97%) say they are investing in these types of projects.