Data And Analytics - Do Culture And Trust Translate Into Value?

By Neil Currie, Head of Data Management, Crowe

Neil Currie, Head of Data Management, Crowe

Clearly, the relatively recent explosion in computing power has unlocked the capability and opportunity for firms to leverage AI and machine learning technologies, as a key part of new data and analytics strategies.

Culturally, our business experience conditions most of us to associate “data and analytics”, with facts, careful analysis, verification and confirmation of the inputs being used in support of decisions. However, despite the stated aim of many firms to be ‘data-driven’ and the technology investments they have made, when a carefully researched solution is presented to a decision maker we often hear “that wasn’t what was expected, are you sure?” being asked in relation to the proposed outcome. When the result of logical analysis isn’t what people expect, they often revert to a paradigm where what feels right, or their ‘gut instinct’ prevails as the basis for their decisions.

This reality means that for firms to succeed in being ‘data driven’ and for their technology investments not to be wasted, they need to recognise the cultural changes that will be required of its people across all its business functions. Firms certainly need to consider and communicate to their people: how these new technologies have been deployed and are to be embraced; how responsibilities, decision making and governance processes will change as a result; and how critical all this may be for firms’ future success, growth and in some cases survival.

For a number of years various academics, analysts and private equity firms, amongst others, have talked about how markets should value the data that businesses hold. With modern “giants” such as Facebook, Google, Amazon and many others now harvesting daily enormous amounts of data from individuals’ interactions on the web, via mobile etc these discussions around value will only intensify.

Trust is already being traded by many customers in return for convenience, their wish for personalisation, and their demand to be served what they need, when they need it

Increasingly, in addition to the cost of its replacement and the cost of storing it, valuation methods proposed for data have included the revenue lost from not having access to the data and the future commercial opportunities from its use. Values ascribed to these latter valuation elements are difficult to justify, not least because the impact on future customer trust and relationships from firms’ use of that data, and what that translates into for firms commercially, is still largely based upon uncertainty and guesswork.

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In a future world where models play a key role in supporting decision making by firms, how important is it for those firms to understand how their customers’ trust will be established and maintained? The new landscape is far from simple. Trust is already being traded by many customers in return for convenience, their wish for personalisation, and their demand to be served what they need, when they need it. For some individuals, there is an explicit and willing acceptance that more of their information than they might otherwise be comfortable with is already circulating relatively freely and being used in support of firms meeting their personal wishes and demands.

A simple facial recognition scenario is useful in this context – how would each of us feel if, when we stopped to pick up lunch at a shop which is part of a chain that we occasionally visit, but not at the same location, the shop assistant were to say “Hi, not taking the banana today?” In this scenario, information gathered as a bi-product of our previous shopping patterns and captured within the chain’s systems, is linked back to us via our facial image. Presumably in the interests of supporting increased personalisation for the individual (and with the added potential commercial upside that this brings to the chain), this use of data becomes the basis for an interaction with a person that we don’t know, we have never met and with whom we have no direct relationship. In contrast, if this scenario played out at the local coffee shop, where we pick up our coffee every morning, if the barista were to ask us if we wanted “the usual” we would probably feel complimented that they remembered who we were?

Has customer trust already become inconsequential in firms’ evaluation of a use case for more widespread use of data and analytics? Culturally, as individuals we already appear to have accepted that firms may get it wrong in their enthusiasm to justify and deliver more advanced use of technology. This is evidenced in recent publicity around the banning of facial recognition in some US cities and the recent scrutiny around its use in the Kings Cross development in London.

Fact - all data models and algorithms contain bias, are open to manipulation, are created by people who have natural biases, are subjective in their decision making, and who can make mistakes. Well-established data governance principles suggest that data attributes such as quality, accuracy, completeness, timeliness, and relevance will influence value for firms in this context. However, the emerging environment is one within which use of data and analytics by firms is increasingly driving, rather than just enabling, their business decision making. Those firms that can best and most quickly understand and manage both the new dynamics around customer trust and new organisational cultural aspects are more likely to gain significant competitive advantage and realise significant commercial value.