By Jeremy Shayler, Business Development Director at LexisNexis Business Insight Solutions – Middle East
Evolution has ‘programmed’ us humans to look for and identify signals amidst the noise in our environment. Now that this environment is made up less of sabre-tooth tigers and velociraptors, and more of petabytes and zettabytes of data, we face a new challenge: how to quickly and efficiently work out what matters and then decide on a course of action.
We continuously create an abundance of data in our day-to-day lives and governments, businesses and other institutions are getting ever more sophisticated at collecting and analysing that data – to learn more about us, to anticipate and predict behaviour, to influence us to make certain decisions to buy, to vote, etc.
In many ways, the phrase Big Data itself is no longer the main topic of conversation. It is now more about application and business focus. How do you manage, measure and monetise your data and information assets?
How does Big Data translate into better, more accurate and timelier business decisions? The more the Big Data space matures, the more its focus will be on the distribution and availability of small data, wherever, whenever and in whichever context it is required.
In marketing, for example, the problem has always been to reach the right audience with the right messages through the right channels at the right time for maximum impact. It has never been more difficult and, at the same time, more straightforward, to solve this problem with data.
It is a ‘delightful paradox’, starting with asking the right questions, applying the right filters, picking the right tools from the hundreds available and understanding how to harmonise, blend and weight discrete and diverse data sets appropriately.
To do this right, we need the right answers to these questions:
How do we make data valuable?
How do we make it understandable and turn it into insights that lead to better business decisions?
How do we ‘crack the nut’ with an intelligent nutcracker, instead of a dumb sledgehammer?
And how, ultimately, we achieve that effective symbiosis between the best of machines (doing the grunt work of data processing) and humans (adding the cognitive reasoning that turns Big Data into small, relevant, business-critical insights).
The human factor. Data alone will not bring better answers to ever more complex questions. The future belongs to ‘strategists with algorithms’: let the machines do the math and humans add meaning and understanding. Tools and data are essential, but ultimately it is still humans who think, make, market and sell.
Most people present data as if it was an insight, but it may just be casual observations rather than the holy grail of genuine, data-driven insights.
It starts with being smart about finding the bits that matter: first, be really good at finding stuff (Boolean) from available content; interrogate data sets; search and find, then analyse.
The key is making it all understandable – understanding what can it do for you in business and splitting Big Data into chunks of relevant small data. Big Data is a jargon and means something different in every possible context.
It needs adequate definitions. The process is always the same: get immersed in data; filter what’s important and what isn’t; analyse (machine and human); and ‘glean actionable insight’.
Consider measuring social media impact, for example: only automated tools can do the grunt work of data processing.
Tools help make decisions about relationships between data sets, but it takes humans understanding to add the reasoning and interpretation element.
The challenge for PR
For marketing, the focus is increasingly on customer experience. The lines between owned, earned and paid media are blurred, and expectations of marketing outcomes from public relations activities are higher than ever.
It is no longer sufficient to earn media placements, to distribute press releases and to manage the social media profiles of brands. Companies increasingly expect PR to perform with tangible and measurable marketing impact.
Big Data in sport – the Moneyball principle
A good example is the Moneyball approach, in reference to Michael Lewis’s bestselling 2003 book about to the success of the Major League Baseball team, the Oakland Athletics (aka the Oakland A’s), and how their manager Billy Beane and his coaching staff brought a rigorous, data-driven approach to a sport where recruitment was done by gut feeling and rules of thumb that were informed by the most salient data points: such as who can smash the ball out of the park most regularly.
The story of the Oakland A’s is proof that data analytics and evidence-based decision making can beat the collective wisdom of experts (and even be the topic of a Hollywood movie, released in 2011, with Brad Pitt as Billy Beane). In 2002 and 2003, the team had one of the lowest salary bills in Major League Baseball. And yet, they managed to recruit players with skills that took them to the play-offs in 2002 and 2003. But as other teams catch up, the principle gets copied. Everybody can potentially benefit from the democratisation of data.
The 3, 4, 5 Vs. Back in the early 2000s, Meta Group (now part of Gartner) defined Big Data initially by three main characteristics, the 3 Vs: volume, variety and velocity of data. IBM and others added a fourth and then a fifth V: value and veracity. In our age of fake news and alternative facts, it is easy to see why and how these two are now inextricably linked. Without veracity, data has no value. Therefore, the success of global fact-checking initiatives, championed by the big social media networks as well as traditional media companies and other organisations, will be critical for the continued business success of Big Data.
PR and uncertainty
PR Week’s 2017 Power Book has the movers and shakers of the PR and Comms industry commenting on the big opportunities for PR this year – and the top result was The Era of Uncertainty and thus the opportunity to help clients grow amid the chaos and uncertainty that we are witnessing, day in and day out. Other key themes discussed included opportunities in content creation, influencer marketing, the fast-changing media landscape and challenging ’fake news’, as well as measuring the impact of PR and the desire to grow more boardroom influence.
Data and decisions under uncertainty. So Big Data is driving ever more elaborate decision-making algorithms and processes, fuelled by exponential growth of data volumes.
At the same time, the challenges around fake news and alternative facts have created a sense that we are faced with more uncertainty than ever. A source of practical answers is the discipline of behavioural economics and, in particular, the foundational work of Daniel Kahnemann and Amos Tversky (see The Undoing Project by Michael Lewis, the author of Moneyball).
As we learned from the work of Kahnemann / Tversky and many others since, we humans are not good at all at making judgements under uncertainty. We seek (and then overrate) opinions that are aligned with our own and we ‘auto-filter’ dissonant views. That goes a long way in explaining how pollsters have been struggling with recent events (Brexit, Trump).
When we start looking a little more closely and we consider the dynamics between the mainstream media and social media, partisan channels and platforms – the likely repercussions of an echo chamber effect – then, of course, we are looking at the psychology of processing and selecting information, of forming and confirming beliefs. And of behaviour based on those beliefs.
According to Information Week, the Big Data analytics market alone will have exceeded $200 billion by 2020. A more profound understanding of the very human side of Big Data will be the key to unlocking this enormous potential.
The article appeared in the October 2017 issue of Gulf Marketing Review.