The data from organisations such as McKinsey and Company and MIT is clear: companies that let data drive their decision-making make more money. Indeed, data-driven decision-making has repeatably been linked to better business performance across revenue, profit, or return on equity.
Small wonder then that Which-50 is seeing increased investment in analytics tools, as well as the emergence of a professional analytics class, the best of whom command salaries well over $200,000 annually.
Companies — and indeed whole sectors — face the challenge of disruptive transformation driven by skyrocketing consumer expectations and fueled by digital technologies. In response, Australia’s business leaders are turning to the sophisticated application of analytics for decision making to help inform and drive cultural transformation in their organisations.
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Yet, how well equipped are the current crop of business leaders to adjust to new realities, given that many of them often made their way to the top at a time when gut feel and the ability to read the winds were the hallmarks of their assent?
Which-50 asked some of Australia’s top CEOs about their views on a data-driven decision making, and their investment in analytics.
Our gut tells us they are on board with the new way of working.
According to Vivek Bhatia, CEO of QBE in Australian & New Zealand, “Access to data insights is critical when delivering change to the organisational culture and behaviour. It enables us to understand where we’re currently at, where we need to be, and how to get there.”
He told Which-50, “In QBE, as we undergo a transformation, we have been using data-driven insights both to communicate the case for change, and to steer its course. This has been very much welcomed by our employees and partners.”
Over the last 12 to 18 months QBE has expanded its data and analytics team, hiring more data scientists and investing in infrastructure and tools.
Bhatia said that embedding analytics in some of QBE’s claims processes has yielded measurable successes, and they are now working to replicate that across a range of other process domains.
“Insurance has always been a data-driven business from the standpoint of pricing and reserving, with advances in technology now making so many other business processes analytically manageable, such as risk management, claims handling, fraud analysis, underwriting, and marketing.”
He cautioned that AI should be used to augment human decision making, not replace it.
“As AI and machine learning becomes more prevalent, we need to ensure that expert human judgments and empathy are still involved in the complex decision-making processes of our industry.”
- Find out how the c-suite views analytics during the IAPA Advancing Analytics 2018 National Conference in Melbourne on the 18th of October.
For example complex risk classes, like workers compensation, commercial property and liability, marine and aviation, “can’t be fully underwritten by algorithms and automated via straight through processing. We need experienced underwriters to apply subjective judgements to evaluate such risks.”
“The same is true in fraud analysis. Algorithms can detect data anomalies, but it typically takes human expertise to investigate and uncover the behaviour that produces them.”
Analytics as a service
WPP-owned media investment company, GroupM has doubled its analytics team in the last 12 months and now has 32 analysts working on its clients’ questions and problems.
“It’s probably one of our fastest growing teams,” Group M CEO Mark Lollback told Which-50.
The former CMO of McDonald’s is a firm believer in the value of providing analytics services to clients who may not have resources or skills internally to operate their own data science teams.
“Most of our clients generally have more data than they know what to do with and they are being asked more and more questions,” Lollback said.
“If you dig deep enough, there is always amazing insight in data. Once you know that insight — and if that insight is real — it is very easy to build strategies from it.”
Boiling the Ocean
Lollback’s endorsement comes with a word of caution: informed decision making derived from data analytics shouldn’t come at the expense of speed.
“You can get almost overwhelmed by data and therefore you get almost scared to make a decision because you feel like there must be more data to mine,” he said.
“You should be really clear about what is the question you are trying to solve, and against that question what data sources could give you insight.”
Brian Householder, CEO of US-based IT company Hitachi Vantara, told Which-50 during a recent interview that when it comes to making decisions quickly, he is a fan of Jeff Bezos’ concept of Type 1 and Type 2 decisions.
Articulated in his 2015 annual letter to Amazon shareholders, Bezos writes that Type 1 decisions are irreversible — one way doors which you go through and can’t come back from. Whereas Type 2 decisions, which make up the majority of decisions businesses are faced with, are reversible — two-way doors that you can go back through if you don’t like what you see on the other side.
The Amazonian leadership principle argues Type 2 decisions should be made quickly, often with only 70 per cent of the necessary information at hand and course corrections can be made after the fact.
“As organisations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention,” Bezos wrote.
Householder says Hitachi Vantara has enabled its leadership to act on Type 2 decisions, combined with a focus on the customer’s needs and asking “does this provide value to them?”
“A lot of times you will find in a traditional business model, parts of what you are doing just don’t add any value for customers,” Householder said. “If that is the case, how do you re-adjust and how do you transform that? How do you reallocate those resources to areas that add the most value?”
For online job site Seek, its focus is on helping people live more fulfilling and productive working lives and helping organisations find the talent they require to succeed. To deliver on this, the company has made large investments in data and analytics.
“One of the core beliefs at Seek is ‘Do the right amount of thinking upfront’, which is designed to avoid ‘gut-feel’ decisions wherever possible, but without slowing decision-making or agility,” Michael Ilczynski, CEO told Which-50.
“Wherever possible, we conduct rapid experiments to generate data and learnings, so that when making decisions we can ‘discuss data rather than debate opinion’.”
Over the past five years, Seek has recruited approximately 70 people focussed on developing AI solutions and has an additional 25 AI-focused professionals in its global development team spread across its Sao Paolo, Kuala Lumpur and Melbourne offices.
A data services team is in place to capture and collate data which is fed into the AI teams, as well as a data analytics team to measure and report on Seek’s product metrics.
Through adopting tools like Tableau to make ‘business as usual’ metrics easily available and appointing internal data advocates, Ilczynski says Seek has “made great progress towards an enterprise understanding of data as a core asset.”
Don’t (completely) discount your gut
Just as analytics does not eliminate the need for human oversight, the role of gut feel can still be useful.
Kathryn Gulifa, Chief Data & Analytics Officer at WorkSafe Victoria, says gut feel shouldn’t be automatically abandoned.
“It can sometimes highlight additional data or analysis that is required to take into account certain qualitative factors,” Gulifa says.
When asked how she would approach a situation when the data is telling a business stakeholder something their gut doesn’t agree with, Gulifa said she would try to understand where that feeling was coming from and see whether it can be quantified or codified.
“If it truly is a matter of trusting gut over the data I would reinforce the statistical significance of the evidence and suggest a pilot implementation where the analytics can be demonstrated in a real way.”
Antony Ugoni, Director, Global Matching and Analytics, Seek says ability to tell a story in layman’s terms is typically the most powerful way to overcome mistrust of insights derived from data.
“Two stories always need to prepared. The first, of course, is the story found in the data. The second is the data-driven story that would disprove the incorrect ‘gut feel’, and needs to be anticipated well in advance. The attribute that needs to displayed at all times, however, is a liberal dose of humility.”
This scenario highlights another trend which has emerged in the analytics space – the need for the right mix of hard and soft skills.
For Gulifa, a good data scientist displays the synergy between mathematics, computer science and business acumen and possesses the curiosity and creativity to uncover new insights.
“The differential between a good data scientist and a great data scientist I believe is in the communication of the insight and ability to influence stakeholders to make decisions and take action based on the insight. Without the soft skills to communicate and influence, insights frequently remain admired on the shelf.”