To celebrate being named one of the world’s top 100 data leaders, Evan Stubbs, Partner and Director, Data and Digital Platforms at Boston Consulting Group, talks about his plans for 2020 and the future of data and analytics
What would you say were your greatest professional achievements of 2019?
The last few years have seen an explosion in the breadth of technology, capabilities, suppliers, regulatory frameworks and trust expectations surrounding the ability to create value from digital and data. However, this growth hasn’t been evenly distributed.
Different geographies have different peculiarities which greatly impact strategy and execution.
So, 2019 was exciting for me because of the opportunity to successfully work through how these factors play out in geographies as diverse as China, India, the Middle East, Australia, New Zealand and Europe.
How will you build on those achievements over the next 12 months?
The continued expansion of these factors between geographies presents constant opportunities to apply learnings from elsewhere and actively look for opportunities to do things differently.
Deliberately cross-pollinating opportunities between industries and policy areas is another area of focus, especially given the increasing focus on the role data and technology plays in trust and engagement, both in the private and public sectors.
What are the key challenges organizations will face this year when applying data, analytics or AI in business contexts?
The biggest challenge organizations will need to consider this year will be how to find the right balance between profit capture, value sharing, trust and transparency and regulatory pressure.
While part of this conversation has to do with ethics, the implications are far larger. Technology and data can and are creating massive amounts of value. But there are varying perspectives on the appropriate distribution of that value between individuals and organizations.
These range from micro considerations, such as the degree to which data-informed behavioral economics should be used to build positive or negative habits, right through to the ongoing macro impact of automation on the workforce of the future.
Failure to get these things right will have significant strategic implications, ranging from outright brand damage through to broad-based regulatory reform and macroeconomic impacts. They are naturally major considerations for both boards as well as governments.
Overcoming these challenges requires articulating clear principles that reflect the desired culture and norms. Data leaders must set and communicate clear strategies and putt the right governance and ownership frameworks in place to encourage good decision-making during ideation, development, implementation and execution.
How do you think the role of the CDAO is changing? And what’s driving these changes?
I think the general pressure is to shift the role of the CDAO from a functional role to a more strategic one.
The role was once predominantly centered around being either the Head Data Scientist or the lead for better information management, both largely responsible for executing the strategy. But conversations are moving more towards seeking input into how technology and data will create value and how growth capabilities such as quantum [computing] and AI might create strategic value or optionality.
Much of this is being driven by board-level concerns around an inability to get commercial answers to these questions from other areas of the firm, despite the clear recognition by most that they will transform and disrupt business models.
How you think the way organizations use AI technologies will evolve over the course of 2020?
AI is on a general trajectory to become broader, more generalized and easier to use through 2020 and beyond.
Most applications in production are still solving relatively narrow problems across fairly narrow channels. But the global leaders are already pushing the breadth of these solutions through a combination of agile software development practices, making smarter use of diverse technology ecosystems and drawing from wider, deeper and more diverse data.
The main evolutions through 2020 will be an increase in the growth of embedded-but-narrow AI systems within packaged applications, along with the competitive pressure to increase the width and generalizability of bespoke AI applications. Classic predictive modelling has become table stakes in most industries within the developed economies.