Transcript
Speaker: This is HSBC Global Viewpoint, your window into the thinking, trends and issues shaping global banking and markets.
Speaker: Join us as we hear from industry leaders and HSBC experts on the latest insights and opportunities for your business.
Speaker: A heads up to our listeners that this episode has been recorded remotely, therefore the sound quality may vary.
Speaker: Thank you for listening.
Speaker: Thank you for joining us today.
Speaker: Welcome to the new normal, adopting emerging technologies in the current pandemic and beyond webinar.
Speaker: The agenda for today will be welcome remarks by Priya Kini, Head of Global Banking, HSBC Singapore.
Speaker: There will then be a discussion moderated by David Koh, Head of Global Liquidity and Cash Management, HSBC Singapore.
Speaker: With our esteemed panelists today,
Speaker: Eric Tachibana, Amazon's Web Services, Global Advisory Head of Asia-Pacific, Japan and China.
Speaker: Over to you, Priya.
Speaker: Thank you.
Speaker: Thank you, Ghazala.
Speaker: Welcome, everybody, for joining us today.
Speaker: In just a few months' time, COVID has brought about years of change in the way all of us, all companies, all institutions in all sectors and regions do business.
Speaker: Three key trends in my mind have changed
Speaker: accelerated.
Speaker: They were already in motion, but perhaps they have accelerated in the last six months or so.
Speaker: Firstly, consumers have moved dramatically towards online channels and companies have responded in turn.
Speaker: And what I think is more surprising is the speed with which companies who didn't have a digital presence have created and enhanced their digital offerings.
Speaker: Secondly, the customer facing elements of organizational operating models are not the only ones that have been affected.
Speaker: In fact, what we see is across all our clients, there's similar acceleration in the digitization of core internal processes in operations such as back offices, treasury, production lines, R&D processes, everything.
Speaker: and how companies are looking at their own supply chains, there's much more digitization in that as well.
Speaker: And finally, of course, like everybody likes to say now, data is king.
Speaker: Along with this multi-year acceleration of digital, this crisis has also brought about a sea change in the way executives are approaching technology and thinking about the role of technology in their business.
Speaker: In a McKinsey survey, which was carried out in 2017, nearly half of the executives who were interviewed ranked cost savings as one of the most important priorities for their digital strategies.
Speaker: When McKinsey repeated the survey last month, more than half the corporates now say that they are investing in technology for competitive advantage.
Speaker: or refocusing their entire business around digital technologies.
Speaker: This is the new normal.
Speaker: Sitting here in Asia, we see the leap of technology is even higher here than probably in many other parts of the world.
Speaker: And as an Asia-focused bank, we are at the forefront of enabling our clients to make these changes here in Asia quickly and efficiently
Speaker: And we continue to invest in new technology-enabled solutions across areas like payments, collections, trade finance, treasury, risk management, in fact, the entire gamut of banking services.
Speaker: Eric, because all this data, I mean, are you seeing any data management trends that companies are adopting?
Speaker: Yeah, I would say there's three things that I would be looking at.
Speaker: The first trend actually is a little bit humbling because I completely forecasted it wrongly three years ago.
Speaker: So originally, when we were setting up our AI MLM,
Speaker: practices in Asia roughly about three years ago, I had this kind of mental model about how I thought we would be scaling up data management consulting business.
Speaker: And my mental model was that I thought that we would need to hire three sorts of consultants for the market.
Speaker: The first one would be data architects.
Speaker: who would basically be building the solutions and these would be making up 60% of who we would hire and who we would then sell out to customers.
Speaker: Then the second group would be data scientists.
Speaker: So these are the folks who are building the models, the algorithms, which Arun was talking about, they'd be building those models on top of that technology infrastructure.
Speaker: And I thought that would be about 30% of our hiring.
Speaker: And then we'd have business analysts who would be working with the customers to sort of apply those algorithms to business problems.
Speaker: And I thought we'd be hiring about 10% of those.
Speaker: Well, the reality, actually what happened was completely the opposite.
Speaker: So what our customers needed were the business analysts who
Speaker: So they actually had a bunch of data architects, legacy data architects, who they focused on upskilling, getting certified in AWS and in the technology stack.
Speaker: And actually, they quickly learned the platform, sometimes better than me.
Speaker: They had some data architects on site who are absolutely fantastic.
Speaker: Yeah.
Speaker: And then from the perspective of data scientists, actually, it turned out that customers wanted to keep the data scientists in-house because they are thinking about the models as becoming a core competency going forward and critical IP that they want to own.
Speaker: They want to control.
Speaker: They want to own.
Speaker: It's part of their core competencies.
Speaker: They actually didn't want as much help as we thought they would want from us.
Speaker: They wanted to do it themselves.
Speaker: And then the business analysts, that's what turned out to be key.
Speaker: And the problems that our customers were having was they had an incredibly powerful cloud-based infrastructure to manage the data.
Speaker: They had super, super PhDs who were driving, you know, cutting edge algorithms that were specific to their business.
Speaker: But they lacked sort of the middle organizational middleware, the analysts who are going to take that power and apply it to specific business problems.
Speaker: And so that's what was lacking.
Speaker: And so that's what they wanted from us.
Speaker: They wanted...
Speaker: Can you bring in industry domain experts who can help us basically ask the right questions?
Speaker: So it's one thing if you have all this power, but if you don't know what questions to ask it, then it's a complete wasted infrastructure and it just kind of sits there.
Speaker: And so we brought in folks who,
Speaker: Basically specialized in helping them come up with what questions you would ask such an infrastructure once you have.
Speaker: And in that process, sort of the second trend emerges, which is that, and I think it was also a little bit unexpected, is that that process of becoming a data-driven organization is a culture change.
Speaker: It's not a technology change at all.
Speaker: Learning how to rethink how I use data is really becoming a lean, agile organization.
Speaker: And what I mean by that is the way that we're actually going to use data is through experimentation.
Speaker: And what we really want to do is we want to ask very immediate questions about how do I convert more customers for this particular product in this particular jurisdiction this week?
Speaker: Right.
Speaker: And so and that kind of a question only happens when we're rethinking innovation entirely.
Speaker: And we're not thinking about innovation as an annual budget cycle where we're going to invest in big waterfall projects.
Speaker: Instead, we're thinking about innovation as a day to day.
Speaker: You know, I look at the I look at the data, I get observations.
Speaker: Those observations become hypotheses.
Speaker: I build an experiment to test that hypotheses.
Speaker: The experiment comes back with data that I can then analyze to determine if my
Speaker: hypothesis was correct and it's time to double down on that and unless you have that culture to understand how to think in in day-to-day experimentation then all of the data isn't as useful as it should be so what we're seeing with that data is that it's really about putting it in the context of of a data-driven culture that gives it the power otherwise it's all of this power just sitting there
Speaker: The two others I would just want to throw out there.
Speaker: One is talking about bias.
Speaker: We started investing at the end of last year in ethics, AI, ML, IoT ethics.
Speaker: And, you know, it's funny because...
Speaker: all these philosophy PhD students can finally go back and say, see, mom, I can get a job.
Speaker: But essentially, we're bringing in... The high paying job, no less.
Speaker: That's right.
Speaker: That's right.
Speaker: Yeah, in the tech sector.
Speaker: So we're bringing in these ethicists to start to look at not just
Speaker: a bias in terms of decision making, but even in terms of inclusion and diversity, including decision making in general, how do you make decisions in a world where we're moving decision making more quickly to the edge and to the machines and devices which are doing much more decision making on their own.
Speaker: And then the last one is just the importance of the edge and the idea that data management is actually
Speaker: It has been centralized, but for us to be efficient with our devices at the edge making intelligent decisions in the moment, let's say autonomous vehicles, that decision-making and that data has to happen locally.
Speaker: And you need to now invest in technology and capability to allow devices to make a lot more decisions than they do today.
Speaker: And that's kind of the last cutting-edge data management issue that we're starting to see on the horizon.
Speaker: Thank you for listening today.
Speaker: This has been HSBC Global Viewpoint, Banking and Markets.
Speaker: For more information about anything you heard in this podcast, or to learn about HSBC's global services and offerings, please visit gbm.hsbc.com.
