SHAW: Maybe I could start by asking you just about a quick overview of your center of excellence that you run. And then as a secondary question, what motivates you to get out there and talk about the work you're doing? Not many companies are that open about what they're doing, especially in the area of analytics.
Bieda: Sure. Well, I think most definitely, analytics is a core part of my work here in sort of evangelizing the importance of analytics within our space. So it's definitely part of what we see as important, not only to attract others into our space but to establish BMO as an analytical company in the way in which we work. And I think you know this as well as I do, it's very hard obviously to find talent in the space in which we're in. We compete globally and there's very few people who do analytics generally. And so, part of our goal in being out there is to share what it is that we're doing such that we can attract great analytics talent because analytical people from my experience, having been in this space almost 25 years, love to work with other analytical minds. And if you create those environments where analytical talent can congregate, they love it.
And so, we are very much about doing that. And so, I end up meeting a lot of people who are just interested in coming into the field all the way through those mature folks who I can most definitely learn from, and make sure that we create the profile for ourselves in the community. And the analytic center of excellence that BMO has, the best way I would think about it is, if you think of all of the core leaders in a retail bank, the retail and commercial bank that you could tug on for success for a customer or a shareholder, whether that's the risk that you want to play with, the risk and return levers like credit limits that you would extend, at what price point, the different client experiences that you would focus on, the different client conversations that you would extend and the order in which you would do that, where we would place people in a sales force, selling what solutions at what time and what market. Think of that kind of cocktail of levers that you would play with analytically, we've made a choice to put those all in one place because we recognize that there's interplay between them. And with the analytic center of excellence, it's really a host of analytical talent, that is, all in one space who are devoted to being able to understand the levers and then apply varying types of analytics against them to be able to help our business partners succeed.
SHAW:Interesting. So, you are effectively a service to various stakeholder groups and lines of business within your organization?
Bieda: Yeah. We've got a partnership model because we, unlike a service model where typically you would, what I've seen, to be honest, because I've done research on this in my days at SAS, there's sort of three models that analytics teams can have. On the left end of the spectrum, you could be a service provider. In the middle, you could be a consultancy where you take your area of expertise and you apply it, so people come to you and you say, "Listen, I've got this business problem. What sort of analytical technique might you'd employ in order to best get at my problem?" But at the other end of the spectrum where, you know, the core 23% of FIs tend to play in global firms, is to be a business driver. So, you're using the data that you have access to, to find opportunity in data. And we're definitely in that space. And so the structure of our teams, even though we're a centralized group where we've got synergies and definite techniques that we employ that are standardized methodologies, where we can have opportunities for our analytics talent to congregate and come together, we are aligned to the metrics of our business partners. So, we're compensated in a very similar way to business partners and we're devoted resources to those teams. So, the business model is very different and it actually works very nicely, and I've been around the globe in a bunch of different models. And this one as a hybrid model, we've kind of got the best of both worlds. You can build and understand the technologies, you can come back home and you can learn from other modelers. But day in and day out, you're actaullu sitting with your partners that you work with. So, you're highly aligned to the business objectives that they're focused on and you're actually paid based on the same sorts of metrics that they're paid on. (7.47)
SHAW:So, if I'm understanding you correctly, you're saying you do have dedicated resources to specific lines of business and/or stakeholder groups, and they are at the table discussing the business issues and questions that they have, and they are participating in those conversations? Is that what I heard?
Bieda: They are, yeah.
SHAW: And how much is the research that you do - or the analytics I should say - directed research, that is, specific requests to answer specific questions, how much of it is exploratory research where you're just trolling to see what specific insights, patterns, deviations, exceptions might pop up into view and lead down the road of insight?
Bieda:We do both. And even within, as you would probably guess when there is a defined business problem, there's exploration even within a defined business problem. So you're always searching for other opportunities in data other than what's asked. So, even an outlier, even in pattern detection and outliers, you find interesting things when you're looking at a problem. We do a lot of analytics that are directly aligned to a business problem and the requests that come from business partners where we're exploring an opportunity that's, you know, tangential to a business issue that we've been asked to look at. But then there are other things that are more R&D related in nature where you can see something in the data that's a bit interesting that takes you down a path. But that's a smaller proportion of what we would do as a team but nonetheless, we do both of those things. It's definitely a smaller portion because as a business analytics team, we align to the strategy of the bank and the strategy of what drives our clients and the experiences in shareholder returns.
SHAW: And in terms of, for example, conventional market research within the bank, do you work in partnership with them? Are they entirely separate? How do you combine sort of a little more attitudinal research that's being conducted with the analytical behavioral work that I imagine you're doing?
Bieda: We do. Yeah, and formally, in a prior life, I've managed both. So I have a good understanding of, you know, quality and quant research and the power of it paired with traditional analytics. So, we work very closely with those teams if it's within our marketing area. BMO doesn't have as great of a focus, I would say, on market research. We tend to use a lot of our behavioral data but no doubt, we look at market research information for some of our branding, for some of our share wallet contrast relative to market and most definitely within our customer loyalty space. So, there's a very natural way you compare behavioral data with other information that's external to be able to get a fuller view of things like market, so you can create benchmarks. So, we definitely look at that whole host of things and work closely with the teams that do that, but we have a lot of folks here too who have research background. So, we explore those datasets in conjunction but typically, we would lean towards looking at the behavioral data first. (10.48)
SHAW: I'm presuming the data scientists that you have are extremely schooled in the use of advanced analytical methods to probe the data. One of the challenges I've found with a lot of analytics groups is they have a terrific ability to look at the data and see the patterns. They struggle, let's put it this way, with actually explaining the meaning and strategic significance of those findings to stakeholder groups. How do you bridge that gap between the quant and sort of the more qualitative analysis that really puts life into the data?
Bieda:Yeah, I found that too over the years. What I've found successful and what would I have launched here is an analytics university that is targeted towards analytics practitioners as well as those who interface with us. And it's predicated on three fundamentals. One is the technical acumen that you need to be proficient in the analytics space. Secondarily is the business acumen that you need to understand how businesses make money, frankly, and the businesses that you work in. And then the third pillar which you're referencing is around kind of the softer skills or interpersonal skills and it has to do a lot with the translation layer elements. So, being able to not only know how to do things technically, not the understanding how mortgages, for example, make money, but a skill to be able to translate that effectively to others, and to be able to highlight the essential elements that matter. So, we have spent an awful lot of time. Anyone who's in a role who's interfacing with a business partner has done loads and loads of training on communication skills, on paraphrasing, on storytelling, on the ability to influence and persuade, like, you would not believe the amount of training that we have done in that space, because what I’ve found over the years is that, you know, an analyst might well have the cure to cancer. But if you can't get to those who are afflicted, it's really not useful. And it's an essential skill for any analyst today and while visual analytical tools as that veneer that's on the upper end, it makes the facilitation or display of analytics much easier than it is formally been. All that said, you need to be in a situation where on the fly you can most simply make simple numbers and highlight what is really important to focus on. And that is a skill that one needs to learn.
SHAW: That's an interesting approach. So, effectively, training the quant people to be as expressive as you are, is effectively what you're saying.
Bieda: Well, I found it too, even in the display of numbers, there are ways that you can do it that confuse, and then there are ways that you can do it where an eye will go towards the essential facts and data. And it doesn't mean that everyone needs to do it in a similar fashion. We've created these things called the 10 commandments of analytics which has to do with how information is displayed - when it sort of passes the 10 commandments, you're actually able to pass go, collect your $200 like on a Monopoly board. We've created a language around how it is that we communicated when things are good to leave our house and hold ourselves to a higher standard. By no means are we perfect at this because we're producing thousands of things out of our shop in a year, so you don't have the privilege of combing through everyone with a great degree of perfection. But we recognize that communication is the agent that fuels what we know and kind of passes through our hands to someone else. It still relies very heavily on that, and we know that in order to have the kind of influence and to have an analytical company, you most definitely need that skill. (14.35)
SHAW: So, that's extremely interesting. And you do have a gift for metaphor, I might add, so that must help immensely.
Bieda: Thank you.
SHAW: You must have incredible diplomatic skills too because you must have a lot of pressure on you. And I imagine the list, the queue, if you will, for the work that needs to be done keeps growing exponentially as people get more accustomed to, and conditioned to, asking for intelligence. How do you cope with that prioritization? Are you business casing everything? What is the calculus that goes into figuring out how much time to put against specific projects and priorities across all of the different lines of businesses you must have to deal with?
Bieda:Yeah, it's a very real issue. I actually did a piece for MIT [Massachusetts Institute of Technology] on that very problem, to be honest. And in our reality here, we produce over 1,500 pieces of analytical work in a year and divisible by, you know, hundreds of people. So they're pumping out very sophisticated things on a rapid basis and that demand grows. And every time you produce a piece of analytics, of course, it leads to more questions. And it's harder still to find people. So, that problem never really goes away. What I did here and in other places, is we ran analytics on the analytics. So, I created a database of all of the analytics that were being requested of us, and then looked at who they were coming from and then attached them to strategic priorities, and then looked as best as I could to ascertain the work. And from there, started to rank, and then created quarterly alignment sessions with business partners so that at every point, looked at it and said, "Listen, guys, each quarter, the leaders that we were working with, of all the different things we thought going on with you, are these the right things?" Businesses are living and breathing organisms, so we recalibrate it continuously. But also, we make sure that the analyst to my earlier point were well trained up. So they too, when they looked at data and they looked at a query, if they understood the levers of a cards business or the levers of a mortgage business, they could at a glance - even if they had to probe a little deeper - understand whether that analysis made sense. And if someone asked for a query, they'd look at it and go, "Hmm, we do that, but that doesn't really make sense." Like, they would know enough to question. And then, what we actually did is, we trained these analysts like crazy and continued to do that. Our analytics university is like any university where there's semesters and it's always on. But I also train my business partners so that they were culling through and making sure they weren't asking me questions to do, say, a test where it's like 100 people or 100 customers where I couldn't measure it anyway. So, on both ends, I had the analysts being a little more skilled as well as my business partner being a little more skilled. So, when stuff came into our house, we were just smarter about what got in. So, I didn't end up with this bloated system where I had, you know, thousands of things in my house that didn't really pay back. And then we spend a lot of time automating the hell of the stuff that we've got, so that we don't end up having “hands to keyboard” all the time coding. (18.00)
SHAW: It's such a significant challenge in any environment that it's a great answer and very insightful. Let me push this just slightly in a different direction but picking up on that point, I mean, there's only so much that can be automated, and you've moved heavily into the area of journey mapping. And I have a bunch of questions I'd love to ask about this. It's clearly become a core competency for you. Is the motivating factor here, that financial services has really shifted out of the bricks and mortar into an online battlefield for customer loyalty? Is that the motivating factor here to ensure that that experience is absolutely the ultimate experience that somebody can have with BMO?
Bieda: Not 100%. I could see in the data that customers traverse the enterprise and how they consume products and services was unique. The analogy that I often use is that customers are like water. So they “river through” the enterprise. We build dams, like, call it an onboarding dam, a sales and service process. But we build these dams and we intend for them to flow down it. And what you often find when you look at the behavioral data is that customers indeed go through those dams. So, it's the thick lines you see in the UI [user interface], we can surface it. But they go down in other ways and they leak into crevices and the journey analytics, of course, is the discipline of capturing all that's wet. And what I've found is that it's incredibly powerful. Not only does it drive top-line revenue, not only does it save costs and things like digital leakage, but it most definitely enhances loyalty.
When you look at how businesses operate today, and the complexity - we're all so siloed. A customer doesn't care. They don't care that there's lines, loans, mortgages, cards, channels, they don't care. They're just trying to get done what they need to get done. And they run like water and they bisect our enterprise. And so it stands to reason that analytics ought to do that, right, like customer segmentation, like, customer profitability, like other types of analytics that run horizontal. And so, it's just a simple thing. It's just follow the customer. Journey analytics is a type of analytics that does that. At its very core, it follows the customer. Now, mapping, which is kind of the early incarnation of this, was really all just laying out what the customer did. The analytical pieces were the anticipation and prediction of the strategic intention or what they were actually trying to do: "Hey, look, Lori's trying to pay a bill," through to the prediction of what she was actually going do after that. She's going to pay a bill and then she's going to do a foreign exchange transaction and she's going to be very unhappy about it, is that kind of prediction or thread. And when you start to look at things that way, people are in motion, like all of us. We are on our way to somewhere and we have feelings about being on our way to somewhere and stuff gets in our way. And I think when you're a company that is always on and you're passionate about the customer, then you assemble your analytics and data so that you understand where that customer is at any point in time. And we got overly obsessed with it because we were trying to keep pace with our customers and trying to honor what they were trying to do. (21.23)
SHAW:Do you have dedicated teams then that are focused specifically on journey mapping?
Bieda:Yeah, I would distinguish mapping from end journey analytics, and I know it's a subtlety and I think sometimes folks use those terms in conjunction with one another. So, we have a unique department that does journey analytics. So, that's like surfacing right from the data. So, what actual journeys are customers on, and then we can tell that from our behavioral data. So, my department, our analytic center of excellence, does journey analytics for a vast number of journeys that we have. And then we've got agile teams across our company who leverage the insights that we surface to drive different customer experiences. And then we use these journey analytics and we combine them with things like user-centered design or other research to create what I'll call an ideal customer experience. And then we also have a process center of excellence that is not unlike my analytics center of excellence. And these individuals really have a great grasp of what all of our processes are across the company.
What we'll do is, when you have a particular journey, I'll call it, you know, a customer is trying to pay a bill. Think of yourself trying to pay a bill. It involves many different things. So, if you're sitting at your couch at home and you're on a tablet, for example, and you're trying to pay your credit card bill. And then, you know, you get interrupted and that pizza that comes to your door, so you put your tablet down and your session times out. And then, later on, you go and you pick up your actual phone, and you try and complete that session. But because it's timed out, you don't really 100% know that that's actually part of the same journey. But then you realize, "Oh, shoot, I got to travel to the U.S. I got to go into a branch and do a foreign exchange transaction." So why don't I just pay that bill and do the foreign exchange all at one time?" So you close out both of those sessions and you show up in branch. Well, what we see is that those are all related to the same intent. What you actually set out to do was pay the bill. But the cocktail of things that you chose to do was the foreign exchange, the payment of the bill, and then you withdrew some other money out of your checking account. But you showed up in branch to do that. You can tell too, through that data, that what you've intended to do and what you ended up doing, you can tell the number of aborted efforts, you could tell the digital leakage, like, the amount of things you tried to do and you just couldn't finish. So, what we do is we actually look at the behavioral data and map out not only you, but the hundreds of thousands of people who do that, and the ones who don't actually need the foreign exchange but just who struggle to complete the transaction because it didn't work for them. And then we pair that with - which is I know where your question is going - with our process team to say, "Listen, of all the people who are trying to do that, what s the combination of things they're trying to do?" And then if you're trying to quote move money, maybe it's not about paying a bill. It's about doing a whole host of different things. So, right at the front, in the user experience on your mobile device, what should that actually look like? Like, should it be broader and different? So, what was the customer really trying to do at one time and how do we help to complete all of that right up front in a mobile device so they don't ever have to show up in branch? And we make it easier for them.
SHAW: So it then really presents itself as an experience design challenge at that point. Are the process teams then, do they have ownership over that experience design? Or does that, in fact, get pushed to another center of excellence that actually takes that on as a task to change that user interface and workflow?
Bieda:Now, what ends up happening, your instinct is right, is that it involves multiple people. And it's not really about the process. If you think about that example, it's an everyday banking situation, it involves digital. It actually in some cases involves fraud, it involves your contact centers. There's a bunch of people, right? So, what we've done is form agile teams that have groups of representatives from different areas. And agile teams by their nature have leadership but they also have representation across multiple groups. But those teams have devoted resources and with the charge to solve a problem. So, you don't actually have a process leader owning that problem. You've got these teams working solutions on behalf of customers. And we're finding over time those agile teams grow as most organizations build out and see that customer journeys are really a combination of people working on behalf of the customer. (26.01)
SHAW:What does this all roll up to organizationally? What's the hierarchy that supports that customer-centered thinking that you're describing? Is there a head of customer experience that really has accountability and responsibility for this all, or is it more decentralized than I'm describing?
Bieda:It depends on the problems you're solving. So, if the particular issue, if the origin of it is a client experience problem, then yup, it rolls up to a client experience leader. If it's a digital issue, then it's managed by the digital team. If it's a product one, then the product person runs point. But agile teams by their very nature are a little bit different than that. So, they sort of bust through your traditional hierarchy where, "Hey, everybody reports to me." It's really not that. In fact, I have borrowed resources or lended resources that I devote to that cause because they definitely need analytics to understand the kinds of problems I'm describing. So, the way we've seen banks assemble themselves - and just companies historically - get a hell of a lot different, as they should, because we need to almost ignore the pillar in which you're in, and assemble around the need of the customer.
SHAW:Where do you see AI fitting into all of this? Is AI going to help relieve you of the heavy lifting that's required to do the sorts of exploratory analysis that you've been describing here for the last half an hour? Or are there other applications that are sort of more relevant and on the ground today that you will be leveraging it for? And then just in terms of the next five years, how do you see your area evolving? (27.41)
Bieda: AI or machine-learning, we're using it already today. It becomes really a very fundamental part of how companies operate in a core competency in and outside of banks. And the types of sophisticated analytics that we are doing will only grow, and the ability to automate decisions and simplify how we operate will be essential. Otherwise, we will not be able to scale to the kind of demand and have the level of flexibility and precision that's needed. And we can see all kinds of applications for connecting and making automated decisions to connect the data that we've got access to today, to the right customer decisions that need to be made, the right customer choices, the types of options that they're looking for, just connecting all of the wealth of data that we have to the things that customers are looking for. So, we already see many, many applications there from all kinds of things, from robotics to how it is that we model, to the automations that underlay it. So there's many applications already. There's a lot of the cyberspace as well as our fraud space that are very relevant in the banking sector as well, not only for traditional marketing and client experience work.
SHAW: Five years from now, is the new battleground competing on analytics? I'm sure you would agree with [Thomas] Davenport's position around that. Where are those battles won, do you feel going forward? What is that competitive battleground going to look like five years from now do you think?
Bieda: Yeah, I think that in Canada and I've worked across different borders, but here in particular, it's a mature market with a subset of players. And we're stealing share ultimately from one another. And the ability for us to be able to make very smart risk-return decisions in real time, understanding what customers need and at what credit limits, at what price points, when they need it based on their life stage, and the dynamic evolution of that will be essential, to use our capital very wisely across all of our areas is essential. It's one of the dominant profit levers as a bank, and it's what customers are seeking. So, companies who master that - and that is an analytical problem - and who ultimately optimize the risk-return, I think will be at the front of the heap.
The other one I would say is from an analytical problem perspective really has to do with that bricks-to-clicks or clicks-to-call optimization. So, with the rise of digital, you know, whether mobile or online banking, the navigation of what it is that a customer is trying to do online and then their ultimate bleed into other offline channels, the mastery and the understanding of those journeys and being able to keep as much of it upstream as the customer desires, and then the orchestration of what has to happen in branch or on call is essential. And hence what leads us to customer journeys because it's what helps you understand that. I think you will see banks massively double down in that space. And our partner, ClickFox, has helped us understand that greatly. (31.06)
And then the third place that I would see is really around the customer conversation orchestration. So, I think companies have been at this for a long while inclusive of banks. And we've kind of led the pack in this space an awful lot. But it's not selling. It's being able to connect a customer conversation at the right time and moment and match it up with the right advisor. So, as banks change and the people within the banks change, there is still a very dominant need to have a conversation with a human being and have a device at a moment in time. It's still a very advice-based business. But the matchup based on the time frame you're seeking it is essential. And so, we do a ton of work with salesforce optimization making sure we've got the right staff in place, making sure we understand the customer intent so we can match those things up appropriately to make right by the opportunity that exists. Those would be three places where I see the most amount in return and where we're lined up with what the customer is looking for.
SHAW: The complexity of what you've been describing today is amazing. How does your infrastructure support that? And you obviously have a number of systems of insight, I imagine. I'm guessing here it's SAS. You reference ClickFox and other technologies. Could you describe for me what those core technologies are that you relied upon and how do you integrate that with source data systems and Hadoop and all of the other data collection tools that you might be operating with? It must be enormously complex just to maintain that kind of environment.
Bieda: It is, yeah, and no doubt, there's stuff that we do and then stuff that are central technology and operations group does on our behalf. I kind of see the world in three chunks. There's sort of data analytics in action, and there's technologies in all three spaces. And most definitely on the data wedge of things, there's been a transformation there as we both know towards more of an “open environment Hadoop” where the accommodation of structured and unstructured sources can live in one place. And that's been over the years that's, you know, evolved from more relational structures into what it is today and, you know, the flexibilities that that affords us is massive. And it's obviously just cheaper to house things there. And what's interesting there is with things like journey data and obviously, the rise of what digital has birthed is the amount of information that is sitting there is huge. And we've even realized, my technology counterparts last, but the 100 terabytes of journey data that I've accumulated, it's larger sometimes than the environments that had been created for us here. So we're growing at a clip that sometimes eclipses the environments that we have available to us. And we kind of laugh because the organization builds the hotels and wants to sort of rent out rooms to the different groups, and I end up taking up all the hotel rooms. So, it's not a nice hotel to stay in sometimes. But nonetheless, you have to work it out because your thirst for data is unquenchable. You want it and you recognize that there are patterns in data that are not evident to the naked eye. And you need to have the data there in order to tease those relationships out. So I'm unlikely to not want to put it there. You know, I'm unlikely to want less data. I want more. So, our data itself is tricky, right? We're working through processes with our counterparts to continually perfect it, to connect it, to have it in an environment that is usable for all the people who are poking at it continuously because that's most definitely the journey.
We've made some choices for some strategic solutions that we thought were differentiating for us. And, you know, this is not the traditional SAS versus open source stuff where it's all, you know, playing in sort of base code and trying to evolve it to something. This is where you play in a solution space versus raw code, like, where you would choose to invest in a partner versus do-it-yourself. And most technology organizations or most technology teams within banks oftentimes want to build versus buy. And where I spend an awful lot of time here is doing the opportunity analysis, the cost-benefit analysis. Because if I were to do that, and if I were to play technology, how much time would it take me to get the yield on that? And what would happen if I got external expertise, what’s the accelerant of that? It's just very classic math. But we've sent an awful lot of time making, you know, a few key choices in places that aligned to our strategy where I thought we could partner for the betterment of the company to get us ahead in some spaces around risk-return, around journey analytics, around some things that we needed to do around test and learn. Those are some areas where I knew that we wanted to be better and different and stronger. And therefore, chose some partners that could help us get there faster than we thought we could get there ourselves. And then on the actioning engine, those are really just triggering, right? It's real-time decisioning engines. It's the ability to be able to take what data you have and disseminate it across places. And of course, we've got the ability to do that but I would say we're not as integrated as we would like. And we're aiming to do that in a better way so that when, as I build things, it's a rinse and repeat across the enterprise.
SHAW: I wanted to push into one other area if time allows.
SHAW:How about the cloud that looms over your entire business these days known as data breaches, data privacy, consensual use of data? You know, you're using data extensively, customer data extensively. Where do those considerations play into this, and do you have concerns at all going forward that you might be faced with restraints or constraints in the use of that information?
Bieda:I do feel that too as a practitioner in the data space. And data is the new water. It's everything, right? It's more precious than water and everyone wants it. And so, therefore, I think we will continue as an industry and as anyone who has the privilege of holding customer information in the constant hunt, right, or be hunted by others for the valuable information which we hold and be continually vigilant around how we manage it. We have here very good data governance practices within our own organization and continue to invest around how do we manage that information in a way that it protects our customers. (38.10)
SHAW: Do you have a chief data officer at this point?
Bieda:We do, yeah, and we have had for several years.
SHAW: And that's, I'm presuming, a close partnership that you would have with that individual?
Bieda:Absolutely. And there are governance principles that sits over top of our entire business. And I feel very comfortable that the manner in which data is managed in our organization is well thought through. And I think that some of the vigilance around that is smart. That is how we have to protect our information and some of the policies around who can access it is just the way of the world and that's what's necessary to manage the kind of sensitive information that we have access to.
SHAW:So, is this a sort of triad where you have a chief data officer, a chief information officer, and ultimately, Lori, a chief analytics officer? Do you see that in the cards?
Bieda:I think each organization does it differently, and I think organizations are still trying to figure out the role, there's definitely chief data officers that have been around for a while. The role of analytics oftentimes sits in the business as it does with us, and the centralized analytics function has a lot to do with choosing the technologies that are useful across multiple businesses such that we don't have cottage industries and there's efficiency across multiple businesses. But I think many organizations are trying to figure out what that role actually looks like. And it obviously depends on the size and it depends on the structure of a business. There's most definitely some opportunity to be able to look across an entire company to find opportunity in the data that can help and manage trade-offs across businesses and there's insights to be had there. And I think it depends on a company structured focus.
SHAW:By all the things you've been describing for the past 40 minutes or so, would you say that BMO has transitioned to be, and I know this is a loaded question, I apologize for it in advance, a customer first culture?
Bieda: I think we've had a customer first culture for a long while. I think the pivot you're seeing us make in a very bold way as an analytically driven company making bolder moves, seeing analytics as an innovation wedge and an opportunity to be innovative is you seeing us make bolder investments in that space as a mechanism to be able to activate some of the potential around our customer.