The following is the first installment of a three-part article based on an interview with Stuart Berwick, the CEO and co-founder of Singletrack, a provider of capital markets client engagement and analytics platforms. Before founding Singletrack, Stuart spent a decade in business and technology leadership roles in the capital markets business units of major global investment banks.
Mike: Stu, can you explain what you mean by “Data Driven Advisory” and why a bank or broker would change their traditional way of doing business to adopt this new model?
Stuart: This kind of notion of “data driven advisory” is something that’s central to our thinking at Singletrack. Breaking it down into its three component parts, when I talk about advisory, I mean the advisory business of a capital markets firm. Principally a sell-side firm. That advice could be in the form of advice given by a salesperson, a research analyst or investment banker. I use the term advisory as distinct from execution, settlement or operations, or the other aspects of the bank.
The second component is data. Fundamentally, this is about utilizing not just experience, intuition, playbooks, or historical relationships to make decisions and take action, but to use actual data. These institutions have access to lots of data and they create lots of data. Leveraging data to provide advisory is about relying on facts and utilizing them. It is also critical to gather that data as ambiently as possible, not requiring direct human inputs. That’s the data piece.
Historically, if I look back at my time working in banks prior to co-founding Singletrack, the model for enterprise technology systems was often characterized by legacy systems built as silos, user unfriendly technology, and high costs of inputting data, which then sat in a database. Yes, you might be able to get access to it, but not much more than that. There was no driving activity towards profitable outcomes. It was fundamentally left up to the individual to do that themselves. In the “data driven advisory” model it’s really about the technology driving users towards profitable outcomes, rather than being expected to figure it out for themselves. That brings us efficiency benefits, as well as improved commercial outcomes.
When we look at the adoption of a “data driven advisory” model we should compare this to other significant shifts that have happened in capital markets, particularly in the sphere of technology and data. Look at what’s happened to execution, for example, over the 20 years or so, when you and I started in the industry. Back then, we had open outcry stock exchanges; voice broking; paper blotters and trade tickets. Over the last 20 years we’ve seen electronic exchanges, electronic trading, algorithmic trading, dark pool liquidity, transaction cost analysis, everything has become electronic. We have anticipated and believe we are leading a similar kind of shift in the advisory space to that which has already occurred in the execution space.
One analogy I often use to illustrate the shift to data driven advisory is the move from physical maps to Sat Nav or Google Maps. In the past you would figure out where you wanted to go. You would look at a map, you’d ask somebody, you’d use your own experience. You might listen on the radio to the traffic reports, and then you’d set out to your destination. You’d find your own way there. Think about how that has been transformed by Google maps in your car; you literally type in the zip code of where you’re going. There are vast data sets that are combined to recommend a route. You are constantly advised throughout your journey about which street, which turns to take, how to avoid traffic jams, how to avoid roadworks or other circumstances. The App learns from your actions, which makes the experience better for you and for others in future. It’s a simplification, but you could see “data driven advisory” as almost a Sat Nav for capital markets, where you have huge amounts of data and technology working in concert with human expertise to enable capital markets professionals to reach their goals in the most efficient way.
Mike: Why don’t you provide our readers with a real life example of how a capital markets firm might use data driven advisory, whether it’s from the banking or the research side?
Stuart: Sure. This is not the use of technology or data for their own sake, this is about helping capital markets professionals achieve more profitable outcomes, more quickly, and more effectively. If you think about your institutional salesperson, who arrives in the morning, has his or her coffee, turns on the screens, and there’s market data, there’s product data. What research is being published today? There is trading data. What positions does the bank hold today? It’s the salesperson’s job to identify who they should contact, through which channel, at which time, to deliver which idea in order to win an order or vote. If you look at the old world, it’s up to the institutional salesperson to aggregate all of that intelligence, and reach a conclusion on what to do. Then go ahead and do it. A data driven advisory platform has that conclusion on the screen in front of them, enabling them to get on and do it, with much greater efficiency and impact.
If I’m a trader is sitting on a block of shares and needs to execute a block trade, who do I talk to? Who’s most likely to take some or all of this block based on a whole set of criteria about preferences, current holdings, sentiment, and so on? If I’m a research analyst, I’ve updated my model or published a note looking at a particular sector theme. Which portfolio managers are going to be most interested in talking to me this morning, or meeting me on my upcoming trip to the east coast? A data driven advisory platform will be able to tell me who to contact.
Or I’m a banker and I’m running a sell-side M&A mandate and I have 30 target acquirers in my pipeline. Well, they’re all at different stages of the process. Some have read the Teaser, some read the Memorandum, etc. Who should my next call be to, in order to drive the process forward for the client?
Mike: It’s really transforming the business from the old way of working where an institutional salesperson needed to collect a lot of information. They had to go to various systems to collect that information. From that, they’ve created their own call list that may or may not be accurate, but of course that’s what they could come up with given the information they collected from the various systems. Then they go about their business and the productivity of that salesman would often be a result of how well they did collecting the information, how well they did prioritizing, etc.
Stuart: Exactly right. These words, ambient and guided, are watch words that underpin this concept. If your data driven advisory platform ambiently collects as much information as possible from multiple data sources and aggregates it together, and then guides the user to those outcomes then, for the best professionals, it makes their lives much more efficient and productive. It actually uplifts their colleagues who may not be as experienced to a higher level of productivity as well.
Mike: That’s great. This whole concept of data driven decision making is something that’s been quite popular for quite some time in various other industries like healthcare, consumer services, consumer goods. Why has it taken so long for this whole concept to hit the capital markets of the financial services?
Stuart: You’re absolutely right. In those industries, this notion of data driven decision making has been very widely adopted. Every time you turn on Netflix, and wonder what to watch on TV tonight, Netflix uses a data driven decision making algorithm to recommend what to watch.
Having been involved in the capital markets industry for nearly 25 years, I think many in the industry have this partially justified sense of sophistication. By that, I mean it is a complex industry and it’s reasonable for somebody to assume, I can’t trust a system to tell me what to do, or I can’t trust the systems to pull the right sets of data together to help me make this decision because it’s a complex market or a complex industry. This is true to some extent. However, the industries you mentioned are also quite complex.. Ours is not uniquely complex. I think that’s part of it.
I also think that the investment in data and technology has been very significant, but unevenly distributed over the last 25 years. There are some areas where there’s been huge investment in technology. For example, all banks are party to that sort of model for execution. This was one of the founding premises of Singletrack. We felt that the advisory space was actually very much behind the curve and would benefit from that sort of technology and that sort of investment.
I also think that financial services is a conservative industry, arguably a bit parochial at times in terms of not being open to ideas from other industries. But this is changing as the younger generation of digital natives enters the industry. In addition, tech has been a significant and dynamic sector where most investment banks have extremely profitable clients. I think these factors have led Wall Street executives to conclude that technology investments cannot be considered a low priority aspect of the business anymore. So I think that attitude has changed as well.
Mike: So what are a few of the key industry drivers, which you believe have convinced banks and brokers to finally adopt this model? You’ve talked about some of them, but what’s really forcing the hand of financial services firms and particularly the capital market groups?
Stuart: This has been our vision and our mission from day one and over the last 12 years for Singletrack. There has been a confluence of forces in the industry, which have really led industry executives to understand the need for “data driven advisory” and which is really driving interest and adoption. A few of the industry dynamics include the growth in investment management and the AUM of the industry. There is an ongoing tension between active and passive fund management and the drive for performance that this has created for active managers. You also have the tension between public capital raising and private capital raising. And then there’s the flowering of new asset classes, certainly new types of research like the growth of expert networks, ESG, alternative data, and crypto research. These research types are becoming mainstream with institutional investors. In the past 25 years in the industry we have experienced the credit crunch, the dot com boom and bust, and lots of other very significant events, including COVID and the tragedy of war in Ukraine. Despite all of these developments, the pie for financial services firms has continued to grow even while increased competition has meant the number of slices of pie has also risen. So if you’re a customer, you’ve got to choose a producer of advice, a raiser of capital, or a manager of capital. Likewise, if you are producing or facilitating the capital raising process or investment idea generation, then you’ve got to be relevant, you’ve got to compete, you’ve got to offer what the investors need. The ability to leverage a “data driven advisory” platform enables both producers and consumers many of the benefits we discussed earlier.
We think about this as a Venn diagram with three circles. One circle is the industry pressures, which I just talked about. The other is structural pressures, such as the regulatory requirements the industry has been facing in recent years, or post COVID working practices. COVID has had a huge impact on the way firms operate, the expectations of staff, and the ability to retain people. Sadly, there are also terrible geopolitical events in the world at the moment. These are all structural pressures. So you’ve got industry pressures in one circle, you’ve got the macro structural pressures in another circle. And then the third one is technology itself. There is this ever accelerating progression in tech. What it’s capable of, what others are doing with it and so on. We see data driven advisory as being the intersection of those three — industry dynamics, structural pressures, and technology.
Mike: The growth of AI and machine learning has really enabled the ability to take data and create insight to a degree that was hard to do in the past.
Stuart: Yes, AI and machine learning is absolutely central to this concept. And indeed it is one of the key technology foundations to our work in this space because you have to leverage technology to turn huge pools of data into insight and advice. What generates the guided next action recommendation is the artificial intelligence.
One other thing I’d say, is you always have a new generation of professionals coming into the capital markets industry. The newest generation are digital first people. They’re used to being able to run their lives on their iPhone. And so their expectations for how technology should work needs to keep up with the way they conduct their lives outside of work. They are demanding the sort of Netflix experience on the trading floor as well.
The current industry environment is driving a profitability squeeze. It’s driving for cost saving. There’s a juniorization taking place across the capital markets industry. I know Mike Carrodus at Substantive Research has done all sorts of analysis on this, but there’s essentially an experience deficit. Fortunately, if you’ve got your technology and data working effectively, then this will more than make up for the experience deficit that is taking place.