“The analogy I like to use is the Rubik’s Cube with all the colours mixed up. What we do is take clients through the process of realigning all their data – getting the colours of each block to match – to transform their data infrastructure,” says David van Rooyen, one of the founders of London-based Pomerol Partners.
Pomerol Partners is a business intelligence consultancy that specialises in advanced data visualisations. The firm builds business toolsets – dashboards – to help senior executives make more informed decisions. That sounds straightforward but within the financial services industry, particularly global investment banks, such is the complexity and fragmentation of data that getting a clear picture of what is driving profits up (or down) is nigh-on impossible.
The data architecture in many of these firms relies on technology that creates a silo-based approach to data management; from department to department as opposed to sucking in data from other areas of the business that can lead to unexpected insights.
“In order for that to happen you need to balance two things: first, you need to balance your business knowledge. Second, you need to utilise your business data.
“Getting that balance is key. To achieve it requires a tool that not only has the data but ensures that the user uses that data. It’s one thing presenting data to a user, it’s quite another to get them to use it. That is the biggest challenge,” says van Rooyen.
What Pomerol Partners aims to achieve with each client is to get them looking at data in new ways – a visualisation process – to gain a competitive edge. The insights drawn from harnessing data to its full capacity can lead to trading desks servicing their clients more effectively as well as:
- Pro-actively identify client pricing outliers
- Optimise collateral levels and strategies by reacting quicker to external market events
- Challenge the technology infrastructure to become bigger and faster
In a white paper entitled “A Front Office Guide to Better Analytics,” Pomerol Partners sets out what it calls an analytical roadmap to disentangling the spaghetti of an organisation’s infrastructure to regain ownership of its data and business logic. This requires a reactive iteration phase – data visualisation and transformation (“business intelligence”) which leads to challenging the infrastructure – and a proactive iteration phase where a firm’s business logic can drive forward change.
This means having the ability to utilise a big data infrastructure in more of a real-time environment (the reactive phase involves historical data to drive the transformation process) where predictive analytical technologies could be employed.
Fred Hefer is another of the co-founders at Pomerol; named because like the wine, which blends Merlot with Cabernet Franc, the firm combines commercial and product knowledge with technical knowledge and IT expertise.
Hefer notes that the financial industry has been slower to adopt new technologies for data management (e.g. telecommunications, retail sectors) and has yet to fully make the leap of faith: a point that the white paper seeks to address head-on.
Part of the problem is that it requires emotional buy-in. Changing the way people in a firm look at their data is largely psychological.
“Take voice-over IP technology to call someone over the internet,” says Hefer. “This was great technology, it led to huge cost savings but when it was first implemented nobody within the investment banks wanted to use it. They still just wanted to pick up the phone at their desk rather than pick up a headset and use their computer.
“It’s exactly the same issue when it comes to presenting data. Whether it’s a hedge fund, a prime broker, people receive data within the organisation in a particular way. One department receives data in a particular format. Another department receives different data in a different format and they try to bring it all together.”
The point here is that firms are only seeing a fraction of the data that applies specifically to their area of the business rather than drawing in data from other parts of the organisation. That’s where the visualisation element comes into effect; using dashboards to get a more holistic view of what’s going on.
Trust the data
“The single largest challenge these institutions face is trusting their data. Very few business decision makers have the luxury of transparent and instant access to their own business logic and data. This causes senior executives to request information in a shotgun type approach to their business managers, finance, risk and operational functions – who all produce distinct versions of their view of the “truth”. The senior executive then goes with the most accurate, or least inaccurate, version given their experience in order to make a commercial decision,” says Hefer.
Taking a staged approach (to building the right infrastructure) helps ensure that data can be trusted; particularly important when mobile devices are increasingly used for business operations.
The first step is visualising all data available and putting a spotlight on the bad data. As soon as clients see and truly understand the flaws of their data, they will see the remediation and start trusting the data more.
“Data veracity is a big deal. We’ve started working on a project with one of the large investment banks. There’s an issue with their MIS reporting in that the reports being generated aren’t fully trusted by people. Not only does the look of the report need to be improved, more importantly the data populating the report needs to be accurate and trusted,” notes van Rooyen.
For too long, financial institutions have focused on revenue generation. Each business division is responsible for its P&L, leading to a silo-based data infrastructure that typifies the industry. It has created huge inefficiencies where, unfortunately, the right arm has no idea what the left arm is doing. If John on the equities desk is down, what’s happening over in fixed income? There are always other market factors involved and possibly personal/training factors involved as well.
“We help to associate seemingly unassociated data. For example, we’re doing some work for a large US bank within FX. We are starting to correlate client turnover with their sales trades. How is their year-on-year turnover impacting the bank’s bottom line, specifically in respect to FX transactions? One of our team has built an algorithm to perform the necessary calculation to correlate the two,” confirms van Rooyen.
Visualising the data is only half the equation. What is also vital is choosing the right people to develop the dashboards that act as the window into the data. Historically, this was done by IT developers with little or no commercial awareness. “The dashboards we build are tailored exactly to the client’s needs,” stresses Hefer.
Appetite for data
By disentangling the data spaghetti financial institutions have the ability to see data presented in a whole new way. Pomerol Partners achieves this by understanding exactly what drives the decision making process by key executives in firms: this could span FX, commodities, equities, hedge funds trading, insurance environments, prime brokerage.
“What we end up driving is an appetite for data by the end user,” suggest van Rooyen. “We’ve built an application within the credit risk department of one firm. Every morning, the chief credit risk officer walks into his office and the first thing he does is look at our dashboard. The more firms get exposed to these dashboards and develop an appetite for data the more questions they’ll start to ask. It’s putting businesses in control of their data.”
Hefer suggests that financial institutions need to start thinking more like technologists to become more competitive. “They’re under cost pressures and the only way to optimise is by making sure that their service is better; that requires documenting their existing technologies and bringing on new technologies. There are a number of “disruptive technologies” that have come out in the last four or five years that can drive a lot of value to sectors like prime services.
“It gives these firms an opportunity to think like a technology business as opposed to thinking like a bank. What can be bolted on to the existing technology to make the firm more efficient?”
Transforming your data
This requires what Pomerol refers to as a “dashboard champion”. Data should be owned and driven in the front office not by technology people but business people; a business intelligence expert that can direct the IT team on what to do, where to bring the data in from and align it in order to start using the dashboards effectively.
This is where incremental improvements slowly lead to meaningful aggregate gains as new business insights emerge.
“The dashboard champion should hopefully use the dashboards to make better trades, save costs etc. That then creates appetite among their colleagues to start using the dashboards. Once that widespread adoption of the dashboards has been achieved, trust starts to build, you own the business intelligence and this transforms the infrastructure,” says Hefer.
Critical to this stage is that the information is accurate. What is required is a checking mechanism to shine a light on bad data. This can be achieved with what is known as ETL technology (Extract, Transform and Load) as more and more disparate data sets are combined to provide a fuller picture of a firm’s operations.
Simply put, an ETL engine allows firms to align disparate data at a level where they can make commercial insights.
As Pomerol’s white paper states: “Your front office users will start trusting their dashboards when they truly understand the limitations of their data – including the proposed remediation.”
All adjustments to the data have to apply to the lowest level of granularity. That means, myriad data that might arrive at the Finance & Risk department, to which various adjustments are made for regulatory purposes, must reach the front office seamlessly so that trader know what impact a trade will have on the market and regulatory risk of the firm.
This can lead to what Hefer refers to as executing or trading based in line with return on risk-weighted assets (RoRWA): “That’s something that few firms can do. It allows you to look at things from a net profit rather than gross profit perspective – with your risk position concurrently considered.”
“By accessing different sources of data on the same client it drives better decision making. But you have to make sure that the information on that client is accurate – which is where the ETL technology comes into play,” says van Rooyen, who illustrates the point by adding: “One prime broker we spoke to had a client for whom they wanted to increase their trading costs or get them to reduce their risk-weighted assets in order to meet the benchmark profitability rate. However, the broker didn’t have a view of the FX business where that same client was actually highly profitable for the bank. They didn’t have a complete view of the client, which we were able to achieve for them.”
This is where the Rubik’s Cube analogy comes into effect. By re-aligning the data infrastructure and getting the right building blocks in the right place, financial institutions have the ability to transform and challenge their data infrastructure to become bigger, better, faster, and drive meaningful results across the firm.
Indeed, when Pomerol Partners refers to the third part of the reactive iteration phase as “challenge the infrastructure”, what is meant here is that the front office does not throw their business logic and data over the IT fence but regains, and then retains, ownership of both. That is why having key business personnel championing the dashboards is vital.
“This is another fundamental step which the front office needs to take towards the data age. An organisation’s IT strategy, including the choice, evaluation and adoption of new technologies, should be challenged from a business perspective. It is also a cultural shift which will enable trading desks to operate more like technology start-ups than slow adopters of progress and change,” opines van Rooyen.
As Hefer concludes: “Our white paper attempts to show firms how to fix their technology. To achieve the transformation in their infrastructure so that they can make proactive decisions rather than being reactive. We have the ability to align a firm’s technologies. We want to show how to take a current liability and turn it into an asset that can deliver a competitive commercial advantage.”
To read Pomerol Partners’ white paper in full, please click here
Embedding dashboards into a prime broker’s CRM application
One of Pomerol Partners’ prime brokerage clients embedded financial dashboards into their client relationship management application, which enables their reps to access the dashboards via their mobile devices. In this particular example, the client rep was called in to discuss a reduction in the Euro pricing for one of the client’s flagship funds. Using the dashboard they calculated the yearly P&L impact of a 2.5 bps discount on a 3 per cent increasing balance.
Additionally, the dashboard highlighted that the bank had a more favourable margin on Dollar based funding. They instantly granted the discount on the premise that the hedge fund would increase their Dollar balances by USD275m on their North American value fund – allowing the prime broker to term the funding. The increased Dollar balances more than offset the initial Euro discount – resulting in a saving for the client, more P&L for the prime broker with the only loser being the prime broker’s competitors for the Dollar balances.
The dashboards enabled the rep to do this all whilst on-site at the client – something that would normally take at least a few hours to turn around.