Bottling Other Industries’ Expertise
Article in Waters Technology with comment from Simon Asplen-Taylor, 1st November 2012
Author: Nicholas Hamilton
Source: Inside Reference Data | 01 Nov 2012
It is widely acknowledged that data management in the capital markets is less advanced than other industries, but what are the reasons for the difference? The retail, telecommunications and energy industries have some lessons to teach, Nicholas Hamilton finds
At a Financial Stability Board (FSB) workshop in October, one speaker began his presentation by suggesting that if data was managed in the airline industry in the same way it is managed in the capital markets, none of the delegates in the room would be alive today. Such observations about the inferiority of data management in the capital markets have become common at industry events, where delegates often marvel at the great advances seen in other industries.
By asking representatives from outside the financial services industry to share their experiences and expertise, the FSB has demonstrated its determination to ensure the legal entity identifier (LEI) is on a par with the best data standards in any industry. However, its decision to invite companies such as Coca-Cola, Nestlé and BT to provide input raises questions about why data management is comparatively immature in the capital markets and what else can be learned from other industries, such as retail, energy and telecommunications.
Chris Pickles, London-based head of industry initiatives, global banking and financial markets at BT, says much higher failure rates are acceptable in the capital markets than in other industries. “If financial firms have an error rate of 2%, they consider they are doing pretty well,” says Pickles. “[In the telecommunications world] that would mean that two telephone calls in every 100 would not get to the person you wanted it to reach. They would either not get to anybody or they would go to somebody else altogether. So a 2% failure rate bears no relationship to manufacturing and commercial industry service levels.”
Pickles says there is “industry-wide inefficiency” when it comes to data management in the capital markets. He says data management systems at financial firms are often “primeval”, but as long as they can be kept running and they are not causing the loss of a significant number of customers, firms are reluctant to replace them. He believes one of the reasons for this is the bonus culture at financial institutions, which is based on the financial year. “People ask: Does it impact me this year? Does it impact my bonus this year? If it doesn’t impact my bonus this year, then it is not a priority for this year. That then puts things off until next year. And if you are lucky that may not happen next year either,” explains Pickles.
Reluctance to change has led to some remarkable anachronisms, according to Pickles, who found one bank that took 30 years to respond to the decimalization of British currency. “When the time came for decimalization, they hadn’t planned ahead because it didn’t impact bonuses,” he says. “So they were still working in pounds, shillings and pence nearly 30 years later. When you paid money into the bank, they converted it electronically from pounds and pence into pounds, shillings and pence and when you asked for your money back, they would convert it from pounds, shillings and pence back into pounds and pence again. That was 1999. And the UK went decimal in 1971.”
Simon Asplen-Taylor, head of the banking and capital markets advisory and consulting team at IMGroup, believes part of the problem in the capital markets is due to data silos created because top-level executives do not have enough interest in data and leave others to take ownership of it. He gives the example of the head of risk at a firm laying claim to data because they believe they are the main users of it. Because the head of risk is a senior employee, no-one dares to question them. “One of the key reasons why some things aren’t happening is because the data is not owned by the organization, it is owned by individuals,” says Asplen-Taylor.
The absence of strong data governance means individual businesses develop their own data sources to fulfill short-term objectives. But the resulting data silos create inefficiencies, inaccuracies and duplication, and make it more difficult for an organization to achieve its strategic goals.
Asplen-Taylor says the federated nature of global financial firms means they are more likely to develop silos. In other industries, such as retail, corporate business structures are more common and silos are less of an issue. “Tesco [the supermarket chain] is a very successful company with a corporate structure. At Tesco, everything is owned at the corporation level,” says Asplen-Taylor. “But an investment bank is split by its geographies and its asset classes. In an investment bank, the head of equities is probably more powerful than the chief executive, because ultimately they own the clients and the business. So the difference comes down to a federated model versus a corporate model.”
Dennis Smith, managing director, advanced engineering group, at BNY Mellon in Pittsburgh, thinks data managers in other industries have been making progress because they have been quicker to adapt to the challenges of unstructured and semi-structured data. “A lot of the activity of capital markets firms is based around structured data and a lot of the newer challenges are in the areas of unstructured or semi-structured data, often involving social media data. Other industries have needed to deal with social media data, unstructured and semi-structured data earlier than many of us,” says Smith. “Don’t discount that many companies in other industries, particularly the energy industry, have recently had more financial resources than banks.”
Smith believes data managers in capital markets firms can learn a great deal from the energy industry’s data management techniques, particularly in relation to big data. “A lot of what is happening in big data technology is driven by the requirements of oil, gas and other energy companies,” says Smith. “This is because they have been quick to pull together a lot of unstructured data from a lot of different sources, with massive volumes. Many are using the technology to manage their massive energy grids.”
Smith says financial firms can learn how retail businesses capture social media data and use it to perform sentiment analysis. Asplen-Taylor says telecommunications is also good at using social media data to identify potential customers for their products, and that they understand the relationship between different users of their service. “I have three sons, each of whom has a mobile phone. If one of those boys had a problem with their mobile, the phone company understands that I actually am responsible for their accounts,” says Asplen-Taylor.
Pickles believes the most important thing the financial services sector can learn from the telecommunications industry is the importance of developing good open standards and adopting them wholeheartedly once they have been created. “Don’t think being non-standard locks in your customers: it just locks in cost and locks out business opportunities,” says Pickles.
The financial services industry still tends to do things in a piecemeal, non- standardized way, which holds it back, he adds. “The key things behind a standard are adoption and understanding that, once you have a standard that is already widely adopted, it is a good thing for other people to use it, because then you get the economies of scale that go with it,” says Pickles. “The barcode is a classic example of a system that starts off small and ends up being the identity system in any supermarket or corner shop you go to in the world. It can be used to identify every element of a transaction, even down to identifying bundles of money being moved from one bank to another.”
As data managers at capital markets firms set about trying to advance their industry, one thing is clear: they have no shortage of examples in other industries to inspire them.