WestLB created such a position – data quality officer, as the bank called it. The first holder of the title was Dr Marcus Gebauer, located within WestLB’s group finance division. Each data project had the same starting point – asking data users where problems existed, to define what they expected, and list what they did with the data. ‘Some outcomes of these roundtables have been quite surprising,’ said Dr Gebauer. ‘We had different business units using the same data with different goals, expecting different content and data fields. This meant in the end that the data simply couldn’t be correct for the many business units.’ There can be resistance to the work of the data quality officer – ‘you’re asking people to blame themselves; they have to tell you what’s going wrong with their own business unit’. Without the support of each of these, his work would not be successful. When setting up a data quality programme, his advice was to have a definite implementation plan and structure. This should start with a few quick wins. ‘You have to start with a small project where you can work with and rely on the people and have easy contact with them.’ But he stressed the need for anyone undertaking such a project to also have an aptitude and a plan for change management. He supported his argument by paraphrasing Charles Darwin, who said the most successful lifeform is the one that is able to change. Dr Gebauer put it in context, adding that you can’t have a simple, straight pathway with data quality at the end, ‘because in between setting out and arriving, someone or something will re-direct you’. You have to be flexible, he stated, ‘with so many resistances you have to find your way through these barriers’. Dr Gebauer felt it was vital to understand what a bank,


as a business, wanted from its data. He had learnt that the quest for quality per se was nothing more than a marketing- led distraction. The main aim of most businesses is to make money; quality is merely a means to an end, he believed. But achieving this real aim can still result in a variety of approaches. When Dr Gebauer talked to WestLB’s executives, he had to find out what really drove them: ‘It definitely isn’t data quality’. So although his job was to look after data quality, he knew he was doing it for the bank’s commercial gain and not for the sake of data quality as an end in itself. Part of his professional skill lay in extracting this truth. Of course, the effectiveness of all of this at WestLB could be debated, given what subsequently happened, as it was a high-profile casualty of the financial crisis and was broken up in 2012, following its bail-out in 2008. There were a number of data-related lessons within


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Daiwa Securities SMBC’s major architectural overhaul (see below). The migration task proved to be a tough one, with the counterparty data much harder than the instrument data. If possible, it was concluded, data and processes should be kept simple and strong data governance is essential. From a corporate culture perspective, there was the need for buy-in from the business, at low level as well as senior management level. A quick win can help, converting doubters and making sure everyone keeps the faith. In fact, Graham Hare, head of finance and operations technology at what is now Daiwa Capital Markets Europe, viewed the bank’s EMU and Y2K projects as ‘early skirmishes’, bringing into focus what was already known – key data was not centralised. Ad hoc databases and manual procedures had sprung up because the existing architecture did not support all reporting requirements, leading to operational constraints. The move to centralised reference data was the first of three stages of work, running from June 2003 to June 2006, followed by a single financial database and then a single settlements infrastructure. The steps for reference data included creation of a standardised model, with detailed analysis of attributes, data cleansing, the introduction of middleware for reliable data delivery, creation of XML grammar for all standing data entities, and the use of publish/subscribe technology via adaptors.


Among the lessons, there was insufficient belief at the outset, so a need for strong sponsorship, ideally with the leadership coming from the business. The units themselves proved reluctant to commit resources. The standing data working party was initially too large to facilitate ease of decision-making and there were ‘too many entrenched positions’. The project teams then also lacked business input, analysis took too long, partly because it had to work around other commitments, and it felt too much like an IT project. There is a need to stress that reference data is part of an


overall programme, a foundation for future progress, said Hare. Such projects are not glamorous nor high profile, except at the budget stage! The worst case is that core systems are adversely affected, bringing complaints; the best case is that it is business as usual. But the projects can bring efficiency gains through improvement in underlying data quality, simplified data distribution, reduced costs of support and future scalability, improved management reporting and the introduction of measurable processes. Key questions are: what data types should be held and what attributes; what should be distributed and where to;


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