An n introduction to Deployments Factory’s quality factory
Brussels, November 2009
Presented by: Thibaut De Vylder, CEO of Deployments Factory SA e-mail: mail: tdv@depfac.com
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1 Introduction In the financial service industry, the quality of data is pointed more and more as a recurring issue. The poor quality of critical data leads to corporate decisions based on on potentially misleading information,, and also has a direct cost. In a Basel 2 context, for example, defaults are are used each time a data is missing ing which can induce huge additional costs in capital!
2 Evolution of the requirements The renewed regulatory pressure linked to the recent financial crisis has: • increased the amount of data required; • increased the number of versions of the data and the related capacity of storage required; • changed the definition of ownership & responsibility of the data; • justified the creation of new reporting chains and flows to produce new reports. This new evolution has two major consequences. First, the complexity linked to the control of the systems is growing exponentially. Second, the complexity creates a risk of non coherence between the information produced by distinct information chains. The combination of these two factors has tremendously increased the need need for very strict data quality control and improvement processes. processes Yet, while massive money is invested in the maintenance, upgrade and replacement of the systems that feed the operational, tional, tactical and strategic decisions, very little investment is made to improve the quality of the data processed in these systems. The paradox lies in the fact that many data quality activities are performed within various groups such as IT, Finance, Risk, the business... working in isolated silos. Today’s managers tend to agree that this is becoming unproductive for companies committed to handle exponential complexity. Regarding the coherence between separate reporting chains, let’s take the following example: if a Basel 2 report shows an x billions outstanding for the corporate clients, we need to be able to explain why and how it corresponds to the y billions disclosed in the accounting statements. Failing to explain these differences may be wrongly interpreted by stakeholders such such as regulators and notation agencies. Failing to understand these differences may also have other undesired effects on the decisions taken internally. For these reasons and since the stakeholders expectations will continue increasing in the upcoming months and years, data quality should become a major concern at every level of the organisations organisations. Confidential
© 2009 Deployments Factory
Page 3 of 3 It should first be the concern of the persons in charge of “business as usual” activities.. Besides, programs and projects impacting the chains, such as migrations migration or new releases, must also guarantee high data quality before and after af their impact on the chains. The answer of Deployments Factory is to implement a pragmatic solution: the quality factory.
3 DF’s quality factory Our concept of quality factory has successfully supported the implementation of data quality continuous improvement processes in va various corporate reporting environments (Basel 2, accounting systems, rating systems...) like ABN Amro, BNP Paribas Fortis (Belgium) and Fortis Bank Nederland. Our experience is based on the following facts: • data ata quality must be considered in various dimensions: integrity, accuracy, completeness, interinter chain coherence, intra-chain chain coherence, time…; time… • measures easures based on agreed upon definition must be implemented in various systems; • KPI’s can be built on these measures; measures • recurrent ecurrent analysis of these measures together with ad-hoc hoc analysis may identify many sources of non quality; • controls must be organised to ensure that the issues are resolved on a long-term term basis. Our commitments are: • A non intrusive and independent approach • Valid for one system or many interconnected systems • Unique generic methodology • A cost effective and fixed-price • Highly qualified professionals • Automated tools & Partnerships wit with best key players
4 Conclusion Implementing a strong data quality framework in an organisation organi ation does not require expensive IT investments. On the contrary, changes in procedures coupled with an appropriate communication are much more effective. The quality factory will help you make these changes come true. Confidential
© 2009 Deployments Factory