Big data analytics is touted for its ability to help organisations in the financial services industry to improve their customer experience, manage risk, more accurately detect fraud, and deliver a host of other benefits, says Gary Allemann, MD at Master Data Management.
However, financial services organisations also need to comply with a variety of regulations, both local and international, around access to information, client privacy and so on. The conflicting demands of driving as much insight as possible from big data while complying with legislation need to be carefully managed to ensure big data is used in a responsible fashion to drive the insights required.
Data governance and data quality are key in achieving the benefits of big data while meeting compliance objectives.
Insights derived from the analysis of big data can assist financial services organisations in a number of areas. However, privacy, particularly privacy of customer information, has become increasingly important in recent years.
For example, in South Africa, the Protection of Personal Information Bill (PoPI) has been signed in to law, promising severe penalties for companies that abuse personal information. PoPI is a data governance bill, governing the full life
cycle of personal data from capture to destruction. In light of this and similar legislation governing data usage, it is vital to ensure that any use of big data is appropriate and does not conflict with compliance objectives.
These challenges are exacerbated by the relative lack of practical experience of South African professionals in big data implementations. Master Data Management’s Gary Allemann is chairing the big data and Analytics in Finance
Conference, taking place at the Conrad Hotel in Dubai from 1 to 2 April 2014.
The conference features practical case studies from some of the world’s largest financial institutions on the application of big data to improve the customer experience and manage risk. The experts will also cover the importance of data governance, compliance and data quality to their big data successes.
