Please solve the following qs with reference to the case study below Qs. Firstly, read the Big Data Analytics for Financial Services article under appendix: 'Are Financial Services coping with Big Data? Warwick Bailey, Vice President of Business Intelligence at Barclays, recognises in the article that Financial Services are almost being 'strangled' with regulation and compliance but still sees options for big data to keep a foot in the door when it comes to wider business needs and revenue generation. To what extent you agree or disagree with Warwick Bailey above recognition? You should subsequently critically discuss the relevance of using this article information to assess the importance of Big Data analysis for financial organization measuring business performance.

Case Study: Are Financial Services coping with Big Data? Possibly as a result of increasingly competitive customer experience efforts in other markets, customers now expect a more personalised service from their banks than ever before, and one with more assurances that they are getting the best deal. The recession has not only made the customer more cautious when it comes to investment - regulators have also taken on a more involved and stringent route approach to oversight. To cope with these expectations, data and analytics have become seen as the only viable keys to progress, with the volume, growth and exploitation opportunities of data in constant surge. Like most markets, Financial Services are only beginning to unlock the real value in in big data, but most recognise that this is a fundamental step towards shaping business strategy - employing factual insight rather than having to rely on trials and concepts. Not only does big data promise to reduce the level of risk, it means more time and money can escape the dreaded swirl of the black hole and instead be more readily set aside for where it is most needed - such as into more advanced big data-related skills and technology. However, where financial services are often coming unstuck is in appropriately strategizing their venture into this field. Any hope of risk mitigation can be gravely wounded if poor planning results in the wrong tech, the wrong people, or the wrong application of analytics. Likewise, a delay in getting up to date with the demands of big data can have a knock-on effect for years to come, meaning organisations need to get it right early to avoid the realisation that their foundations are naught but sand. Trying to modify an existing approach can often be more difficult than starting from scratch, while business cases are best built around long-term propositions. Warwick Bailey, Vice President of Business Intelligence at Barclays, recognises that Financial Services are almost being 'strangled' with regulation and compliance but still sees options for big data to keep a foot in the door when it comes to wider business needs and revenue generation. "There's nothing wrong with compliance and regulation because it needs to happen throughout the industry. It's just not an exciting place to be. It doesn't grow the business. It retains trust by producing compliance value. Initiatives I've seen over the last 3-4 years are Basel-type initiatives, about how to understand the data providing detail behind the capital requirements of a bank "The issues I've had have always tried to marry up the compliance element with the growth element. You've got something to really grab hold of from a business-case perspective. It's not just managing data from a compliance point of view, you're also looking at profitability, at credit risk analysis, customer opportunities, and so on. It's pulling together these silos we often see across the financial services world. If you've got that [marriage], you've got a long-term proposition for any project." Of course, where opportunity awaits, there also lies risk. Big Data provides an obvious benefit to security and surveillance in the financial sector, particularly in the wake of a number of large-scale breaches that have hit headlines in the past year, but these breaches have often been a result of storing large amounts of sensitive data without resilient security considerations. "I see two focus areas for big data in the surveillance space," says Theo Hill, head of data and technology strategy at UBS. "Rapid filtering of huge data sets to produce alerts that can be actioned quickly in a call centre style manner, and enabling a holistic overview of employee or client behaviour." "Organisations should have a robust data leakage detection programme and maintain controls that make it hard for individuals to remove data from their network. They need to flag individuals with enhanced privileges on their data lakes so that data leakage - or other issues - involving them are always investigated as high priority." As the field progresses at pace, there is some debate as to how much of the analysis and output can be done by machine rather than man. However, while big data may streamline processes and generally trim the fat from a lot of manual work, the demand for skilled analysts is increasing. "I don't think that there is necessarily a huge contention between automation and skilled staff," says Hill. "It's just that the skills required for some roles will change. Operational staff will need to become more data savvy, and the distinction between many IT and non-IT staff will reduce." Bailey also sees a long-term role for the intelligent human, but one that is currently proving difficult to fill. "Resources are the biggest issue for us. We need people who can interpret data, who can talk to the business and the IT communities, and can make the data easy-to-read - seeing the wood through the trees, in other words." Within the next year, it will become increasingly apparent as to who is doing Big Data 'right' and who is lagging behind. Meanwhile, as financial services look to get a firm hold on Big Data, organisations in this sector must also have an eye towards IoT and predictive analytics, where the data commercialisation will continue into its next stage of evolution. Machine learning will undoubtedly accelerate (most notably to combat fraud and risk) and data governance, lineage, compliance will become more deeply integrated with Big Data Platforms, with banks developing or purchasing point solutions. The Big Data for Financial Services conference (London, July) will be looking at these issues alongside the experts, with a particularly keen eye on what the next wave of progress will mean for this sector. "The next step is all around bringing it back from big data and into small data," says Bailey. "Progress is about making it real for businesses, bringing that big data challenge to a smaller audience and smaller solution. Bridging that gap. Not being able to access all of the data, but some of it. To understand how to bridge the gaps and build up that business understanding." "Big Data still requires good data quality to produce effective outcomes," Hill adds. "That's something that is rare in many financial service systems. If your system isn't tuned effectively, there is a strong risk that you increase rather than reduce the manual burden, as each output needs to be worked. At this year's conference, I hope to get a view of how other FS organisations handle cross-border data restrictions."