How big data is set to boost the effectiveness of analytics in banking
Jaroslaw Knapik, Senior Analyst, Financial Services Technology
Big Data will dramatically enhance key areas in banking such as fraud analytics, customer analytics, and web analytics. It does not replace banks’ current analytical infrastructure, but extends its scope: it has become conceivable to conduct analyses based on all data, not just a sample.
Big Data also extends the range of data types that can be covered, the problems that can be addressed, and the user groups able to use it within a single organization. Beyond the familiar base of transactional data and text, emerging technologies and frameworks are providing banks with the power and tools to digest digital and physical channel interactions and various types of data such as customer data, graph data, and geo-location data.
This is not just a matter of the data being available, or of technology seeking a problem to solve. Data from customers, banking channels, back-office systems, and third-party sources can yield significant insights that are useful for many activities such as customer marketing, risk management, and infrastructure optimization. The use cases from Bank of America, Citi, and Zions Bank detailed in Ovum’s recently published report Examining Use Cases for Big Data in Banking show how Big Data tools are used today and what the banks’ developments plans are. This is only a small sample of potential use cases for Big Data in banking in the longer term.
Big Data should be an investment priority for many banks
Banks face many challenges, such as difficulty in driving revenue growth and managing risk and compliance, but they are investing in technology to enable them to address these issues. Results from Ovum’s ICT Enterprise Insights survey show that the top IT projects of retail and corporate banks and wealth managers are managing enterprise risk, security, and compliance, and exploiting information for business insights.
Although there are a number of technology solutions in place for these activities, Big Data projects specifically enhance areas such as web security, compliance checks, and customer analytics, and these are the key benefits of Big Data in the short term. Leading banks have already initiated relevant investments, and many of their smaller peers are likely to follow.
Spending on management information systems (MIS) will reach $9.3bn by the end of 2018
The market size of management information systems (MIS) in 2013 – including data collation, analytics, and reporting systems – to support banks in areas such as distribution, risk, finance, and compliance is estimated at $6.9bn globally (including software, hardware, and services). This is around 5.7% of technology spending in the retail banking industry.
After spending growth of more than 4% between 2011 and 2013, growth is expected to accelerate between 2014 and 2018, with the growth rate ranging between 5.3% and 6.4%. Ovum estimates that by the end of 2018, overall technology spending on MIS in the retail banking industry will reach $9.3bn, representing 6.1% of overall technology spending within the industry.
Delivering a Big Data project requires collaboration and change across the business
Implementing an effective Big Data solution requires a combination of people, processes, and technology focused on the desired business outcome. It starts with people taking ownership of the data and processes that belong to their business; data can be trusted only when there are people directly accountable for its accuracy, timeliness, and completeness. There must also be formalized processes to govern data through its entire lifecycle, from data entry to validation, integration, maintenance, and end of life.
With stakeholders from the business and IT formally involved, and with processes in place for the consistent management of data, organizations can start data analysis projects by applying technology to automate and integrate data and enforce business rules and policies throughout the lifecycle.
Big Data changes the approach to data governance and quality: data does not have to be processed beforehand, but analysts can work with all the data and often focus on achieving results that are broadly accurate and the result of iteration, depending on the applicability to a business case. This requires quite a change in mindset, particularly for risk and finance functions where ensuring data quality and traceability is very important.
The type of data will dictate the architectural and analytic approach to analysis
It is essential for banks to know their data, understand it, and understand the characteristics of the data that they need. If you have a business case, understanding whether your problem involves structured or “unstructured” data will dictate your architectural and analytic approach. Banks need to look to capture more information than they are used to, going beyond risk and marketing data, and treat it as an enterprise asset.
As institutions are not used to working with this new data, they tend not to think of analytics when creating new systems. Users are likely to perform a rough analysis first, and then use the results to guide them in refining the analysis. These strategies are most effective when users have a good idea of what kind of data is actually available and its level of quality and validity. Such an approach does not replace banks’ current analytical infrastructure but extends their ability to analyze more data and gain insights that were previously difficult and costly to achieve.
The post Big Data set to boost the effectiveness of analytics in banking appeared first on Ovum.
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