While we may look enviously on the success stories, the challenges of actually implementing a Big Data strategy are many.
TThe Financial Services industry thrives on a whole host of data: transaction data, customer data, market data and social media data, among others. As computing power has evolved to be able to process greater quantities and types of data at greater speeds than ever before, many financial services organizations have been waking up to the potential opportunities.
Over 70 percent of banking and financial markets firms say that information and analytics is creating a competitive advantage for their organizations, according to a recent study from IBM Institute for Business Value in collaboration with Said Business School at the University of Oxford.
THE POTENTIAL USE CASES WITHIN FINANCIAL SERVICES ARE HUGE, ESPECIALLY AS THE MARKET MOVES FROM "PRODUCT CENTRIC TO CLIENT CENTRIC."
The potential use cases within financial services are huge, especially as the market moves from "product centric" to "client centric." For instance, banks are already using Big Data for fraud detection (and prevention), customer insight, regulatory compliance and reporting, and improving operations.
However, while we may look enviously on the success stories, the challenges of actually implementing a Big Data strategy are many.
So what's holding financial institutions back from realizing the potential of Big Data?
1. Lack of Leadership Support
Senior management are often not convinced completely about the benefits of Big Data, and thus are not willing to invest in/build robust tool(s) to deliver integrated solutions. As with all initiatives, you really need to demonstrate the benefits that investing in one particular solution over another would yield. When it comes right down to it, hard numbers is what will speak to your senior leadership teams.
2. Company Culture
Internal politics, performance review structures, and organizational structures can impede the progress of Big Data as business units and individual managers may feel uncomfortable with the open sharing of data. While it's impossible to change company culture overnight, you need to be aware that some people might not be as excited about a more open sharing of data, and look at how you can work with Human Resources to ensure that performance schemes are aligned with the brave new world of open data you're going to be introducing to your business.
3. Lack of Integration (data is distributed in legacy systems throughout the organization)
Fragmented business processes and distributed data (e.g., data in legacy IT systems) creates big challenges for Big Data. Many banks, for instance, hold large reservoirs of important data in their legacy systems, and these systems do not work easily with systems like Hadoop. This is not a challenge that can be easily tackled; it requires time and investment to extract 0the data out of these old systems and put it in a useable form, and this needs to be factored into your Big Data initiatives.
4. Regulatory Requirements
After the 2008 economic meltdown, there has been an increase of new, strict regulations (e.g., Dodd Frank, Basel III, FATCA) resulting in complex rules governing access to critical client data. Thus it has become difficult to negotiate the maze of regulations around data access. Time to get your legal department involved.
5. Business Silos and Modules
Financial institutions, banks and insurance companies are mature organizations that have often gone through multiple acquisitions over the years. This creates very silo'ed business modules and hidden barriers to Big Data. These structures, like legacy systems, are likely to slow your implementation down and should be factored into your plans.
6. Data Security
Protecting data from security risks is a massive concern for financial services organizations and needs to be built into any Big Data projects, adding an additional layer of complexity.
7. Data Quality
Poor data quality can result in defective analysis. If managers do not trust the underlying data, they will not trust the resulting analysis. There are many steps organizations can take to improve the quality of both structured and unstructured data being used in analytical systems. For unstructured data, for instance, organizations can use ontologies with end-user inputs, semantic libraries and taxonomies, while for structured data it's crucial to emphasize the ongoing importance of inputting the data correctly.
8. Lack of Talent/Experience
Because this is a relatively new and emerging area, there is a scarcity of people with the required analytical, technical and business skills to generate business results from Big Data. Equally, there is a dearth of people with prior professional experience actually implementing Big Data systems. Clearly, as universities catch up with changing business needs and more companies embark on Big Data strategy, both of these limitations will gradually be overcome naturally.
You can find out more about a proposed model to bring together a more integrated business architecture to support a Big Data implementation in my whitepaper Capitalizing on Big Data in Financial Services through Integration & Optimization.
But what do you think? Have you encountered any of these challenges in your Big Data implementations? Which one posed the greatest barrier?