Although financial services firms need robust data management to meet growing regulatory pressure and drive new market opportunities, many still face challenges when it comes to implementing effective big data strategies. Capgemini Consulting and Efma recently published a report which identified that half of all financial services executives mention ineffective coordination of big data and analytics teams as the biggest challenge in big data implementation. However, only 16 per cent of these firms have appointed Cheif Data Officers (CDOs) and many are still focusing heavily on compliance issues and are not taking full advantage of the real potential of data analytics. Ramana Bhandaru, Vice President, Capgemini Financial Services tells bobsguide why the CDO will be will be vital in driving the success of big data initiatives for financial services firms.
What existing challenges are companies facing when it comes to implementing effective big data strategies?
The existing challenges that companies are facing include lack of skill set, data security, data quality and organisational alignment:
Lack of Skill Set: The demand for experienced Big Data professionals has exceeded the supply. Given the pace at which the Big Data technologies are emerging, this is likely a trend that will continue. With regards to data security, Big Data strategies are centered around provisioning large data sets from an enterprise Big Data hub and running analytics on these datasets. The tools for securing the data to meet compliance requirements around credit card data, medical records, credit history and personal information are not as mature as the tools provided by the traditional databases making data security around Big Data a challenge.
Data Quality: Data being loaded into a Big Data platform will only yield good analytics and results if the input is of good quality. The data in existing platforms ranges in quality at present, and this is presenting challenges.
Organisational Alignment: Big Data strategies are also geared towards implementing customer-centric strategies like Customer 360, which are difficult to implement with lines of businesses operating as independent units. Organisations have to be re-aligned in order to transition from a traditional product-centric approach to a more customer-centric approach. The re-alignment has to be both top-down and bottom-up, requiring significant organisational change.
Capgemini and Efma identified that half of all financial services executives cite ineffective coordination of big data and analytics teams as the biggest challenge in big data implementation, what can companies do to improve this?
Traditionally within large organisations there has been a clear separation between the IT teams and the analytics teams in terms of organisational hierarchy. IT teams fall under technology and the analytics teams fall under the line of business. In very large organisations, there are often support and enablement teams that facilitate co-ordination between these two groups. This organisational structure has worked well in the past, since the overlap between the IT and analytics roles have been minimal.
Now however, with Big Data emerging as the platform for analytics, the line between these two groups has started to blur in terms of skill set. The requirements for analytics roles have changed significantly; data scientists now have to have a strong grasp of Big Data technologies (in addition to statistics and traditional SQL-based tools) in order to create the analytic models. The technology teams have acquired the Big Data skill set faster and can bridge the gap on the analytic side by hiring data scientists or training people internally.
This shift has started to create a power struggle between the organisations in terms of ownership of analytics.
A new organisation that combines these two roles under common leadership is required to overcome these challenges. A re-organisation effort for such a transition to take place is likely to result in redundancies and voluntary exit of key people. However, this should not deter companies from undertaking this transition. Similar movement of individuals from competitors will also happen and will result filling up the lost skill set.
Why will the Chief Data Officer (CDO) be vital in driving the success of big data initiatives for financial services firms?
Data is a core asset for an organisation. With the emergence of Big Data, the volume of data that an organisation uses for analytics will grow exponentially. The source of data and the type of data that is being generated is changing as well. Data from external sources, machine and sensor data, structured and unstructured data will become a part of the data assets that are going to be a crucial aspect of an organisation’s decision-making machinery, transforming companies into data-driven businesses.
While Big Data technologies are still maturing, there is a spotlight on data security, data quality and data governance specifically, due to the recent incidents at well-known companies. The impact of a data breach can result in significant financial, as well as reputational, losses.
Given all these factors and the importance of data, having a strong leadership role, such as a Chief Data Officer, is as vital to an organisation as leadership roles in areas like Finance, Marketing or Risk.
How can CDOs achieve success?
With the introduction of Big Data capabilities in organisations, the demand for rapid access to more data has increased exponentially. Data science and analytics organisations tasked with deriving value and insights from the data are looking to eliminate the traditional IT cycles for access to the data. Instead, they are demanding self-service data discovery and business intelligence capabilities. This makes a CDO’s job difficult from a data management point of view. A sound data governance practice that clearly delineates between sensitive and other data while having the proper security and privatisation capabilities in place becomes critical to a CDO’s success. In summary, a CDO can achieve success by upgrading their capabilities to keep pace with this rapidly evolving data landscape.
The report found that 79% of financial services executives believe that the ability to extract value from big data is an important factor in future success – how do you think companies can best find and use the value in their big data?
Big Data can be leveraged for strategic initiatives, such as implementing customer 360 and ‘know your customer’, for deepening customer relationships and understanding the customer better, so that the right products and services can be offered at the right time and through the right channel. In addition, the profitability of products and services being offered can also be measured with increased accuracy.
At the same time, Big Data can be leveraged for various other use cases, like increasing operational efficiencies in the call centers, fraud detection, reporting and many other areas of analytics across all lines of business.
How do you see the big data landscape developing in the future?
Most organisations have adopted Big Data technologies and are going through a cycle of stabilizing the platform. In the next iteration, we’ll start seeing analytics being ported to Big Data platforms. In addition, the core Big Data platform tools are undergoing a transformation and moving from batch oriented processing to real-time processing with the introduction of tools like Storm and Spark, which open the doors for additional use cases that can leverage Big Data.
IT vendors are also pushing strongly towards leveraging cloud platforms for Big Data analytics. We could possibly see the Financial Services market moving data from on premise to cloud, but there is some work in terms of how security concerns will be dealt with before this happens.
What should financial services firms be doing now to prepare and innovate for the future?
The organisational structure is key in order to be data driven. Financial Services firms must make the transition from being product-focused to being customer-focused as a means of retaining and increasing revenue. Firms should strive towards making the customer experience consistent across all channels and provide a truly digital experience. Laying a strong foundation for Enterprise Information Architecture that incorporates Big Data and analytics capabilities will be a key to their success.
What impact do you see new non-traditional players who are using big data effectively, having on traditional financial services firms?
Non-traditional players have demonstrated that technology can be disruptive enough to break the barriers to entry in the Financial Services business. By leveraging Big Data and analytics effectively, these players are able to provide a more personalised user experience at the point of relevance for the user.
Traditional Financial Services companies have the advantage of already having sunk costs in assets and platforms like ATMs, Banking Centers etc and most importantly their brand value that has been built over decades. What is lacking is the cohesiveness in the way these assets are leveraged for providing a personalised and unified customer experience. For example, the mortgage department of a bank may have denied a loan application to an applicant while the marketing department is still sending out offers to the customer. All of these challenges, along with the disruption introduced by non-traditional players, has resulted in traditional Financial Services firms launching into digital transformation initiatives that will have to make effective use of Big Data and analytical capabilities.