Data Management

12:05 PM
Promod Radhakrishnan, Oracle
Promod Radhakrishnan, Oracle

This Is Why There Is A Lack Of Adoption Of Big Data Solutions In Financial Institutions

Financial services organizations focused on institutional clients don't have the volume or variety of data to support big data investments.

Financial services organizations focused on institutional clients don't have the volume or variety of data to support big data investments.

Promod Radhakrishnan, Oracle
Promod Radhakrishnan, Oracle

Much has been said and written about the confluence of social media, mobile (platforms), analytics and cloud driving the next round of innovation and a further surge in efficiency gains. While several industry sectors, notably retail, have jumped full steam in to all four of these supposedly game changing vectors, financial services firms have been a bit more circumspect about any drastic commitments in these areas. One notable exception, however, comes from retail financial services shops that have invested in the mobile (platforms) and analytics areas. It is interesting to note the various perspectives regarding each of these vectors when it comes to firms more focused on commercial and institutional customers, especially when it comes to the right approach for leveraging new technology for data management and analytics in the big data domain.

There is a tremendous level of adoption of 'traditional' high-power database appliances, but the same cannot be said about pure-play solutions focused on the big data domain. Senior stakeholders who are tasked with running high-power technology teams supporting portfolio managers, traders or for that matter commercial loan front-office teams, do not in most cases have the time, energy and most importantly money to invest in utopian big data and visualization projects in the absence of a clear demonstrable business case.

There are sufficient, if not abundant, options for backend data infrastructure to handle big data across the structured and unstructured domains. While some options focus on an end-to-end stack offering for supporting industrial-size volumes, other options imply a best-of-breed approach with a mix of hardware and software options from multiple vendors. Similarly, on the end-user analytics and visualization front, there is a reasonable basket of solution options offering an ability to support complex statistical analysis, and those offering the ability to provide rich visualization of the resulting analysis. But, the situation gets a bit tricky when it comes to leveraging these upstream and downstream components to build a viable architectural blue print end-to-end, in order to support a real business need that can be prioritized for immediate funding.

Low Data Volumes

For example, even a Tier 1 institutional buy-side shop would probably not have the data volumes to justify investing in a high-power data infrastructure just for supporting a big data project, whether it is for high-end active portfolio modeling or for multi-asset class risk aggregation. The same applies to large commercial lenders or private wealth management firms. Similarly, until the area of semantic analysis matures, most institutional shops are not in a rush when it comes to storage and analysis of unstructured data. So, without volume and variety being critical drivers in this context, what is left is the need for velocity, which by itself would not demand a true big data solution. This often leads to a situation where big data projects are either pushed out from a prioritization angle or else contained to a small pilot that probably solves only a technology problem, like avoidance of complex ETL, for example.

The next big challenge is the lack of industry-specific solution components, be it architectural blue-prints or extensive pre-built analytical models that support big data use cases. This forces in-house technology organizations to painfully invest in recruiting and training talent required to conceptualize, plan and execute on such projects. The very fact that this requires a certain minimum commitment of time, energy and money, all 'scarce resources with alternate uses,' often prevents these initiatives from taking off.

[Data Management Trending In Exchanges & Small Firms]

The above is not supposed to be a comprehensive list of issues that prevent adoption of true big data driven analytical solutions in institutional shops, but gives a fair idea of the challenges involved. There are probably more obvious drivers in case of retail-focused institutions just because of the volume and variety factors that come in to play in that market segment, but drivers are often not-so-obvious for organizations focused mostly or only on institutional customers.

Having said that, there are many organizations which have crossed these hurdles to implement big data initiatives delivering true business value. However, until the time a critical mass develops in this space around industry-focused solution blue-prints, analytical models and end-to-end product and services plays, adoption will probably remain slower than expected.

The views expressed in this article are my own, and do not necessarily reflect the views of Oracle.

About The Author: Promod Radhakrishnan is a Regional Sales Director for Consulting Services with Oracle's Financial Services Global Business Unit. As part of the sales leadership team for this specialized group focused on financial services clients, Promod manages and directs the development and implementation of go-to-market strategies and sales plans for banking and capital markets information technology solutions across multiple regions in the Americas. Prior to Oracle, Promod was with Cognizant's Banking & Financial Services Practice, leading sales and client relationship for a few strategic clients in South East US.

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User Rank: Apprentice
8/30/2013 | 2:46:52 PM
re: This Is Why There Is A Lack Of Adoption Of Big Data Solutions In Financial Institutions
Promod brings forward some interesting points on the obstacles to Big Data adoption being primarily small data volumes and lack of demonstrated business value. I have a different view, and argue that cultural differences around data quality, timeliness, and architecture are the main challenges in my blogpost: http://ashokanadvisors.blogspo...
We fundamentally agree, however, that the issue is not a lack of credible solutions offerings.
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