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Big data in financial services begets chief data officer

With big data in financial services requiring various skills, a chief data officer may be ready to step up. A Capgemini expert discusses this evolving role.

A broad dialog lately focuses on the potential role of a chief data officer (CDO) in managing and stewarding data. Needs differ from industry to industry, according to Zhiwei Jiang, who is global head of the insights and data practice for Capgemini's financial services consulting group. In an interview with SearchDataManagement, Jiang, who came to Capgemini after 20 years as a technologist in banking, discussed data issues that he viewed through the prism of a recent study the company produced together with Efma, a banking association. That study, "Stewarding Data: Why Financial Services Firms Need a Chief Data Officer," boldly states that the CDO is a must in the sphere of finance.

Capgemini has recognized four opportunities for big data in the financial services: risk management, operational efficiency, customer experience and identifying new business models. It makes you wonder, could one person be good at all those things?

Zhiwei Jiang: Ha! The short answer is no. There is no magic wand, no way one person could solve all those problems. But I have seen successful CDOs. They know how to establish the foundation. When I say foundation I mean data strategy, data stewardship and data governance.

Zhiwei JiangZhiwei Jiang, Capgemini

The successful CDOs branch out. They have a data model team, a data governance team -- they have an overall roadmap for big data adoption. Some actually run a data science team that covers data analytics. So, usually when given a budget and proper support, they establish four or five different streams that tap into the overall landscape of the data -- from data governance and data standards all the way to the data lake and the data science. That is how most of the successful CDOs increment it.

The Capgemini/Efma study holds that organizations with a CDO reported a 43% success rate for big data initiatives versus 31% in firms that have not appointed a CDO. But who should the CDO report to? The CEO? The CIO?

Jiang: I can give you a few views on it. In large organizations, especially large banks, the CDO reports into the CIO. Sometimes even into the EA, the enterprise architect. The second option is to have the CDO at the same level as the CIO, oftentimes reporting to the COO. In some rare cases they may report to the CFO.

The majority of financial services firm we spoke to have CDOs in place.
Zhiwei JiangCapgemini

My own view is that the goal of the CDO is to try to drive business changes -- to deliver business values, whether it's revenue increase or cost savings, or, within an industry like financial services, to meet regulatory requirements. My recommendation is to have the CDO at the same level as the CIO, reporting to same boss. Often that is the COO. The CDO should have the same level of accountability and responsibility as the CIO.

The title has some traction. The majority of financial services firms we spoke to have CDOs in place. And people, more and more, agree on how a CDO function should be organized.

The compliance requirements of finance, and the pressing need there for data governance, seem at odds with a lot of the retail industries, in which Web developers have been able to innovate without some of those restraints.

Jiang:  I would say the financial service's big data adoption is actually slower than in others, certainly slower than in government or retail industries. That is true even though financial services companies sit on huge amounts of data. There are a lot of things they can learn from what retail has done about getting a 360 degree view of the customer. In fact, you will see a trend across the industry where companies that you can describe as retail banks will take a different approach to big data than investment banks. Retail banks are making progress in this space.

Is it fair to say coordination between analytics teams and data management teams seems to be lacking when it comes to new technologies like Hadoop?

Jiang:  Yes, that is spot on. If you look at the innovation side of things, it is very confusing. If you look at all the Hadoop distributions, all the ETL tools, all the SQL tools and all the data visualization tools, it is very confusing. In the traditional relational space, it was simpler. It's confusing and some people are scared away with all these choices.

What I see on the ground is that the financial industries, for several different reasons, is behind in the adoption of the new big data revolution. The first reason for that is most of the companies have yet to find a killer app -- one that would move the dial.

Other reasons are that there are lots of regulatory changes and lots of pressure on cost savings. The focus is not on the innovation. It's how do you improve the margins, or how do you cut the costs, or how do you meet regulatory demands.

Then, too, there is culture. People today in the CIO seats come from traditional application development and management backgrounds. CEOs come from ops. When you think about it, the data skills you need today are kind of different. Data thought leadership is sometimes lacking.

Still, retail banks and credit card companies have made innovations around key business values. And in terms of operational efficiency and compliance, advances have been made on preventing fraud and money laundering.

Jack Vaughan is SearchDataManagement's news and site editor. Email him at [email protected], and follow us on Twitter: @sDataManagement.

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