Unlocking the Potential of Data in Financial Services — A Roundtable Discussion with Senior Financial Executives
by Rackspace Technology Staff
In the age of rapid digitalization, data-driven organizations are outpacing their peers. Enterprises can’t afford to be left behind with outdated tools and strategies. But what about a sector as heavily regulated as financial services? How can financial service institutions start their data journey while remaining compliant with data regulations?
To seek answers, senior financial service executives gathered for a roundtable discussion, “Unlocking the Potential of Data in Financial Services.” It was organized by Jicara Media and hosted by Rackspace Technology® and Amazon Web Services (AWS).
According to Shwetank Sheel, Director of Data Services Sales APJ at Rackspace Technology, banks are experiencing a dramatic shift in customer expectations. He said they want social media-like experiences. But this requires banks to use increasing amounts of data to drive business transformations, while also meeting regulatory and internal stakeholder demands.
Sheel said, “The need for customers to have the same experience when they’re interacting with their financial partners that they have when they’re using Facebook has driven a sea change that is driving the need for data transformation.”
Financial executives’ real-world data challenges
As organizations scale up their business, the amount of data at their disposal also multiplies. The topic: What should they do with massive volumes of data — especially when balancing data access and privacy regulations is such a crucial concern for a global investment bank.
An IT executive from the investment bank stated, “When it comes to getting access to data, structuring it is a big headache. The challenges are access and data privacy. Naturally, we’re always going to have our hands tied. Based on various rules and regulations and what I’m allowed to do, it’s always a challenge.”
In the case of a large insurance company in Southeast Asia, it was a matter of data centralization and decentralization. The insurance company’s CIO said they digitize all documents. As far as the source data is concerned, they have a centralized repository with a data warehouse and analytics tools.
“We’re always oscillating between centralization and decentralization,” said the CIO. “Initially, everything was decentralized. Now, gradually we started centralizing the data. When the volume exploded, there were various other challenges. We are again now looking at decentralizing.”
Regarding the Singapore operations, the CIO said they are not using “that much data” as of now. Thus, they believe centralization is the right approach currently. Meanwhile, according to one of its senior IT decision-makers, complexity is among the leading data challenges faced by a large international bank based in Singapore.
Dealing with silos and silos full of data
“One of our major concerns is the legacy data that is being contributed by various markets and clients in multiple forms and shapes. Data standardization is vital and data modelling is important, but how do we integrate the data in the multicloud infrastructure? And how do we present the democratization of the data to the client?”
For a Singapore-based securities firm, age-old industry practices now clash with privacy laws, particularly regarding data silos. According to the firm’s lead enterprise architecture, “We were not so keen on using data from the past.
“There were silos and silos full of data. With the tighter data regulations, we’re now facing consent challenges, for example, going back to customers’ Google® accounts, as far back as when the regulatory framework was not yet agreed upon. Now, having to backtrack on a lot of that consent, getting that documented, and ensuring that the content and consent are achieved, permeated throughout our legacy systems, which are also still in silos.”
Democratizing data access and adoption between silos
An equally pressing issue the financial executives are facing is how to democratize data access and adoption across departments. Moody’s Analytics, which has worked with Rackspace Technology on this issue, shared how it went about its data journey.
Louis Chapman, Senior Director, Predictive Analytics, Product Strategy, and Operations at Moody’s Analytics explained. “When Rackspace Technology jumped in with us, the first challenge they helped us conquer was, how do we make our data presentable to somebody who’s going to use it? That was a big one for us. If you can make your presentation layer intuitive, you solve a lot of problems.”
Further, Chapman stated, “People might think this is a documentation problem. That you need to have meetings to go over this. That you need to be able to create videos and spread them across your organization. The bottom line is the design of the presentation layer needs to be intuitive, and you need to be able to push the right data forward. So, what we are doing is taking jumbles of data, collecting it for a long period of time, and then synthesizing it into something that’s straightforward.”
According to Sheel, the maturity of an organization plays an integral part in data collaboration. “When a data organization is well funded and well understood, we will typically engage with them as an additional pair of hands. That’s primarily how we work with Moody’s. Overall, they know what they want to achieve.”
Sheel said that they usually engage with well-funded and well-understood data organizations as an additional pair of hands, citing Moody’s as an example. But when organizations have yet to see the value of data, they usually gauge their seriousness, often recommending that they start small to determine possible use cases. For example, he suggested that an insurance company could use recorded analytics to improve its IVR as a potential use case with a big return on investment.
Recognizing the value of data and making it more central
Meanwhile, the Spotify model has also proven effective for some Rackspace Technology clients, said Hemanta Banerjee, Rackspace Technology Vice President for Public Cloud Data Services. “But how do you get people outside of the normal reporting hierarchy to collaborate?”
One client’s approach was to form guilds and chapters around topics such as power BI, cataloging supply chains and food procurement, and measuring analytics adoption. It presented data using dashboards during meetings. This enforced top-down behaviors and set up clear structures for people to start collaborating.
Ultimately, the key to balancing data is organizational buy-in, with which most financial executives struggle. “In finance, there are a number of organizations that are still looking at organizational buy-in and are starting small, figuring out how to get the data into a usable format, then to the synthesis question and creating governance and visibility.”
Sheel added, “This goes all the way until you get buy-in. Then it's about delivering results that will keep your CEO looking good. At every stage of that journey, the question is how to keep the show moving forward.”
Sheel’s hope is for companies to recognize the proven value of data and make it more central within their organizations, resulting in budgets being allocated for larger, longer-term data projects.
This post was created with Amazon Web Services (AWS) sponsorship.
Are You Realizing the Cloud Optimization Benefits of Kubernetes and Containers?
September 22nd, 2023
Google Cloud Next ’23 Highlights— AI and Beyond
September 14th, 2023
Why You Need an MLOps Framework for Standardizing AI and Machine Learning Operations
September 12th, 2023
Mastering Multicloud: The Power of Cloud-Neutral Data Storage
August 31st, 2023