Data is king. Banks that know how to harness it, manage it and monetize it can get better business insights, create more growth opportunities and stay ahead of regulatory demands. Huge benefits can be reaped by developing a clear data strategy that defines how to access, ingest and connect essential data.
Data is the lifeblood of financial services organizations of all types and sizes. All banks, insurance companies, asset managers, wealth managers and capital markets firms deal with massive amounts of data. Whether it’s the trading desk or underwriting team, whether for opening accounts, advancing loans, recruiting staff or providing regulatory reporting, in the banking industry, data drives the business.
Data can be used in many powerful ways, and the digitization of financial business processes has opened new opportunities to turn data into information, glean insights and generate new value from it. However, the ability to gain insights from data is often hindered by the presence of nonstandard and inefficient processes to go along with aging infrastructure and applications, sometimes referred to as technical debt.
All organizations, especially tier-one banks, face the challenge of how best to optimize their technology landscape to make it more cost0efficient, business-effective and secure when leveraging the flow of available data. They are challenged to find efficient and effective ways to use data to introduce more relevant products to customers, as well as to manage security threats and report relevant information to their constituencies.
A critical first step is embedding a sustainable data strategy — and executing it — across financial services business practices and reporting requirements. To do this successfully, banks need access to data from myriad internal and external data sources, both structured and unstructured. Increasingly, they need to do this in real time — and store and analyze the data in a way that enables timely and informed decisions. The goal is to arrive at a data strategy and data platform that facilitate growth and meet the needs of internal and external customers.
Tapping into the data goldmine
Laying out a data strategy goes a long way toward ensuring that the data that flows through an organization is accurate, reliable, consistent and relevant. Developing a data strategy begins with knowing what data you have and what to do with it. DXC Leading Edge identifies three primary states that data supports in an organization:
• Discover. This state involves seeking information and engaging in new methods to help a company evolve and advance. For banks, this means looking to better understand your customers and identify new products and services that meet their needs.
• Develop. When making strategic decisions, banks should use data to know the best ways to balance risk with reward. Data-driven action needs to be taken to support growth and drive higher revenues.
• Defend. This state concerns protecting resources and maintaining the health and viability of the organization. In banking, this includes meeting compliance, legal and regulatory requirements.
Using data effectively to inform decisions in these three areas goes well beyond data technology or the ability to collect significant amounts of data. The ability to use data for competitive advantage depends on a healthy data metabolism. That means getting the right flow of data and insights at the right time and speed to optimize decision making.
Six steps for crafting a data strategy
In common business settings, putting together a data strategy involves developing a common reference of methods, services and architectures for acquiring, storing, analyzing and operationalizing data. In banking and financial services, special emphasis must be placed on taking a customer-centric approach to data and defining how data will be used to serve the consumers of the data. When developing a data strategy, organizations must meet several considerations. Here are six key steps:
1 Simplify your data.
The road to getting the most out of your data begins with simplifying it. Start by minimizing your data footprint and reducing the cost of data that drives your business. Simplified data is easier to maintain, secure and trust, and it lowers your market costs.
Getting your data into a state where you can understand it, map it and archive it in a centralized manner lowers the burden when regulatory initiatives are imposed, especially when considering things such as workflow. Also, simplifying your data will let you more easily capture an accurate assessment of the carbon footprint of your data so you can apply new technologies to enable a move from high costs and high energy usage to lower costs and lower energy usage.
2 Align data with business objectives.
Too often, initiatives related to data are driven largely by IT, and the business side is not involved in the process. Banks need to make sure that the business is fully on board with owning and using data and that there is a strong focus on the target operating models around the data to drive business value.
One way to achieve a successful alignment is to make sure an IT initiative has business-related metrics built into the sprints and the KPIs for the project. For example, know how much cost and resource savings will be achieved by automating a specific task, such as processing credit card applications, and be able to document that in a way that can be clearly communicated.
3 Break up large data projects into small stages.
One of the biggest challenges IT faces in talking to the business is making sure they know what you’re talking about. When terms like AI and machine learning are thrown around, the business side has no way of judging whether you’re talking sense or spinning nonsense.
When undertaking data-related initiatives, one approach is to break up projects into small stages that are easy for the business side to understand. Then ask the business to spend relatively small amounts of money per stage, so it becomes a much easier decision for them to write the check than it would be if you asked for a big chunk of money up front.
4 Leverage advanced analytics to monetize your data.
Part of taking a customer-centric approach is putting consumer needs at the center of your data strategy and using analytics to meet those needs. Still, an important tenet to keep in mind is that unless you take data and translate it into useful insights, the data has no inherent value. Even if you translate data into insights, those insights still have no inherent value until you turn them into action.
Then, if you can take those actions at a level that can be scaled across the bank, you start generating business outcomes. If it’s not scaling, it doesn’t necessarily translate into business outcomes. Analytics and digital engineering come together because digital engineering is about embedding those data insights into business processes as much as possible. For example, leveraging AI to drive automated credit decision-making processes for low-value/low-risk transactions will reduce the number of staff required to perform these tasks, meaning lower costs. It also frees those employees up to focus on higher-value transactions, which drives faster decision making and time to fulfillment, thus delivering better value to the customer and a faster increase in assets for the bank.
5 Build a unified solution.
As banks try to ingest and make use of tons of data, it is crucial to have a unified, integrated technology solution that provides a smooth end-to-end workflow. An IT landscape mired in technical debt will slow data down. If there are lots of choke points as the data flows, adverse outcomes could occur, such as an unhappy customer stranded on the phone because the right data couldn’t be accessed quickly.
Banks need a simplified, centralized view of their data and their customers to make sure products, services and insights are being offered to the right constituencies in the right way, and in an almost instantaneous fashion. Making data run smoother throughout the life cycle of data processing is part of what the industry refers to as tactical optimization. An integrated technology solution needs to be in place that can execute already-defined processes faster, more efficiently and at scale.
6 Embrace a data-driven culture.
It is critical to foster a corporate culture where decisions are data-driven and evidence-based — that is, make decisions on the basis of data and research, and not on gut feelings or anecdotal evidence. Companies need to change the decision-making culture to cope with the huge amount of data flowing through the organization. This is where having a well-balanced data metabolism comes into play, where you use just the right amount of data to make an important decision.
Getting buy-in on a data strategy from all levels is key to maintaining a data-driven culture. For one, key decisions around data need to be sponsored and driven by executives at the top level. To prove the value of data-driven initiatives, create success stories to showcase how previous projects have boosted the bottom line. Then communicate those successes in simple business terms while avoiding technical jargon. Success is an attractive force, and you will gain new converts if you can demonstrate that business success has been driven by data.
How DXC can help
From happier customers to higher profits, numerous benefits come from having a unified data strategy. Expected business outcomes include a better customer experience, an enhanced employee experience and improved operational efficiency. Operating more efficiently results in lower costs, and in banking, becoming a more data-driven organization will help you manage risks better while achieving regulatory compliance.
Forging a successful data strategy includes getting the right technology and the right partners in place. DXC Technology has decades of experience in implementing advanced systems that manage data for many industries, including banking and capital markets and financial services. DXC’s expertise includes knowing how to optimize costs around running large data sets and maximizing the benefits of cloud technology. Another area where DXC excels is knowing how to optimize risk engines so data can be processed faster.
DXC helps organizations build modern core banking platforms that go well beyond simply talking about deriving insights from data or delivering outcomes. We help our customers build the right foundations and simplify and structure their data so they can shift their focus to investments that drive higher returns. Our ability to establish a single source of composable data analytics and data cleansing enables customers to save on transformation and invest in innovation.
A team of DXC professionals can take relevant data and insights and embed them into business processes so the right person at the right time has access to the right data and insights to make important decisions in a data-driven way. DXC does this at scale so data-driven insights translate into business outcomes.
A data strategy should encompass all aspects of an organization’s operations. DXC covers each step of the end-to-end value proposition and the technology required for each step across the entire life cycle, from data to business outcomes, while using our industry expertise to successfully converge business and technology.
Conclusion
The future of banking and financial services relies on applying data capture, data ingestion and data analysis in a centralized system or platform that drives data monetization and data-based decision making. Organizations can achieve this by developing data strategies that establish frameworks for improved data management and embedded analytics, and leverage data-driven insights that allow for an increased focus on customers and alignment with business strategy.
About the authors
Jeremy Donaldson, managing director of Banking & Capital Markets (EMEA) at DXC Technology. He has more than 25 years of experience in applying technology solutions to solving complex business challenges in numerous management and consulting roles. Connect with Jeremy on LinkedIn.
Andrew Haigh, head of Banking & Capital Markets (EMEA) at DXC Technology. He is focused on helping customers derive value from data and delivering innovative solutions across the banking and capital markets spectrum.
Paul Hewitt is director and head of the Data, Analytics and Machine Learning practice at Luxoft, a DXC Technology company. Paul has spent most of his career focused on helping organizations derive value from analytics in the BCM and financial services industry.
Sunil Menon is managing director and Head of Manufacturing and Life Sciences (EMEA) at DXC Technology, and has expertise in areas such as helping organizations implement best practices for data-driven manufacturing.