Data analytics isn’t about solving a single problem; it’s about solving hundreds of small ones that can help a bank continuously grow revenue and improve operations. Many banks struggle to transform their data into meaningful, valuable analytics, because they simply collect data and do the math. Data analytics should involve three major components: define the business opportunity/problem; determine how the solutions work in the context of their environment; and understand how a solution delivers value. This process will enable bankers to know the “what” behind the “how” and “why” to make more informed decisions.
Banks leveraging data analytics can improve their operations in the following four ways:
- Manage risk and regulations.
Data analytics can minimize risks by ensuring the completeness, accuracy, and availability of a data source aka the Single Source of Truth. To find this truth, financial institutions will need to be able to abstract data from both core and disparate systems and then normalize them into standard data elements. Then data must be validated for consistency, completeness, and integration before any intelligence can be added. Creating a clean data environment can expose any data quality and integration issues that may pose a potential risk and/or regulatory issue.
- Identify future markets and best investments.
With a clean data environment, banks can develop a strategy to understand, acquire, and deepen relationships for higher-value customers. Once banks can define consumer relationships and households, then they can group them based on product and transaction behavior. Segmenting customers based on behaviors can help the bank understand how they derive value from the relationship, while also revealing which relationships are the most profitable. This will enable banks to sell more products that are truly valuable to their customers, and it will help them price relationships more accurately.
- Create a more efficient and smarter organization.
As we head into a recession, banks will need to find ways to stay profitable to survive the revenue downturn. Preparing data in advance will enable banks to see where every dollar is made or lost. With this knowledge, the organization can create a strategy to deepen new and existing relationships and optimize the flow of capital. Data analytics can also help banks identify areas for operational improvements or enhancements. These analytics can provide more insight into situations, including the amount of time, money, and resources spent.
- Make data-driven decisions.
Data analytics allow banks to make more evidence-based decisions, as well as discover new opportunities to grow. Facts and figures can reveal the most used products and services at the bank, which can help when creating strategies for future technology investments. Banks can also use transaction data to see if a customer or member holds products and/or services with a non-bank provider and suggest bank-based alternatives. In either example, bankers can have a more meaningful conversations with customers about their needs and how their financial institution can support them.
Again, data analytics isn’t about solving a single problem; it’s solving hundreds of small ones that can help a bank continuously grow revenue and improve operations. It’s important to start data exploration with the business outcome in mind, which means banks should democratize access to data, focus on data governance, and use analytics to create new processes, products, services, and models. With a vision and clear strategy, as well as the support of the organization at all levels, data analytics can become central to operational improvement and revenue growth at banks of any size.
Mac Thompson is Founder and President at White Clay, which provides bankers with a single, accurate view of their data to optimize profitability and liquidity, protect shareholder value, and improve customer relationships. Please visit http://www.whiteclay.com/ for more information.