By Mark Gibson
Banks require insights from data analytics to make informed decisions and optimize performance. The right data strategy ensures this happens in an organized and cost-effective manner. Your data strategy should be as unique as your overall company strategy. It should reflect and be designed to accomplish the primary strategies of the bank. It should also recognize that data is not a magic fix-all for all your problems, but a tool to provide insight into running the business more successfully and profitably. These requirements dictate how involved the CEO and senior management should be, where the data function reports in the organization, and, perhaps most important, how analytical horsepower is distributed within the organization.
Our previous article described why a data strategy is so important, and how to set one up within your organization. This article builds upon that foundation and establishes critical success factors in setting up your data strategy and managing it over time.
How your data strategy relates to your corporate strategy
Data strategy defined. A data strategy is a written plan that documents how your organization will use, manage, and leverage its data to accomplish business objectives. Critical components of the data strategy include data sources and acquisition; data quality; data storage and infrastructure; data usage; and data security and compliance.
The linkage between the data strategy and the accomplishment of business objectives is critically important. Since business objectives are derived from the bank’s strategic plan, it becomes clear that data strategy is created in a way that helps the organization successfully execute its strategic plan.
For instance, if the bank’s strategy is to primarily target small and medium-sized businesses, it would be critical for the data strategy to emphasize business customer ‘householding,’ third party data appending (sales size, industry type, woman/minority/veteran-owned, etc.), and other important business data considerations to help make strategic decisions based on the bank’s customers and targets.
Even more interesting is the fact that the reverse relationship is true. Data strategy can actually influence corporate strategy. Imagine that executing your data strategy uncovers an important insight about your customer base. The majority of your checking relationships are secondary. Customers have come to your organization for money market accounts and mortgages rather than checking accounts. This finding would have major implications on your new customer acquisition strategy, your product strategy and even your customer segmentation strategy, not to mention your marketing strategy.
Data strategy is a means, not an end
There is another important implication from the first sentence of the data strategy definition: Data strategy exists to help accomplish the bank’s business objectives. In other words, data strategy is not the ‘goal’ in and of itself, but rather an important tool that helps the bank accomplish its goal.
This suggests that data strategy, along with investments in data acquisition, management and analytics, all need to be linked to the accomplishment of specific business goals and associated financial return.
As Jonathan Cheris, founder and CEO of PieCart Analytics and an advisor to CPG states, “An organization needs to approach data strategy and analytics with monetization in mind. The data team needs to be able to explain the specific value they are providing to the organization in business terms.”
So, data strategy is more like ‘applied science’ than ‘basic science.’ Each component of the strategy, and its related investments, should reflect the accomplishment of a business goal. As an example, when discussing the acquisition of third-party data, the strategy should be thoughtful about how that data will be used to make business decisions. Applying Cheris’s thinking about how the team would monetize that aspect of the strategy, imagine that appending third-party data to the bank’s business customers uncovers the fact that the bank is over-penetrated in manufacturing and professional services firms and that both these categories are much more profitable than the average business relationship. This could very well influence the bank’s customer segmentation strategy, including which geographies to expand into, and which bankers to hire.
Gaining support for data strategy
When developing data strategy, make sure it is embraced by senior leaders, and that it is sufficiently funded to make a real impact on the organization. The best way to do that is to treat your CEO as your primary internal client. What are his or her top priorities and how can fast access to the right data enable better decision-making? Design your strategy and tactics to make sure you are providing an interactive dashboard that they can use. You want your CEO and at least one line of business leader to be an early champion of the data strategy.
For instance, one CEO was frustrated that she could not get an accurate count of how many customers the bank had. The data strategy was designed to show the C-suite how many customers and households the bank had, what those households owned with the bank, how many were primary households and the difference in profitability among various customer segments.
Another line of business head wanted to be able to see how his branches and salespeople were performing. The data strategy was designed to provide a daily dashboard that ranked branches and individuals on new deposit and loan accounts opened the previous day. This enabled the executive to send daily and weekly emails congratulating the top performers.
When the CEO and senior leaders are provided with tangible value quickly, they are much more likely to become vocal advocates of the data strategy and be willing to make investments in infrastructure and people to bring the strategy to life.
Who drives data strategy?
Historically, marketing was in charge of housing and using customer data, often deploying a marketing customer information file. That has changed significantly over the years, with only 36 percent of bank marketers overseeing the data analytics function, according to a recent ABA survey of bank marketers.
Today, it is more common for data to be housed in finance, IT, Business intelligence or in a dedicated data department. A minority of banks house data and analytics within a line of business or revenue-producing function. This approach actually has the advantage of more easily linking data projects to strategic business objectives and revenue-producing activities.
Regardless of where the data function is located, marketing should make a serious effort to be a part of the development of any data strategy, to ensure its critical use cases are provided for, and that timely access to information and analysis is available.
The need to ‘democratize’ data
The biggest challenge facing banks is a lack of resources available to perform data analytics, according to a recent ABA survey of bank marketers regarding data and analytics. This is caused in large part due to data strategies and delivery mechanisms being ‘top-down’ versus ‘bottoms up.’ Most institutions have set up a central data or business intelligence function with analysts and each department has to submit a project request. These requests are then prioritized and it’s not uncommon to wait weeks or months for an analysis to be performed. Given that financial and risk management requests are typically prioritized over marketing inquiries, this can have serious consequences for the speed to market of marketing campaigns, and the associated revenue generation of those programs.
There is a better way. According to Cheris, “A federated business intelligence strategy puts the power of data in the hands of users throughout the company, eliminating resource bottlenecks and enabling rapid response to business questions.” This strategy gets rid of the need to submit requests to a central team to get prioritized. Rather, it uses a distributed analytics platform like Qlik Cloud Analytics to place an easy-to-use tool on the desktop of users in finance, marketing, retail banking, and commercial Banking. The tool allows each user to create their own dashboards, make their own inquiries in real-time, and generate customized reports.
Cheris adds, “In today’s rapidly evolving environment, bankers need to be able to get an answer to a question in minutes or hours, not months.” Distributing the data and analytical power enables much faster decision-making, which translates into smarter marketing campaigns being launched weeks or months faster than with a centralized data strategy.
Meeting the data needs of the organization
Data-driven insights are more critical each and every day. Optimizing your data strategy helps make sure those insights are being generated in an agile and cost-effective way. Linking your data strategy to your organization’s overall strategy will ensure it is relevant, and will also help stay on the radar of your CEO and senior management. Remembering that data strategy and analytics are not a goal, but a tool to accomplish business results will increase the probability you will receive adequate resources to implement your data strategy. And finally, emphasizing a distributed ‘bottoms up’ approach to making data and insights available to the organization will allow your organization to make much faster decisions and get to market with new programs before your competition.
Bank marketers have historically taken the lead in translating customer data into meaningful insights and results. Wherever the data and analytics function resides at your institution, it’s critical for bank marketers to continue to play a critical role in setting the strategy and leveraging the outputs to make bank marketing programs as smart and effective as possible.
Mark Gibson is the marketing practice leader at Capital Performance Group, a strategic consulting firm that assists banks in improving the return on their marketing investment. He can also be reached on LinkedIn.
