Data systems are fractured; some processes are broken, and governance is overlooked. There’s some data, here, in one repository and often there’s much needed data somewhere else, in other repositories, within the organization or with partners. With so much data, it’s getting harder to keep track of all the sources, their lineage, updates and/or refreshes. Data owners may be obligated to lines of businesses rather than a centralized data team, creating obstacles in enterprise analysis. It forces financial institutions into a mixed strategy for managing data and makes finding valuable insights almost impossible. But these issues aren’t insurmountable. Careful planning and execution can lead to meaningful improvements in your data strategy that uncover customer insights and trends.

Data is getting bigger

Aite notes in their report, “Current State Assessment: Global Analytics Ecosystem,” that the number of data sources and types grows exponentially. Not only are the sources and types of data growing, but as activities get more plentiful, history, too, is getting bigger and longer. There’s a shifting focus to mobile and transactional data for identifying life events. Understanding these and other data points will help financial institutions (FIs) spot changing behaviors and act upon upcoming needs. These capabilities will help FIs differentiate themselves from their competitors. Differentiating yourself today gives you an advantage. Using holistic data about your customers makes that easier while also becoming table stakes across the industry.

Where to begin

When it comes to understanding and approaching your customers you can start by keeping in mind that your competition isn’t just the bank across the street. Your competition includes non-FIs that are setting the standard for data usage and customer interaction. Companies, such as Amazon and Netflix, have raised the bar with all-inclusive analytics to leverage data to understand their customers’ needs, match them to the company’s solutions, and present them in highly tailored experiences. What’s more, customers increasingly expect these experiences. To keep up, you need a holistic data strategy with an ecosystem at its core.

Bringing data points together with an integrated approach

Before you can leapfrog your competitors, you need to know all your data sources, their elements and quality. Then you need to align your data strategies to corporate goals. Are you aiming for increased sales or wanting to solve customer issues before they become problems? The answers are in the data.

Pooling data together requires an ecosystem where infrastructure, data sets, tools and capabilities are brought together in a comprehensive environment. Data from core banking, payments, mortgage, lending, fraud, loyalty, marketing and many other systems can be aggregated via request/response APIs and streamed webhooks.

To get more out of your data, you’ll want to do more than just get data in. Cleansing the data makes it more actionable by contextualizing merchant and transactional payment information that’ll help you better understand the underlying person. Then, use the data to create insights in visualizations, drill-down reports and predictive models. These are all necessary components of an ecosystem and we should know. FIS is building the most complete data ecosystem in the fintech space, called Ethos^TM, that scales and adapts to our clients’ differing needs.

High quality data builds better models

It takes an ecosystem to unlock your data’s potential. You need breadth and depth of data as well as a quality lineage for the best results. Without the right data attributes or too little history, you may not get a complete or accurate customer picture.

Today, you want to know more about your customers than business intelligence or descriptive analytics can provide. You need data science models. In a time of pandemic uncertainties and CECL requirements, building models around loan default probabilities can help you better manage your portfolios. Models that reveal customer behaviors can identify likeliness to attrite at the product or relationship levels. Models can even recommend better product fits or next best products for your customers.

To build models, you must define your business objectives, match data to them, train the model with machine learning techniques, test the results in a data science notebook, compare the results to the original objectives and then adjust as needed. Afterwards, you’re ready to release results and go to work.

If you haven’t begun your data journey, now is a critical time to start as we enter a period of great change and uncertainty.