The financial services sector is accelerating its adoption of digital technology. Innovative new technologies are redefining the sector, shaping the services that financial organizations offer, the ways in which they interact with consumers, and the ways in which they apply new sources of data across departments.
Nevertheless, the evolution of financial services is set to continue. Let’s examine the 5 top trends in financial services that will change how you think about your data:
Trend #1: Making the business available “as-a-service” for Financial Services
Turning back-office capabilities into marketable services is one of the newest and fastest growing areas for traditional financial services organisations.
While potentially competitive FinTech organsations continue to proliferate and disrupt, they have found that their agile customer engagement business models still need to rely on the transactional fundamentals perfected by traditional financial services providers.
So why not let them, and others, have access to those services Open Banking standards, financial services “in-a-box,” APIs, and other such initiatives, are embracing new ways to enable the value chain.
For most traditional service providers, it is not so easy to achieve this level of agility, especially when lines of business, and their silos of data, are not integrated. The data mesh concept is encouraging new ways to cut the time and cost of data integration by enabling companies to use their data in-situ, facilitating the delivery of the data needed to support more open APIs.
Trend #2: Reshaping the organisation as a technology service provider
Is your organization a bank? Or is it a technology company that provides banking services? While lines blur, traditional financial service providers need to implement new types of products and services to enhance the experience of their customers and those of their business partners.
Identifying new opportunities to drive value for customers starts with the data. But with so much data available it’s impossible to create the agility to integrate and digest it all, even when it’s in a data lake. So what’s the answer? The answer is, don’t integrate it in the first place, at least physically. New techniques of data virtualization enable the faster creation of data sets without having to move the data from where it is located, saving time and money. More importantly, it provides agility in the selection of new data sets, for more insightful analytics.
Trend #3: Promoting “data-driven” as part of the institutional culture
Reliable, effective, decision-making processes require an underlying education in data management, often referred to as a data-driven culture. Initiatives for becoming data-driven culture help analysts to become more confident in finding, accessing, using, and interpreting larger volumes of data. Data democracy is the support of wider access to data and help with data, throughout the organization.
Data-driven cultures, however, are only as good as the individual organizations are able to timely provision reliable and pertinent data. It is not always possible to get convergence from owners of different data sources on what the data means and how it should be collected and used. This disagreement creates bottlenecks for many organizations trying to integrate data into traditional data warehouses for analytics. A data mesh approach, overcomes this by enabling businesses to collect data when they need it, on-demand, from the existing sources. By removing the need to relocate data, the number of silos is reduced, and the integrity of data governance is conserved.
Trend #4: Creating the customer’s digital twin
Insight into customers — their evolving expectations, their environment, their relationships, their preferences — brings the ability to automate service, gauge the appetite for new products and services, analyze risk and predict churn.
Data for the digital twin, the virtual model of real-world phenomena, in this case your customer, is drawn from actual customer interactions and from derived data. Generally, the more data gathered, the “closer” to the actual customer is your digital twin. Collecting large quantities of data from disparate sources makes for slow progress using traditional extract, transform, and load (ETL) approaches. Data virtualization is helping to increase the speed-to-market of digital twin technology by overcoming the bottlenecks associated with traditional data integration methods.
Trend #5: Monetizing Data for Financial Services
Good data has good commercial value, and the discipline of infonomics provides support to the mechanism of linking information to monetary value. Financial services companies have the opportunity to discover new revenue streams through the analysis of their data assets. Many banks already share data with business partners, albeit predominantly anonymized, aggregated, and synthesized into the form of trends. However, the ability to append new data sources, to easily find, for example, geographic, weather, pandemic, relational, device, and other types of topical information, makes the data more valuable from an economic perspective.
Digging deeper into more data sources requires both breadth and depth, which is not easy when there are potentially hundreds of data sources to choose from. This is where traditional approaches to data collection, such as ETL approaches, become ineffective. Data virtualization provides an alternative, more cost-effective solution.
If you are interested on the top trends in data management (all industries), please visit Data Management Trends in 2023: My Top 5 Trends to Watch.
