Almost 9 Out of 10 Finserv Business Leaders Lack Data Confidence, Says InterSystems Research


An example of some of the different systems in place at a typical financial services firm. Source: InterSystems.

By Roy Urrico


Finopotamus aims to highlight white papers, surveys, analyses, news items and reports that provide insight into the current financial services industry environment including credit unions.


Eighty-six percent of business leaders at financial services companies lack confidence to use their data in decision-making according to new research from InterSystems. The report also revealed 98% of respondents cited data and application silos within their organization as the main culprits.


Cambridge, Mass.-based InterSystems, which provides data solutions in the finance, healthcare, and logistics sectors, commissioned data analyst firm Vitreous World to survey 554 business leaders within financial services to understand the biggest technology and data challenges their organizations face.


“Becoming a data-led organization is clearly far more complicated than business leaders expect, and delivering the right data to support critical business needs is becoming increasingly challenging,” said the report.


Fifty-one of respondents from the finserv companies − including commercial, investment, and retail banks − across 12 countries globally, listed the number one priority for the next 12 months as accessing and receiving data in the correct format to drive decision-making. Another 47% identified understanding data management as a top priority.


Ann Kuelzow, InterSystems,

“The financial services sector handles and processes huge amounts of data every day. It is the lifeblood of organizations and is key to everything from compliance to business 360 and customer 360, which enables them to deliver the products and experiences their customers need,” Ann Kuelzow, global head of financial services, InterSystems, commented.


Kuelzow added it is vital that financial institutions find ways to overcome the challenges they are experiencing by utilizing data fabrics to help harmonize, and analyze information.



Data Challenges


The InterSystems research also emphasized much of the data challenges stem in large part from overly complex data infrastructures implemented with a disjointed set of technologies and applications, leading to data silos that make it difficult to obtain information and insights in a timely manner, and in a way that is easy to interpret and share. Some potential data silos in financial institutions include customer portfolios, customer relationship management (CRM) systems and transactional systems, as well as those that exist in front, middle and back-office systems.


“A large investment bank can have hundreds or thousands of different systems, both organically for front, middle and back office, and CRM systems and so forth,” Joe Lichtenberg, Head of Product and Industry Marketing for InterSystems.

Joe Lichtenberg, InterSystems.

Receiving access to all the data is a big deal for organizations, Lichtenberg noted, because otherwise they have to make assumptions and summarize the data. “The business wants to be able to drill into that data, and you need the granular data behind it to answer questions and get more information.”


Another big data challenge, according to survey respondents, is delayed access to data (37%). Real-time access to data allows financial services institutions to boost key initiatives, with 35% viewing improving operational efficiencies as a top initiative for which they require real-time access to distributed data, followed by making strategic decisions (31%), and digitalization/automation (28%). More than a quarter (26%) of business leaders also believe having access to real-time data would help to improve enterprise risk and liquidity management.


The inaccessibility of data from all the needed sources (33%) or in the format needed (32%) are also major issues, along with visibility, with 31% saying it is difficult to gain a view of enterprise-level risk. The primary impact of these challenges is difficulty in gaining a complete 360-degree picture of customers to deliver personalized services (36%). This can affect an organization’s ability to retain existing customers, attract new ones, and create a competitive advantage.


In addition, 35% percent of business leaders said these challenges make using data for decision making difficult. Similarly, 34% revealed they are not able to base decisions on real-time information so often must rely on assumptions that are not always correct.


Part of the solution, Lichtenberg explained, is putting all the information in front of the business and having it be accurate, complete and current. “But the other part of it is providing the business with the ability to drill into that data in an interactive way, and ask questions and get immediate answers rather than making a request to IT that goes back in the queue.”


Weaving Data Fabrics into the Process


“The vast majority of the folks that participated in the survey said that they were interested next-generation architectural patterns. “Data fabric addresses many of the limitations of prior approaches like data warehouses, data marts and data lakes,” said Lichtenberg.


Many financial services firms appear to be recognizing the value of data fabrics, per the InterSystems report. Data fabrics, described in the report as “the future of data management,” enable organizations to bridge data silos and speed and simplify access to data assets.


While still an emerging architecture, 56% of business leaders in financial services know of data fabrics, a figure that rises to 80% among U.S. survey takers. Seventy-seven percent overall, and 93% in the U.S. said they would consider implementing a data fabric to simplify access to distributed data.


Lichtenberg explained data fabric eliminates data retrieval delays. “It brings together data from dozens or hundreds of different systems puts it in a consistent format (and) can apply different types of analytics.” Some of the different disciplines include retrospective analytics, used when the outcome of an event is already known; predictive analytics, to forecast trends and events; and prescriptive analytics, which helps organizations prepare for certain scenarios.


InterSystems Brings a Complete View


According to the study, despite the challenges firms experience with accessing data, 50% report they use data to offer customers hyper-personalized products and services all the time, while 42% do so on occasion, and 9% are not able to do so at all. Only 34% are confident they have a 360-degree view of customer data.


InterSystems offers several of what they term “use cases” for financial institutions including Business 360, Customer 360, Institutional Client 360 and a Cloud Fintech Gateway.


“(Business 360) provides an accurate view of the business; by scrutinizing the different data silos and production applications housing data; (and) Customer 360 provides a review of each individual to hyper-personalize their experience,” noted Lichtenberg. “All of those have been built into this data fabric, and it gives a real-time comprehensive view with analytics.”


Lichtenberg explained the importance of “getting access to all of this data, not just for decision making, but to feed new typically cloud-based, applications that are both customer facing and internally facing to promote agility and provide outstanding customer experiences based on intelligent analytics-based applications.” And they require access to comprehensive current and accurate data.


Lichtenberg added that a number of credit unions strive to provide hyper-personalized experiences using InterSystems’ data.


For example, Baker, Mont.-based Fallon Federal Credit Union (FCFCU) (now part of the $111 million Circle, Mont.-based Grasslands Federal Credit Union) used analytics to help its base. “When the pandemic hit, face to face interaction went away almost completely. (FCFCU) was already using our software.” The credit union, Lichtenberg explained, pulled together data from many different systems and ran predictive analytics built into the smart data fabric.


“The credit union (FCFCU) ran predictive machine learning models they built from all of this data to identify which of their members were at most need of assistance, then proactively reached out to those members to see how they could help,” Lichtenberg said. Some members changed the terms of their loan or took advantage of different services. “By doing well for their members, they actually did extremely well for their business and saw their best financial performance in history.”


Lichtenberg added many financial services business leaders are striving to take a more strategic approach to their operations, made possible by having a holistic view of the entire business at their fingertips. Doing so, he said, will give firms the agility needed to not just survive, but thrive, and gain a true competitive advantage in a volatile world.

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