Making Change: 3 Ways Financial Services Companies Can Turn Data into a Competitive Advantage
Sr. Content Marketing Manager, Sigma
Data holds the promise of better efficiency, greater agility, higher returns, and happier customers. But if you’re in the financial services space, you probably feel like you work for your data more than it works for you.
Many financial services (FinServ) companies deal with analytics workflows that are endless cycles of high-level dashboards, BI request queues, complicated SQL, extensive data modeling, manual workarounds, and siloed data extracts to Excel. But it doesn’t have to be this way.
In this article, we see how leading financial services companies like Cowen, Clover, and Migo are transforming data into a competitive advantage by establishing a single source of truth, adopting a common analytics language, and going beyond known KPIs to find answers.
Establish a Single Source of Truth
Financial services companies have millions or even billions of dollars of profit or loss riding on the ability to quickly analyze massive data sets. This data comes from a variety of different sources, both internal and external, much of it in unstructured and semi-structured formats. According to IDG, the average company collects data from more than 400 data sources.
Often, data is siloed in disparate locations, including in traditional data repositories and applications like Oracle and DB2, on-prem enterprise apps, databases like MySQL and MongoDB, SaaS software, public data repositories, and more. Before data can be put to use efficiently, it must be aggregated in one location so it can be analyzed holistically. To accomplish this in a cost-effective, flexible, and scalable way, you need the cloud.
In most organizations, BI teams use coding languages such as SQL or LookML to join disparate data sources in cloud data platforms like the Snowflake Data Cloud or Amazon RedShift. But more often than not, this process creates operational data bottlenecks that lead to frustrations that result in risky Excel extracts that can cost organizations millions of dollars in fines. Financial services firms need a next-level version of Excel that allows analysts to crunch billions of rows of data seamlessly — and is backed by the security of the cloud.
Establish Self-service Analytics That Works For Everyone
Bankers, analysts, traders, partners, and actuaries need the ability to find answers to business challenges and move to take advantage of opportunities quickly. Spreadsheets have served as the tool of choice for non-technical users who don’t have expertise in SQL — users typically download data from various sources and dump it into Excel.
And even people with technical skills love spreadsheets: 88% of people who write SQL still use Excel when exploring data because it’s faster than manually writing code. The fact is that spreadsheets are flexible and easy to use. And nearly everyone is familiar with the spreadsheet interface. For this reason, the spreadsheet is the ideal “language” for self-service data analytics.
Additionally, when all team members can work together using this common language, they can better collaborate. Building upon one another’s work, people can each contribute their expertise and provide unique perspectives, allowing teams to arrive at solutions they may not have otherwise.
See how Cowen created a communal environment for self-service analytics and eliminated ad-hoc requests.read now
Enable Iterative Analysis Beyond the Dashboard
Data agility is crucial to gaining competitive advantages because time is money in financial services. But being agile just isn’t possible with the traditional analytics workflow that relies on BI teams to deliver dashboards and then respond to follow-up questions.
No team will arrive at business-transforming insights using high-level, static reports. Financial services teams must find answers to “why” and “what-if” questions, which are often buried several layers beneath the dashboards provided by BI teams. Teams need the ability to answer follow-up questions in meetings to make quick decisions instead of having to “take it offline” and wait for hours, days, or weeks efore they can make decisions.
With the latest technologies and innovations, financial services teams can go beyond the dashboard and independently dive into the underlying data to do their own iterative analysis. Even non-technical users should be empowered to expand and collapse aggregates, bring in additional calculations, slice and dice quickly, and create well-constructed pivot tables, even on massive data sets with up to hundreds of billions of rows of data.
See how Migo achieved a swift and successful pivot to a recovery-focused marketing strategy in 30 days during the pandemic with Sigma.
Putting it all together: How Clover Transformed Its Financial Analytics Workflow
Let’s look at an example of how Clover, a First Data business, is using the three strategies above to turn data into a competitive advantage. Clover builds a global open-architecture point-of-sale solution aimed at small and medium-sized businesses. Its products are changing the consumer/merchant experience for the better, opening avenues for seamless customer-merchant interactions.
Like many financial services companies, Clover relies on data to inform business strategy, improve its product, and deliver a great customer experience. As a self-described “Snowflake shop, ” Clover’s centralized repository of data houses information from dozens of sources that its business teams rely on each day.
In 2018, it became clear that ad hoc analysis and reporting took up more of the Data Reporting and Analytics (DRA) team’s workload than it could handle and competed with other priorities. It took 2-3 hours to complete a request, which wasn’t sustainable. The DRA team couldn’t work on other projects, and business teams were waiting longer for necessary insights. The analytics bottleneck was delaying data-driven decisions.
The Clover team connected Sigma directly to its centralized Snowflake database to help business teams quickly explore and analyze billions of rows of data in a spreadsheet-like interface without using SQL. The data engineers were also able to perform advanced tasks in Sigma. Today, with Sigma, Clover is able to get the right data into business teams’ hands and reduce the need for frequent ad hoc requests.
Alex Mora, Data Engineer at Clover, explains, “With Sigma, business teams could immediately access and analyze all our data centralized in Snowflake. Entire workflows were simplified overnight. We no longer had to query 215 databases. Sigma was right there for them, and they latched onto it immediately.” As a result of using Sigma, Clover achieved a 90% decrease in time to data insights and dramatically reduced its ad hoc reporting queue.
Sigma helps Financial Services Teams Gain a Competitive Advantage
For financial services companies competing in today’s environment, time is money. When your business experts have the ability to access quality data in a secure way and explore that data in-depth to answer crucial questions, you gain a significant advantage.
Learn how you can streamline your financial data analytics workflow with democratized cloud analytics platform teams know how to use.