A Day Early and a Dollar Extra: How Data Helps Agile FinServ Companies Defy Market Volatility
Business agility is worth its weight in gold — and Financial Services (FinServ) companies with the foresight to anticipate change and rapidly adapt will quickly outpace unsuspecting competitors.
This guide explores the modern analytics tools, teams, and techniques leading FinServ organizations need to accelerate decision-making and time-to-action related to financial trading, investment analysis, and regulatory compliance. Readers will learn:
- How rapidly evolving market conditions are driving change in the financial services industry
- What three obstacles are holding FinServ organizations back from enabling domain experts to make fast, data-driven decisions
- How agile processes and collaboration can up-level financial analytics and help organizations stay ahead of the competition
Volatility, Strain, and Disruption: 3 Factors Driving Change In the Financial Services Industry
Global financial market volatility has risen dramatically in the past couple of years, driven by factors including the COVID-19 pandemic, new and increasingly complex regulations for financial institutions, unstable governments, rising economic nationalism, and large-scale hacking by organized criminals and state-sponsored groups.
The long-term economic impacts of COVID-19 are only beginning to take effect. For example, the Federal Reserve’s Beige Book survey notes that disruptions in production and supply chain logistics are responsible for significant price surges in commodities like agricultural products, building materials, cleaning products, and microchips. And, when combined with increased aggregate demand as a result of stimulus checks and PPP loans, one thing is clear: The threat of 1970s-like inflation is real, and FinServ institutions must prepare for further uncertainty.
At the same time, the breakout momentum of new financial instruments such as crypto assets like Bitcoin and Ethereum, new investment vehicles such as SPACs as an alternative to IPOs, and evolving retail investor behaviors are upending many of the traditional ways people build wealth. In today’s financial landscape, “whales” aren’t the only market makers — new investors embracing Robinhood and similar apps, as well as the influence of informal internet communities and crowdsourced stock hype, can influence asset prices in previously unexpected ways.
Under these conditions, the only constant is change. And, while there is no way to prepare, data provides an avenue for FinServ organizations to minimize risk at both macro and micro scale.
Analytics Agility Derisks Financial Services
The ability to analyze data quickly — and turn insights into actions to drive a business forward — is a powerful competitive advantage. For example, in 2017, 70% of top-performing organizations used advanced analytics to overhaul business strategies and update how they compete in their respective markets.
The FinServ industry, perhaps more than any other, is built on complex and iterative data analytics at its core. Yet, according to McKinsey, only 7% of financial services organizations fully leverage analytics in their business.
Agile methodologies and technologies de-risk financial services by creating a highly adaptable culture based on data insights. Agility powers the ability to learn quickly and adjust business direction rapidly as situations change. This allows agile companies to adapt to these changes before more traditional companies can truly understand what has happened.
An agile approach to analytics dramatically improves time-to-action and drives tangible business outcomes such as the ability to:
- Make data-driven decisions at the speed the market demands
- Take a holistic view of market conditions in real time
- Have business experts work with their data in an intuitive environment on their terms
3 Ways FinServ Organizations Can Leverage Data To Defy Market Volatility
1. Outsmart and outmaneuver the competition by tearing down data operations obstacles
The problem: Traditional BI solutions delay critical decision-making and inhibit much-needed business agility
Time is literal money for FinServ companies. The speed at which organizations make data-driven investment decisions can be the difference between massive profits or devastating losses.
Most traditional analytics tools require data to be heavily modeled by data and BI teams before consumption by domain experts. That means that business users have to know the questions they want answered. But the nature of problem-solving in the industry means that getting to the right answer typically requires domain experts to ask a series of iterative and increasingly complex questions that they may not know in advance.
Without the ability to use their BI tool directly, FinServ experts have no choice but to ask their BI team for help, resulting in a series of ad hoc requests that balloon into multi-week projects where work is repeated and time is wasted trying to better answer the initial question.
The analytics workflow is broken, and old waterfall workflows of the past don’t cut it in our fast-paced, always-on world. Today’s knowledge workers need to make data-driven decisions on demand and can’t afford to waste weeks waiting for answers from their BI team or trying to get insights from stale dashboards.
The solution: Lightning-fast cloud analytics
From financial trading and root cause analysis to regulatory reporting and scenario modeling, domain experts throughout the FinServ industry need a way to quickly and freely access the data they need to make agile decisions.
Cloud analytics offers a wealth of benefits that allows teams to be more data driven, including:
- IMPROVED DATA LITERACY
When business users are trained in data analytics best practices and are invited to put data to use, teams can more easily collaborate and make better decisions. And, when non-technical users can conduct their own ad hoc queries using a familiar interface that doesn’t require coding skills, they can surface insights in real time. - IMPROVED ANALYTICS ADOPTION
When business users can mine data themselves to find insights, they’re much more likely to use data in their decision-making. With intuitive cloud analytics solutions like Sigma, data pipeline tools like Fivetran and Matillion, and cloud data platforms like the Snowflake Data Cloud and Amazon Redshift, analytics becomes simple for non-technical users. - BETTER, EASIER COMPLIANCE
New and increasingly complex regulations make compliance and regulatory reporting a significant challenge for even the most diligent organizations. Yet, despite all of the money, time, and resources that go into safeguarding data, one of the most basic, common, and ultimately harmful risks often goes overlooked: data extracted to spreadsheets by business users. In 2020, financial institutions in the US alone were fined $11.11B for violating regulations like anti-money laundering protocols, personal data leaks, and more.
Cloud-native analytics tools
Plug into managed cloud data platforms that provide synchronized governed access and comply with data privacy regulations. Cloud analytics offers compliance peace of mind with limited access points, robust security tools, and governance oversight.
Success Story: How Cowen Stays Ahead Of Regulators
As a publicly-traded financial institution, Cowen Inc. must store and retrieve data for each transaction — up to 11 million trades per day — to maintain regulatory compliance. But Cowen’s trade-processing and record-keeping system introduced significant hurdles for business users because it required SQL expertise to access this historical data.
Every data request had to go through the IT department, which then provided the data in the form of siloed, stale, error-prone Excel worksheets. Cowen needed a user-friendly solution where business teams can get a holistic view of their data while empowering them to respond to regulatory requests quickly and efficiently.
Today, Sigma empowers teams across Cowen to independently access and analyze historical data and take a comprehensive approach to their analytics. Sigma’s ability to manage Cowen’s multi-billion-row worksheets and drill down into row-level data while remaining performant has eased the burden of meeting strict regulatory requirements significantly.
“If we receive a regulatory inquiry or an inquiry from auditors, anyone can now go into Sigma, easily retrieve those records for any given date, and provide them with confidence, knowing the data is accurate,” says Ross Levin, Managing Director of Global Clearing, Settlement & Securities Finance at Cowen Inc.
2. Accelerate decision-making with a universal analytics platform
The problem: Traditional BI solutions are too technical for business experts
The volume and sensitivity of data gathered by financial institutions provide unique challenges compared to other industries. The New York Stock Exchange, for example, captures 1 terabyte of data each day, tracking the second-by-second changes in the prices of securities and other investment vehicles.
To address these challenges, FinServ organizations spend millions on analytics and business intelligence (BI) solutions. But these tools create technical and operational bottlenecks that prevent experts from doing their jobs effectively because they require knowledge of SQL, or proprietary coding languages like SQL, to slice, dice, and pivot for near-real-time analysis.
The pressure to deliver results can be so intense that non-technical knowledge workers take matters into their own hands. Data is downloaded onto laptops, Excel worksheets are created, and spreadsheet sprawl ensues. But traditional spreadsheets simply weren’t built to handle the variety, volume, and velocity of today’s data. In spreadsheets, for example:
- Data is limited to 1 million rows or less before slowing down or crashing
- Combining data from across different sources is extremely difficult and tedious
- Extracts have inherent security and compliance issues because sensitive data can be shared and copied without restrictions or visibility
- The data is immediately out of date as soon as it is extracted and cannot be updated automatically
The solution: Empower your team with a data experience they already know
Because domain experts need the ability to explore data on an ad hoc basis and find the answers to both high-level and follow-up questions, organizations need an analytics tool that can accommodate non-technical users. Team members without SQL skills must have the ability to manipulate queries and formulas in an easy-to-use interface.
A “common data language” also facilitates collaboration and sharing — two activities that are inherent to the value of agile data analytics. Domain experts each have a unique perspective and skill set that they bring to the table, and teams can take more effective action when they have input from everyone who has insight into a situation. Some cloud analytics tools make it easy to share, build upon, and repurpose colleagues’ analyses — all in a secure environment with no version control issues.
Accelerate decision-making with a universal analytics platform
The problem: Traditional BI solutions are too technical for business experts
The volume and sensitivity of data gathered by financial institutions provide unique challenges compared to other industries. The New York Stock Exchange, for example, captures 1 terabyte of data each day, tracking the second-by-second changes in the prices of securities and other investment vehicles.
To address these challenges, FinServ organizations spend millions on analytics and business intelligence (BI) solutions. But these tools create technical and operational bottlenecks that prevent experts from doing their jobs effectively because they require knowledge of SQL, or proprietary coding languages like SQL, to slice, dice, and pivot for near-real-time analysis.
The pressure to deliver results can be so intense that non-technical knowledge workers take matters into their own hands. Data is downloaded onto laptops, Excel worksheets are created, and spreadsheet sprawl ensues. But traditional spreadsheets simply weren’t built to handle the variety, volume, and velocity of today’s data. In spreadsheets, for example:
- Data is limited to 1 million rows or less before slowing down or crashing
- Combining data from across different sources is extremely difficult and tedious
- Extracts have inherent security and compliance issues because sensitive data can be shared and copied without restrictions or visibility
- The data is immediately out of date as soon as it is extracted and cannot be updated automatically
The solution: Empower your team with a data experience they already know
Because domain experts need the ability to explore data on an ad hoc basis and find the answers to both high-level and follow-up questions, organizations need an analytics tool that can accommodate non-technical users. Team members without SQL skills must have the ability to manipulate queries and formulas in an easy-to-use interface.
A “common data language” also facilitates collaboration and sharing — two activities that are inherent to the value of agile data analytics. Domain experts each have a unique perspective and skill set that they bring to the table, and teams can take more effective action when they have input from everyone who has insight into a situation. Some cloud analytics tools make it easy to share, build upon, and repurpose colleagues’ analyses — all in a secure environment with no version control issues.
Agile Financial Trading: “The Early Bird…”
Financial traders and investment professionals at banks, hedge funds, investment firms, and other FinServ companies often spend every waking moment thinking about how to make their portfolio’s performance better. They’re constantly trying to identify trends and patterns to inform the best time to buy or sell stocks and other financial instruments.
The best traders often have fast internet connections, expensive subscriptions, and applications to gather market data and execute orders. But, when it comes to data analysis, most BI tools are too cumbersome and slow — so traders live and breathe in traditional spreadsheets. Since traders are spreadsheet gurus, this works as long as the data is small enough to fit. But, even then, the data in Excel is always stale.
The massive scale of the global financial system has become far larger than the 1M-row limit of Excel. And, when you include fine-grain historical data, traditional analytics tools are quickly brought to their knees. This hampers rapid decision-making and costs these investment managers and their companies money every day.
What they really need is a spreadsheet-like environment with formulas and pivot tables like Excel, but with quick access to their full, live data.
3. Enable Business Users With Governed Exploratory Analytics
The problem: Lack of access to complete, granular data
Solving FinServ analytics challenges doesn’t require pretty dashboards or more Excel — it requires a better, more exploratory approach to analytics. Traditional business intelligence products fail to deliver upon the promise of “self-service” analytics, instead delivering pre-canned reports and drilldown paths that don’t give domain experts the ability to flexibly work with the data they need to do their jobs and make informed decisions.
Organizations need a solution that allows FinServ experts to independently explore all of their live data down to granular detail in an environment they know and love: the spreadsheet.
The solution: Governed self-service analytics that works
The best way to increase analytics velocity is to empower domain experts with self-service access to the complete data they need in a spreadsheet experience they know how to work with.
For self-service to fulfill its potential to assist in better data-driven decision-making, it needs to go beyond known KPIs and facilitate answers to ad hoc questions that couldn’t be anticipated before they come up.
Because Sigma operates on top of the cloud data platform — a centralized, secure, and fully governed repository that acts as a scalable, single source of truth — it allows anyone to explore and query live data directly.
Agile Investment Analysis: Finding the Needle in the Haystack
The aim of investment analysis is to determine how an investment is likely to perform and how suitable it is for a particular investor. Key factors in investment analysis include the appropriate entry price, the expected time horizon for holding an investment, and the role the investment will play in the portfolio as a whole, regardless of type.
Massive amounts of historical data are required to perform comprehensive analyses of this nature. It is not uncommon for these data sets to contain hundreds of thousands, if not millions, of rows of data. FinServ business teams need a powerful self-service solution with a proximity to data that empowers users to do free-flowing iterative analyses and make better, faster, data-driven decisions that drive higher profits and productivity.
Ready to Make a Change?
Agile analytics ensures that companies are making the most of their data and empowering their domain experts. And, with an agile data analytics tool like Sigma, FinServ experts are able to find the insights they need when they need them, and can maintain compliance while doing so.
Let’s Sigma together! Schedule a demo today.
