As the booming FinTech sector accelerates innovation in financial markets, secure compliant collaboration has become ever more essential. Live, seamless, asynchronous sharing of conversation and content that meets stringent regulatory standards is critical to all manner of finance professionals wherever they work around the world. Our client, a leading SaaS platform for the financial services industry, offers a secure, encrypted cloud-native automated collaboration and messaging.
Over the last two years, the company has targeted growth by expanding its market to create new opportunities for person-to-person collaboration via seamless chat-based, file sharing, automation of trade workflows all in real-time. Their clients range from global mega-banks to boutique specialty firms, all of whom rely on worldwide synchronous and asynchronous collaboration to keep their financial transactions ahead of the curve.
As the 2020 pandemic disrupted almost all other forms of cooperation in the industry, the collaboration SaaS company doubled down on the evolution of its architecture – aiming to do so without sacrificing profitability and development efficiency. Certainly, modern cloud software engineering has made great strides in solving problems of security, reliability, and performance. For example, deep and detailed performance data is available in near real-time through a host of tools and techniques for observability.
But solving for cost transparency too often means waiting for a spreadsheet and a bill at the end of the month. Millions of EC2 instances across the user base spin up and down in tandem with the daily ebb and flow of frenetic activity in the financial markets. Too often, a single minor configuration change or innocent resource commitment in the development organization resulted in a five-or-six figure expense spike.
Using the industry-standard FinOps Foundation Framework would help bring financial accountability to the variable spend model of the cloud. Timely, accessible data and reports were the missing ingredients for enabling distributed teams to make business trade-offs between speed, cost, and quality.
Product operations at the company needed to boost organizational accountability for cost management and cost awareness among their developers. Like many fast-growth B2B SaaS organizations, they set out to put FinOps discipline on par with security, compliance, and governance. Problems they took on:
- Finance and Operations execs needed to attribute engineering cloud expenditures to release productivity, customer revenue, and profitability of some features over others
- Inconsistent cost observability, compounded by dynamic organizational and team structure, made it too hard to chase down the source of cost spikes, especially in upstream development
- Friction in software development processes, especially in the rush to release, created a temptation to spin up (and abandon) costly on-demand resources and ignore potential discounts
CloudGeometry combined its expertise in DevOps, SaaS architecture, and data engineering to build out a scalable foundation for FinOps data, integrated with existing financial controls and business operations. AWS Cost and Usage Reporting (CUR) information, normally only seen via AWS APIs and the AWS systems management console, became a standard part of business operations, integrated with the Domo BI & Visual Analytics platform.
The data platform opened up new ways to drive decisions based on business value.
Trending and variance analysis helps to show why costs increased. As a result, internal team benchmarking could drive best practices and enable continuous improvement.
By structuring, standardizing, and streamlining global FinOps data, CloudGeometry gave the business and financial managers at our client company a level playing field with their engineering counterparts. Now, everyone was speaking from a single set of facts in deliberations over ongoing cloud product and customer operations.
In parallel, software delivery efforts can be measured beyond classic IT metrics like performance and reliability. It meant the product development organization could “keep score” on the ROI of architectural changes directly through to the bottom line. Cost anomalies could be resolved rapidly, further enabling continuous improvement.
Athena and Glue Data Catalog
Provide well-structured ETL and job management from native CUR for all AWS service events
Lambda/Step Function Automation
Automate entity mapping into Aurora PostgresSQL, enables standard RDBMS analytics at scale
Visual Analytics Dashboard
AWS Best Practices for Well Architected Cost Optimization using QuickSight & Domo