The New Frontier in Finance

From Streams to Cycles

To address the expanded scope of responsibilities, some CFOs – primarily in small to mid-market (50-500 employees) – now report overseeing data & analytics and RevOps teams to bridge organization alignment. More generally, there is a shift towards all analytics and operations teams working together in a more integrated way to effectively orient the company on long-term strategic goals and break down information silos between teams.

Poor data quality costs the US economy up to $3.1 trillion annually. Dysfunctional financial reporting was estimated to cost US businesses $7.8 billion in 2022. Despite the proliferation in data tools, many companies are struggling to still utilize data to run the business.

This remains a massive problem. In fact, over 70% of enterprises are still behind in their ability to create value from data. 87% of data projects never even make it to production. Data projects that do not involve consumers of data, like business teams, in the design phase often result in lower adoption. Including data teams as partners or integral parts of the business team can encourage better alignment and faster decision making.

Businesses are selling into more channels and business models than ever before, making revenue management a significant challenge. As this complexity grows, so does the need for a cohesive department to accelerate growth and provide line of sight into revenue. In a 2022 survey, 95% of C-Suite executives in high-tech and software said they have implemented RevOps-related initiatives or are in the process of or considering doing so. By housing the RevOps function with GTM finance partners, CFOs can ensure that both departments are anchored on the same post-sales goals to drive change across sales, marketing, and customer success.

That said, organizational alignment doesn’t solve all problems. The tools used in the respective departments today may not support a unified workflow.

In an interview with Contrary Research, a strategic finance lead at a $10 billion+ HR software company described the connection between finance and RevOps this way:

“There are huge workflow gaps with existing finance and RevOps tools right now. For example, in CPQ, CRM, and billing systems (i.e. RevOps tools), if a customer comes online, they could be 6 months out from starting the tool and ARR doesn't go live until then. How do you set the onboarding period and make sure it’s booked correctly through [your financial systems]? We had to build internal tools to figure this out.”

In addition to operationally balancing financial and revenue reporting, there are limits to integration between these systems. Sterling Snow, the SVP of Revenue at Divvy and now Bill.com, made this clear:

“Every time you launch a product, there's a lot of manual work between finance and revenue teams to clean up and map the data, including ARR, closed/won, and billings. It takes a lot of time to align on core definitions.”

Building Blocks of the Finance Stack

The ERP is the system-of-record which contains the accounting general ledger and offers non-financial resource planning capabilities (e.g. supply chain, project management, inventory). Smaller businesses tend to opt for Quickbooks (Intuit) and Xero. Larger companies graduate to enterprise-grade solutions like NetSuite (Oracle) or SAP. Some aspects of the traditional ERP have been effectively unbundled into offerings like Coupa or Bill.com. Coupa is a business spend management tool that competes with SAP Ariba or Oracle’s NetSuite SCM module. Bill.com has carved off aspects of a traditional ERP that would be served by products like SAP Concur or Oracle’s Financial Module.

Traditionally, management viewed data warehouses as cost centers under the CTO. While many companies do have internal processes to store and collect data, the sheer quantity of data produced in the last decade has forced CFOs to turn to external sources. By centralizing the data warehouse and using it as the source of truth, finance and non-technical teams can more quickly query and analyze multiple data sources. Access to historical data increases performance transparency and agility for decision making. Popular data warehouses include Snowflake, Amazon Redshift, and Google BigQuery.

In an interview with Contrary Research, James Lee, the VP of Finance at Productboard, outlined the complexity in setting up a fully functional data management system:

“The hardest thing is ensuring data quality and metric standardization. Companies often wait too long to set up a business intelligence/analytics department or data lakes. Having a good setup allows you to democratize knowledge and drive decision speed. Everyone needs to stare at the same numbers.”

FP&A tools enable finance teams to plan, forecast, report on, and analyze their business. First-generation companies like Anaplan, Adaptive, and Planful primarily served as repositories for financial plans, but did not eliminate friction in the cross-functional work that goes into the act of planning, reporting, and decision-making. As such, finance teams still wrestle with a combination of CSVs, Excel/Google Sheets, and ad hoc SQL outputs. The idea of automating and streamlining these things is a more modern need which only newer tools are trying to solve.

Although Microsoft Excel is still the most popular FP&A tool, newer platforms are focused on consolidating user workflows into one place by creating access controls, collaboration functionalities, data integrations, and web-based modeling and reporting. These tools take one of two approaches: (1) augment spreadsheets (OnPlan, Datarails, Cube), or (2) replace spreadsheets entirely (Vareto, Pigment, Mosaic). The former approach will be more familiar to Excel-heavy users, but can be limited in features and functionalities as these companies do not have total platform control.

Labor can constitute ~70% of total operating expenses, yet headcount planning is notoriously difficult given the multiple stakeholders, complex data sources, and competing priorities involved. Newer tools aim to be the source of truth that syncs across employee data systems, including human resources information systems (HRIS) and applicant tracking systems (ATS). Unlike spreadsheets, these tools allow companies to plan their teams, track hiring, forecast attrition and budgets with role-based access controls. Notable players engaging the workforce include ChartHop, Knoetic*, and TeamOhana.

Managing revenue has become an increasingly complex exercise for companies as they start to balance a variety of different models for monetization. Managing the entire revenue cycle, and in particular the process of accounts receivable (e.g. money you’re owed), has become an exercise in complexity. For traditional subscription businesses, companies like Zuora, Chargebee, and Recurly are typical platforms for managing AR. In addition, the rise of usage-based pricing models has given rise to companies like Metronome and Orb.

Managing a company’s expense account can lead to death by a thousand cuts for many businesses. The average company spends $343K a year just on SaaS subscriptions. Legacy players like Avidxchange and Bill.com helped businesses pay faster, but newer AP and spend management companies are focused on helping businesses pay less. They do this by contextualizing drivers for vendor and employee spend, automating accounting workflows, and streamlining reporting and bill pay. Notable players include Ramp*, Brex, Vic.ai, and Glean.ai.

According to a 2022 survey, ~50% of businesses are struggling to control cloud costs, partly due to lack of visibility into usage. Reducing cloud costs has become a top priority with 81% of IT leaders directed to reduce or take on no additional cloud spending. “FinOps” is a cross-functional practice that brings together finance and engineering to optimize cloud spending, gain more financial control and predictability, and deliver products faster. Although this category has existed for more than a decade with companies like Cloudability, CloudHealth, and Apptio, increasing complexity in cloud infrastructure led to new players like Vantage, CloudZero, and Zesty.

Today, the average company uses over 130 SaaS solutions, with SaaS spending now the third largest cost item behind payroll and real estate. This, combined with the decentralization of purchasing decisions, has created immense friction for modern businesses. Tracking, managing, and negotiating software spend remains a clunky, complex process.

Procurement identifies cost savings and negotiates on the company’s behalf. SaaS management helps organizations organize, optimize, and orchestrate its SaaS suppliers. The categories have converged over the past two years as players look to complete the SaaS buying lifecycle, from discovery to vendor management. Notable players include Productiv, Zylo, Tropic, and Vendr.

Most companies have an accounting close and tax preparation process. If all goes well, the close is a routine process that does not attract much attention from management. But it’s a completely different story if the numbers are late—or wrong. Speeding up the closing process (typically 5-10 days) enables executives to make faster decisions and frees finance teams to focus on more important strategic priorities.

However, these processes are extremely intensive, as accounting and tax teams must work with finance to reconcile transactions and file tax forms under tight deadlines. Historically, companies like Blackline and Avalara have been the legacy tools of choice for close management and tax management, respectively. Companies like Floqast, Trullion, Puzzle, and Anrok have emerged to take advantage of the way these processes are changing.

Payroll and benefits platforms simplify the management of a global workforce, from recruiting and onboarding to compensation and compliance. Broader HR platforms like Rippling and Gusto aim to be the single system of record across departments. As the pandemic fueled the distributed workforce, companies like Deel, Remote, and Papaya Global helped businesses hire both independent contractors and full-time employees and streamline payroll. Although these systems are primarily owned by HR, finance teams often involved as payroll expenses constitute a significant portion of spend.

Neobanks primarily serve startups and offer traditional banking services (e.g. checking/savings accounts, financing, treasury) coupled with modern technology, such as APIs that allow customers to build customizable automations into their workflows. After Silicon Valley Bank’s collapse, companies like Mercury and Brex experienced billions of dollars of deposit inflows.

Big tech companies have their own dedicated corporate treasury departments which engage in critical capital return programs to drive shareholder return. Apple, for example, has its own asset management company called Braeburn Capital to handle its cash. However, this function does not exist at the SMB level today. Emerging players focused on standalone treasury product include Vesto*, Arc, and Meow.

Braving the New Frontier

• Demonstrate ROI for adjacent departments: As CFOs continue to align finance and business partner workflows, tools must demonstrate tangible ways that help both finance and stakeholders work together more efficiently. Improving finance productivity alone is not enough. Who else are you enabling? How will your tool help cross-functional partners collaborate with finance?
• Start with data, not machine learning: Many startups are quick to tout “AI/ML for finance.” But applying ML effectively requires robust data architecture to support your data flows. What data will you have access to, and what new data will you be creating? For example, will your software be a new system-of-record for a specific use case, or are you focused on consolidating existing datasets and layering on your own insights on top? Be clear on the data structure that underlies your software, and how it will interact with other tools (e.g. via integrations). If your tool’s data seems siloed, finance teams will be reluctant to adopt it. Also, without a clear data foundation, you won’t be able to effectively leverage AI.