Traditionally defined financial data is a tracker of results, but it is a poor guide to how your business can deliver and achieve results. Everything that comes up in an organization ultimately falls into revenue, with far more money than just dollars, according to Siqi Chen, co-founder and CEO of Runway Financial. It's a wider area than many financial teams care about exploring.
For current and future CFOs, This expanded view must be reflected in the model and prediction. Data flow ency (interregulation of messy data between departments) and sensual insights (understanding how different departmental models affect revenue) are essential capabilities for financial executives. Software solutions also need to advance.
We asked Chen, former Vice President of Growth at Postmates, Zynga's product head and CEO of Sandbox VR, to explain how and why the organization can tie data together to get more accurate photos of its business.
With regard to organizational data, what will AI be doing for finance in the near future?
One role of AI is to understand the complexity of data, including automation, as well as the different types of data generated and how they all fit together. What questions do you often get about CFOs? The number of accounts or line items will change from one month to the next number. Why did that change? Finance probably needs to track the formula and drill Excel. However, AI is beginning to understand how models work in Excel. It's not just about changing the model and data, but also about the underlying context. That's pretty dramatic [development] It has happened in AI models over the past few months.
What kind of “data flow ency” does CFO need?
It's about understanding everything that's happening in your company and all the decisions that's going on. In the case of finance, it is usually captured in the HR system, GL (expenses and revenue), and perhaps CRM. However, there is more data within the company, including marketing data, product usage data (used data that is usually stored in a data warehouse), engineering cost data (which may be found on AWS).
There is also context-unstructured data, such as product roadmap and strategic planning, but perhaps that is [workspace like] Concept or Google Docs. The concept of data flow ency involves understanding what is reflected in the data and ensuring it is clean and captured in a useful way. And you can put it all in one place to understand it. You also need to understand the quality of your data, how your system flows, and how to improve it.
To truly understand what's going on within the company and translate it into a financial model, finance needs to work with other departments within the company.
At the financial level, most models are concerned about budgets and spending. However, at the operational level, it could be product development. Where do you allocate resources?
For example, one of the key outputs of a product development organization is the product roadmap. What levers do you pull in the product roadmap? Perhaps new features or versions could increase contract values or improve sales conversion rates. We want to live in the world – and I think this world is out of reach. A product or engineering manager should be able to raise funds. A common understanding and integrity of business is more important than ever.
What is preventing it from happening?
Many companies can't imagine having those conversations today. But people want people to know what they do [tie that to] Economic outcomes. However, when considering the default tools available for finance, how does finance share that information? I think it's basically a limitation of products and tools.
The interview was first published in the June 27th issue of CFO Leadership's Finance and Accounting Technology Briefing.
