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Finance teams’ shifting role toward advanced analytics

In recent years, there has been a significant shift in the role that finance departments typically play in leading their company’s digital transformation and spearheading the organization’s use of advanced analytics.

The expectations used to be to report on how the organization has done (over the last quarter, year, etc.) and in most cases, it was a matter of simple data and basic financial reporting. Now the role is about helping the company grow from a financial perspective, utilizing all available metrics and taking full advantage of real-time data analytics.

While the concept of data analytics is nothing new, the focus on advanced analytics has created a need for new approaches, innovative organizational strategies and modern technology platforms to support SaaS finance teams within growing companies and their evolving analytics needs.

There are many stakeholders around an organization impacted by the SaaS reporting needs of the business. Whether that’s the finance team charged with the collating, parsing and analyzing of financial and operational data, or the consumers of that business intelligence such as revenue operations, executive leadership, board of directors or other external investors. These stakeholders all have a vested interest in the efficiency, availability, analytical depth and reliability of reporting tools employed by the organization to support strategic data-driven decision-making.

In today’s market, business intelligence platforms are as critical as ever in ensuring that organizational growth initiatives are generating success and that capital is being employed efficiently. Consequently, the risk of not having a sufficiently advanced analytics platform has never been more significant – and that is not going to change anytime soon.

Examining common data analytics challenges for SaaS finance teams

The adjacent graphic might look all too familiar to you. Perhaps you recall a time when you (or someone on your team) made a change to an Excel formula or moved a reference cell, and suddenly your entire reporting model was broken with no obvious unwinding mechanism in sight.

While Excel and Google Sheets can be useful tools, they are manual, error-prone and backward-looking solutions that obstruct a user’s ability to perform high-quality analytics – as they only offer surface-level metrics and limited segmentation. Furthermore, those solutions don’t scale – only increasing in complexity and instability as data volumes expand – leaving growing companies absorbing enterprise technical debt and scrambling for a more comprehensive and reliable tool to meet ever-growing reporting demands from stakeholders.