Best Practices

Unveiling the Power of Data Analytics in Finance: Insights from Joy Zhang

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Joy Zhang is a Customer Growth Manager at Vareto. Past experience includes Finance Manager, Prime International Expansion at Amazon, supporting the success of the Prime Program in recently launched countries world-wide, and Finance Manager of Decision Support at UW Medicine, where she supported strategic growth programs across the UW Medicine system.

Modern business runs on data of all kinds. Every customer interaction generates data, offering businesses a wealth of information and providing insights into customer preferences, market performance, and more. When analyzed correctly, this information enables companies to make informed decisions, refine their offerings, and maintain a competitive edge. 

But having the data isn’t enough. You have to know what to do with it. Data analytics isn’t always tied to finance, but finance teams can play a crucial role in deriving actionable business insights from swaths of data—even beyond the financials. 

The power of data analytics in finance

Finance teams are accustomed to utilizing historical data to project forward but are frequently limited to historical financial information. Data analytics brings other forms of data (outside of just financial), like operational, consumer, or even internal personnel data, into the financial processes. Advanced analytics drive additional insights, which should drive more impactful action and strategy. 

There is a wealth of operational data outside of financial KPIs. In a healthcare setting, this can include the number of patients, procedures, referral rates, cancellation rates, or personnel data, including the ratio of nurses to doctors, the ratio of patients to nurses, and the ratio of patients to space capacity.

While these data points aren’t called out on a financial statement, they are key drivers to revenue and expense. For example, forecasting the flow of patients directly impacts future topline revenue.  Bringing in external sources of data can improve a finance team’s forecasting model to be more contextual and aligned with the realities of the business. 

Interpreting operational data also informs the business of what levers they can pull to better control their finances. In healthcare, staffing is a key lever that departments can pull to increase revenue or decrease expenses. For example, revenue may be stagnant because the current physicians’ capacity is maxed out. A lever to pull is hiring another physician, and the cost-benefit analysis is whether the cost of hiring a physician outweighs the revenue from new patient visits. 

Or perhaps revenue is stagnant and not growing because the building space is at capacity, and the organization has more patients on the waitlist than it can accommodate. The action forward is to rent more space and analyze whether paying for more space outweighs the revenue of seeing patients more quickly and reducing the waitlist.

Collaboration as the key to success

Data analytics is the most meaningful when understood within the greater context of the business. Data itself is concrete. It’s proof that a set of actions and behaviors have already occurred in real life and may predict future behaviors or actions. But, data is also easy to misinterpret and infrequently outputs the clean trends seen in a textbook: interpreting data is both a science and an art.

First, the science. These are the hard skills needed to analyze data and draw numerical conclusions from large sets of information. These hard skills may include basic mathematics (mean, median, mode), more advanced statistics (correlation, standard deviation, confidence intervals), or actual data science (programming, machine learning, AI).

Then, there’s the art. Even numerical outputs aren’t clear-cut and can be muddy. It takes experience to identify when statistical outcomes are meaningful or relevant. Most importantly, it takes experience to interpret the data in a business context and understand the story behind the numbers. 

While many finance professionals have the mathematical ability and logical prowess to compute the data, they may lack the context to bring it meaning. This disconnect could be because of siloed teams, inaccessible data due to privacy policies, or a lack of understanding of the broader business context. 

Here’s where it’s vital to bring in the department leader who's an operational expert in that specific area or field. In my experience in healthcare, the business context was frequently tied to a medical explanation, and medical experts regularly collaborated with finance to increase their shared knowledge base. Empowering a finance professional to say, “I have these numbers, I’m seeing these trends, and I don’t know what it means,” and having the department or medical leader come in and say, “Here’s why this makes total sense,” is incredibly valuable. A consistent feedback loop between finance and business leaders drives finance to understand what types of data to focus their attention on. It empowers business leaders to make more data-driven decisions. Finance teams that work hand-in-hand with business partners deliver real value to the organization. 

Embracing data analytics in finance

Bringing broader data analytics into finance can be a game-changer. Insights from data don't just inform—they shape the future of businesses. So, how should finance professionals respond?

Data integrity remains a priority. Enhanced collaboration is essential. And utilizing the right analytical tools can streamline processes. Selecting tools that align with the team's needs and can offer a single source of truth for collaborative teams is crucial.

As the role of finance expands to encompass these broader analytical responsibilities, professionals in the field must adapt and remain integral to their organization's decision-making processes—even beyond the finance department.

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