Capturing Data for Financial Analysis

Before an analyst can perform financial analysis, there are a few prerequisites the analyst needs to complete. First, the analyst needs to capture – or work with someone who has access to capture – financial data from the various systems within an organization. After obtaining the data sets, the analyst needs to merge the data sets of similar (financial) nature into a single extract or worksheet- this will greatly enhance the financial analysis process. Lastly, the analyst needs to determine if data cleanup is needed prior to performing financial analysis. When the analyst has worked through the three steps above, the data should now be ready for financial analysis.

I would argue that the most important step from the three prerequisites mentioned is the first step, which is capturing financial data. After all, if there is no data, there will be no analysis. Additionally, borrowing a computer science concept, we know that GIGO principle – which stands for Garbage In, Garbage Out – remains true for financial analysis. The quality of our analysis depends heavily on the quality of financial data we have access to. Capturing financial data correctly and effectively, therefore, becomes a very important task.

There are many acceptable ways to capture financial data, but a few principles still stand regardless of the analyst’s methodology of choice. The first principle is: the more data we have, the better it is. For example, lets assume that August is an exceptionally slow month for an organization. Looking at only two-months-worth of data – for example, July and August– we do not know if July’s revenue of 10,000 USD is considered high for the organization nor August’s revenue of 5,000 USD is considered low for the organization. If we have access to more data, however, we can identify financial patterns quickly. If, for example, we have access to all monthly revenue data for the fiscal year, and that we see the company’s average monthly income is 10,000 USD, we can quickly see that August’s revenue is indeed an outlier, and August is an exceptionally slow month for the organization.

The second principle in capturing data for financial analysis is: the more financial details we have, the better it is. If we know a nonprofit has an expense of 100,000 USD in the month of July, for example, we will not be able to make a lot of conclusions about the nonprofit and its operations based on this information alone. If we have a detailed financial information, however, we will be able to learn additional insights about the nonprofit. For example, if we know that the nonprofit spends 20,000 USD on fundraising activities, 50,000 USD on salary and benefits, and 30,000 USD on its facilities in a single month, we can deduce that 50% of the nonprofit’s monthly expenses are likely allocated towards paying its employees. This deduction can be useful as an assumption when we are building a budget or forecast for a specific financial period. For example, if we budget or forecast a particular month’s expense to be 200,000 USD, we can make a reasonable assumption that 100,000 will likely be allocated towards staff members’ salaries.

The most important principle in capturing data for financial analysis is this: at the end of the day, financial data capture needs to be driven by business needs and goals. Before choosing the best method to capture an organization’s financial data, it is important to first ask, “What business question are we trying to answer?” This principle is important because the type of financial data we need to capture depends on what we are trying to accomplish from the business perspective. If we are trying to forecast revenue target for the new fiscal year, for example, we will not gather useful information by analyzing the organization’s expenses, and vice versa. At the end of the day, financial analysis serves one main purpose- to practically help an organizational leader make the best strategic decision for their organization.