It’s absolutely vital that any organization maintain visibility into the health of their business. Data science can take organizations to a new level of understanding when it comes to financial stability, growth, and the ability to compare actual performance to goals. This featured finance dashboard demonstrates some of the insights finance teams can gain through data science.
As financial teams monitor the fluctuation of costs and revenue, they maintain focuson identifying meaningful decisions that drive higher value for the company. Properly executed data science within the financial space empowers these teams to focus on what should be done in the future rather than what’s already happened. This dashboard is an example of how data science models focusing on financial insight can be integrated into the business process and used to make model results more accessible to financial teams. Dashboards play an important role in highlighting data science outputs and ultimately help organizations make informed decisions faster.
This dashboard displays the cost, revenue, and net income of a company and highlights the dominant drivers for any fluctuations in net income. We can see that in September of 2019, there was an abnormal increase in both labor and product costs for the Cow’s & Cream Seattle location. At the same time, revenue is steadily decreasing, and forecasts suggest the company will start losing money in the coming months if labor and product costs continue to increase.
This robust analysis is made possible by multiple data science models that support this dashboard. They paint a picture of forecasts and financial fluctuations, which commonly have many contributing factors. Each model addresses a slightly different question that informs a financial investigation. These are some of the defined business questions that drove the development of these models and led to the design of this dashboard:
- What was the cost and revenue profile and what is it likely to be in the future?
- What are the main contributing factors in fluctuations in cost and revenue?
- Which of these contributing factors demonstrate uncommon behavior?
- Of the factors that display uncommon behavior, which are projected to remain out of the ordinary in the short term? Which are projected to become a long-term shift?
These kinds of questions are a common component of financial investigations, but specific organizations will have their own set of relevant contributing factors to explore.
Explanation of dashboard use
This graph illustrates the cost and revenue balance leading up to today’s date and the projected forecast for the next 4 months. The ability to analyze future cost and revenue trends is highly valuable and a direct result to building the data science model that powers this dashboard.
Generally speaking, there are many elements that contribute to fluctuating cost andrevenue, each with their own trends and nuances that are difficult to predict. This metric is an output of a data science model and represents a robust illustration of the factors that contribute to shifts in cost and revenue.
This section highlights trends that are significantly above or below what is expected for the selected city. This allows dashboard users to quickly see elements that are out of the ordinary and therefore contributing to fluctuations. Data science models identify these anomalies, which helps business users understand peaks or values in revenue fluctuation.
This section compliments visualization to the left because it shows how anomalous trends may fluctuate in the future. This predictive trend, like that of visualization 1, is a directly result of building the data science model that supports the dashboard. Itanswers the question, “Is this out of the ordinary behavior likely to continue for the next few months?”