- Statistics for Data Science
- James D. Miller
- 348字
- 2021-07-02 14:58:47
Financial reporting
In every organization, there is a need to produce regular financial statements (such as an Income Statement, Balance Sheet, or Cash Flow statement). Financial reporting (or Fin reporting) is looking to answer key questions regarding the business, such as the following:
- Are we making a profit or losing money?
- How do assets compare to liabilities?
- How much free cash do we have or need?
The process of creating, updating, and validating regular financial statements is a mandatory task for any business—profit or non-profit based—of just about any size, whether public or private. Organizations, still today, are not all using fully automated reporting solutions. This means that even the task of updating a single report with the latest data could be a daunting ordeal.

Financial reporting is one area that is (pretty) clearly defined within the industry as far as responsibilities go. A data developer would be the one to create and support the processing and systems that make the data available, ensure its correctness, and even (in some cases) create and distribute reports.
-https://venasolutions.com/
Typically, a data developer would work to provide and maintain the data to feed these efforts.
Data scientists typically do not support an organization's routine processing and (financial) reporting efforts. A data scientist would, however, perform analysis of the produced financial information (and supporting data) to produce reports and visualizations indicating insights around management performance in profitability, efficiency, and risk (to name a few).
One particularly interesting area of statistics and data science is when a data scientist performs a vertical analysis to identify relationships of variables to a base amount within an organization's financial statement.
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