• Written by Azhar Aliyar, Director

A financial controller checking the latest budget report notices that the numbers do not quite add up, despite the system indicating that everything is in order. This discrepancy often stems from issues with the underlying data foundations, which can lead to incorrect or incomplete information being presented to decision-makers.

Field-level meaning and why column names are not documentation

The meaning of each field in the financial database is crucial to ensuring that the data is accurate and reliable. However, column names are often insufficient as documentation, as they may not fully capture the nuances of the data. For instance, a column named "cost" may refer to the cost of goods sold, but it may not be clear whether this includes or excludes certain expenses. To address this, it is essential to establish clear and concise definitions for each field, including the units of measurement, data types, and any relevant calculations or transformations.

This process requires careful consideration of the specific requirements of the finance function, as well as the systems and processes that generate and use the data. By taking the time to thoroughly document the meaning of each field, organisations can ensure that their financial data is consistent, accurate, and reliable.

The systems that get left out of scope and the partial answers that follow

When implementing a financial data analysis system, it is easy to focus on the most obvious sources of data, such as the general ledger or accounts payable. However, other systems, such as accounts receivable or payroll, may be overlooked, leading to partial or incomplete answers to financial questions. This can result in decisions being made based on inaccurate or incomplete information, which can have serious consequences for the organisation.

A thorough understanding of the organisation's financial systems and processes is necessary to ensure that all relevant data sources are included in the analysis. This may involve working with various stakeholders, including finance teams, IT departments, and external auditors, to identify and integrate all relevant data sources.

The finance data this depends on: general ledger, budget, open commitments and purchase orders, receivables and terms

The accuracy and reliability of financial data depend on several key data sources, including the general ledger, budget, open commitments and purchase orders, and receivables and terms. The general ledger provides a comprehensive record of all financial transactions, while the budget outlines projected income and expenses. Open commitments and purchase orders reflect the organisation's future financial obligations, and receivables and terms indicate the amount and timing of expected income.

By integrating these data sources, organisations can gain a complete and accurate picture of their financial situation, enabling them to make informed decisions about resource allocation, investment, and risk management. For more information on how AsscherAi can help with financial data analysis, visit our finance page.

What it changes about: variance explanations that arrive after the period and budgets that look healthy while overcommitted

Traditional financial analysis often relies on variance explanations that arrive after the period, which can make it difficult to identify and address issues in a timely manner. Additionally, budgets that appear healthy may actually be overcommitted, hiding potential financial risks. By using a financial data analysis system, organisations can identify and address these issues more quickly, enabling them to take corrective action and mitigate potential risks.

This can involve using data to identify trends and anomalies, as well as to forecast future financial performance. By taking a more proactive and data-driven approach to financial management, organisations can reduce their exposure to financial risks and improve their overall financial health.

Measured with: variance to budget, commitment coverage, days sales outstanding

Several key metrics can be used to measure the effectiveness of a financial data analysis system, including variance to budget, commitment coverage, and days sales outstanding. Variance to budget measures the difference between actual and projected financial performance, while commitment coverage indicates the extent to which the organisation's financial commitments are covered by available funds. Days sales outstanding reflects the amount of time it takes for the organisation to collect payment from customers.

By tracking these metrics, organisations can gain a better understanding of their financial performance and identify areas for improvement. This can involve using data to identify trends and anomalies, as well as to forecast future financial performance. For more information on how to get started with AsscherAi, visit our contact page.

What this does not do

While a financial data analysis system can provide significant benefits to organisations, it is not a panacea for all financial management issues. It does not replace the need for skilled financial professionals, nor does it eliminate the requirement for careful financial planning and budgeting. Rather, it provides a powerful tool for financial analysis and decision-making, enabling organisations to make more informed decisions and drive better financial outcomes.

It is also important to note that a financial data analysis system is not a one-time implementation, but rather an ongoing process that requires continuous monitoring and maintenance. This involves regularly reviewing and updating the system to ensure that it remains accurate and reliable, as well as adapting to changing financial conditions and organisational needs.