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What are the best practices for performing variance analysis with performance reporting tools and software?

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Variance analysis is a key component of corporate finance, as it helps managers and stakeholders compare actual results with planned or budgeted performance. However, variance analysis can be challenging and time-consuming, especially when dealing with large and complex data sets. That's why many companies use performance reporting tools and software to automate and streamline the process. In this article, we will discuss some of the best practices for performing variance analysis with performance reporting tools and software, and how they can help you improve your financial decision-making.

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Iman Hamdan - ACCA

Advance Your Business and Career by harnessing the power of Strategic Finance Expertise With Us | Strategic Finance…
Agbesi Dagodzo,CA,FMVA?,MBA

Manager at EY
Serge Radisic

Co-Founder & Managing Partner, Minerva.com.au | Co-Founder & CEO, Uptio.com | I modernise traditional FP&A functions…

1 Choose the right tool or software

When performing variance analysis, it is essential to choose the right tool or software that meets your needs and objectives. There are a variety of options available, ranging from simple spreadsheets to sophisticated cloud-based platforms. Consider factors such as the size and scope of your data and budget, the level of detail and accuracy required, the frequency and format of reports, integration and compatibility with existing systems and processes, as well as the security and reliability of the data and tool or software. Additionally, features such as data visualization, scenario analysis, forecasting, alerts and notifications, collaboration and sharing, customization and flexibility can help enhance your variance analysis.

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Iman Hamdan - ACCA

Advance Your Business and Career by harnessing the power of Strategic Finance Expertise With Us | Strategic Finance LinkedIn Content Creator.
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For effective variance analysis using performance reporting tools, set clear benchmarks and KPIs for precise measurement. Automate data collection to enhance accuracy and use real-time analytics for immediate insights. Choose software with customizable dashboards to spotlight important variances. Update analysis criteria as business goals or market conditions change. Promote interdepartmental collaboration by sharing data, enabling teams to address variances proactively. Include detailed explanations in reports to clarify the causes behind discrepancies, aiding strategic decisions. This comprehensive approach ensures understanding of performance drivers, supporting informed adjustments and fostering a culture of continuous improvement.

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Serge Radisic

Co-Founder & Managing Partner, Minerva.com.au | Co-Founder & CEO, Uptio.com | I modernise traditional FP&A functions and Siloed Data Environments with Digital Ecosystems that work!
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One thing to consider when choosing the appropriate tool for your business is the speed of adoption. Carefully spend time to understand how quickly a new tool will deliver value and how much upskilling is required to use the tool effectively. If you have a set of transferrable skills, this can be a big advantage. For example, transferring excel skills to a tool which has a high-performing Excel Adin.

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2 Define your variance metrics and thresholds

The next step is to define your variance metrics and thresholds, which are the indicators and criteria that you use to measure and evaluate your performance. Variance metrics are usually expressed as percentages or ratios and can be based on revenue and expenses, profit and margin, cash flow and liquidity, sales and market share, or quality and customer satisfaction. Variance thresholds are the acceptable or expected ranges or limits for your variance metrics, which can vary depending on industry, business model, and strategy. For instance, a variance threshold of +/- 5% for revenue growth or +/- 10% for operating expenses could be set. Additionally, the frequency and time frame for variance analysis should be established; this could be monthly, quarterly, or annually.

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Agbesi Dagodzo,CA,FMVA?,MBA

Manager at EY
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In my experience it is important to set variance thresholds as wells as expectations on each item or variance noted. This ensures focus on variances that matter

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Serge Radisic

Co-Founder & Managing Partner, Minerva.com.au | Co-Founder & CEO, Uptio.com | I modernise traditional FP&A functions and Siloed Data Environments with Digital Ecosystems that work!
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Variance and thresholds vary for each business. It's valuable to have the ability to drill down to the lowest level in a hierarchy to understand where the variance is. For example if you have a 5% variance at high level (top line revenue) , there could be a product category which has led to this variance. Having the ability to quickly drill down in the data hierarchy will uncover this.

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3 Collect and validate your data

The third step in variance analysis is to collect and validate your data. It is essential that the data is accurate, complete, consistent, and relevant, as any errors or discrepancies can affect the quality and reliability of your variance analysis. You must also make sure that your data is aligned with your variance metrics and thresholds, and that it reflects the current and realistic conditions of your business environment. Automated data extraction and integration tools or software can connect to data sources, such as accounting, sales, or CRM software. Data cleansing and standardization tools or software can help detect and correct any errors, duplicates, or outliers in your data. Additionally, data quality and governance tools or software can monitor and enforce data policies and rules such as security, privacy, and compliance.

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Prasaanth Selvam

Functional Business Analyst @ TCS Bancs - Investment Banking(Wealth Management)| PGPM in Business Analytics and finance
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1.Collect and Validate Data: Gather accurate data from relevant sources and verify its integrity to ensure accuracy. 2.Utilize Automated Data Integration: Leverage software for automated data integration from various sources, reducing manual errors. 3.Define Key Performance Indicators (KPIs): Clearly define KPIs aligned with organizational goals for meaningful insights. 4.Establish Baselines and Targets: Set baseline benchmarks and targets for comparison with actual performance. 5.Standardize Reporting Formats: Adopt standardized reporting formats for consistency and easier comparison. 6.Implement Drill-Down Capabilities: Enable drill-down features to investigate variances at a granular level.

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4 Perform and interpret your variance analysis

Performing and interpreting your variance analysis is the fourth step in the process. You should use performance reporting tools or software to calculate and display your variance metrics, compare them with your variance thresholds, and generate and present your variance reports. When performing and interpreting your variance analysis, consider using data visualization and dashboarding tools to create and customize your reports; scenario analysis and forecasting tools to simulate and project different outcomes; alerts and notifications tools to track and monitor your results; as well as collaboration and sharing tools to communicate with team members, managers, or stakeholders.

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Serge Radisic

Co-Founder & Managing Partner, Minerva.com.au | Co-Founder & CEO, Uptio.com | I modernise traditional FP&A functions and Siloed Data Environments with Digital Ecosystems that work!
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Begin by employing elegant data visualization techniques, such as refined charts and dashboards, to elucidate subtle trends. Tailor reports to accommodate diverse stakeholders, ensuring clarity and relevance across different levels of the organization. importantly foster a culture of collaboration ensuring seamless information flow among team members.

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5 Identify and address the causes and effects of your variance

The fifth step is to identify and address the causes and effects of your variance, which are the reasons and implications of your performance gaps. You should use your variance analysis to understand and explain the factors and drivers that contributed to your variance, such as internal factors like operational inefficiencies or cost overruns, external factors like market conditions or customer behavior, or random factors like errors or outliers. Additionally, you should use your variance analysis to assess and evaluate the impacts and consequences of your variance, such as financial impacts like revenue loss or profit decline, strategic impacts like market share erosion or competitive disadvantage, and operational impacts like resource allocation or budget adjustment.

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Serge Radisic

Co-Founder & Managing Partner, Minerva.com.au | Co-Founder & CEO, Uptio.com | I modernise traditional FP&A functions and Siloed Data Environments with Digital Ecosystems that work!
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Begin by conducting a root cause analysis to uncover the underlying reasons behind the variance. For example, if there's a higher-than-expected production cost for a particular product line, compare it to similar product lines to identify any unique factors driving the cost variance.

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6 Implement and monitor your corrective and preventive actions

The sixth and final step is to implement and monitor your corrective and preventive actions, which are the solutions and recommendations that you derive from your variance analysis. You should use your variance analysis to identify and prioritize the areas that need improvement or intervention, as well as formulate and execute the actions that can address or prevent your variance. Additionally, you should measure and track the effectiveness and efficiency of your actions, making any necessary changes or modifications. To do this, you can utilize action planning and management tools or software to define and assign your actions, set goals and deadlines, and monitor progress and results. Furthermore, feedback and evaluation tools or software can help collect and analyze the team’s, managers’, or stakeholders’ opinions on your actions. Finally, performance review and improvement tools or software can help review and update variance metrics, thresholds, data sources/systems, and performance reporting tool/software.

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Serge Radisic

Co-Founder & Managing Partner, Minerva.com.au | Co-Founder & CEO, Uptio.com | I modernise traditional FP&A functions and Siloed Data Environments with Digital Ecosystems that work!
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Get ahead of the curve by using AI/ML to gain insights. For exmaple - predictive analytics forecast future trends, enabling proactive adjustments to meet demand or mitigate risks. AI-powered anomaly detection systems continuously monitor data streams, swiftly flagging deviations for immediate investigation and action. ML engines can offer tailored recommendations backed up data driven analysis.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Haytham M. Azmy - MBA, CTP

CFO @ iSON Xperiences Egypt | MBA in Finance
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It worth to mention, that positive and negative variances needs to be analyzed, since it is not necessary that positive variances means positive result. You might miss something or have an issue in your data inputs. As well as, monitor the KPIs and industrial benchmark of deviations, is it common on your field of services or products or not. As regardless of your projection there might be an acceptable level of deviations you have to project or accept.

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What are the best practices for performing variance analysis with performance reporting tools and software?

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1 Choose the right tool or software

2 Define your variance metrics and thresholds

3 Collect and validate your data

4 Perform and interpret your variance analysis

5 Identify and address the causes and effects of your variance

6 Implement and monitor your corrective and preventive actions

7 Here’s what else to consider

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