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Posts Tagged ‘performance reporting’

Numbers Don’t Lie, Except When They Do: How Statistical Factors Influence Performance Reporting

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Image Source: Freepik

As dashboards, performance reports, and meeting invitations are piling up, a decision must be made or a strategy needs to be developed. This typical workplace scenario can be overwhelming and can make people fixated on numbers. Unfortunately, organizations sometimes heavily rely on such figures to make decisions that prioritize the company’s interests over those of people and the planet. To ensure that a company’s approach to performance reporting and strategic decision-making remains effective and holistic, it is important to understand the different statistical factors causing inaccuracy and inconsistency.

1.Weighted performance scores

Setting priorities and focusing on what matters by correlating importance with a weighted value will definitely facilitate the performance reporting process and is integral to calculating an overall performance score. However, according to The KPI Institute, establishing weight could be subjective and misleading if one is not careful about how to report it because changing weights from one period to another leads to inconsistency of data in time and creates a fallacy of performance improvement or regression.

For example, a report could be presented to the management regarding the improvement in the performance of the sales department from the year 2021 to the year 2022 and that the department performance score has improved by 4%. However, there is a possibility that their performance has not changed at all if all they have done was change the weight of the underperforming key performance indicators (KPIs) to get different results.

To avoid such a misleading presentation, it is important to redirect the conversation to the metrics’ results and not the weighted score or the target. This will drive the decision-maker to concentrate on what matters and see the bigger picture.

Figure 1. Sales department’s performance in 2022 and 2021

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2. Omitting the baseline in graphs

Arguably, the most common form of misrepresentation in graphs is when its Y-axis is manipulated to exaggerate the differences between bars. The truncated graph is produced when the axis starts from a value other than zero. This might give an illusion that the differences are high.

Even if the audience is informed that the Y-axis was truncated, a study found that they still overestimate the actual differences, and the results are often substantial.

Start the axis at zero to show the true context of the data. This will ensure that the data is presented naturally and accurately, reducing the chances of being misinterpreted.

Figure 2. The differences between a regular graph and a truncated graph

3. Small sample size

Darrell Huff, the author of “How to Lie with Statistics”, believed that samples can be trusted only if they are statistically significant. Hence, to be worth much more, a report or analysis based on sampling must use a representative sample, and for it to be relevant, every source of bias must be removed.

For example, a skincare company might advertise its facial wash by putting “users report 85% less skin breakout” on its packages. Upon closer inspection, one may discover that the test group of users consisted of only 15 people. This sample size works well for the skincare company because it is easier to repeat the experiment in a smaller group until they get their desired result while ignoring any findings that do not support how they want to promote their product. Sooner or later, a focus group will show a significant improvement worthy of a headline, and an advertising campaign based on a deceiving result will be created.

There is no clear-cut answer on what the appropriate sample size is since it will be highly dependent on the research type and population size, and there are many statistical methods and equations that could help determine the appropriate sample size, such as Herbert Arkin’s formula or Krejcie and Morgan Table. In addition, when reporting the survey or research results, it’s important to be transparent with the recipient audience and communicate the survey methodology, population, and the logic behind determining the sample size.

4. Misleading averages

While averages can give a simple summary of large data, they might also give half-truths. Case in point, if the range between numbers is too high, then the average would be meaningless. It is optimal that when an average is presented in data, it should be introduced along with supporting facts to provide in-depth analysis and make the right decision at the right time for the business. It is, therefore, important not just to rely on an average but also to look at the distribution of data, understand the range, and consider other statistical measures like median and mode. The median represents central tendency measurements instead of the average or mean because it is less impacted by outliers.

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To conclude, it’s crucial for all stakeholders—whether they’re performance specialists or executives at the top management—to understand how human biases can infiltrate numbers and form a narrative that is completely different from reality. This can be avoided by ensuring that the reported data covers all the quality aspects of completeness, uniqueness, conformity, consistency, and timeliness. In addition, organizations must establish a data validation process to ensure the credence of performance reports.

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About the Guest Expert: Wedad Alsubaie, a seasoned Senior Strategy Management Officer at the National Unified Procurement Company (NUPCO), holds certifications in strategic business planning and KPI and performance management. With extensive experience in enhancing corporate and individual performance, she led the performance development program in Mobily and is now focused on corporate strategy and performance management at NUPCO.

Editor’s Note: This was originally published in Performance Magazine Issue No. 29, 2024 – Strategy Management Edition.

The Importance of Data Gathering in Strategic Planning

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Some say when you fail to plan, then you plan to fail. This is the reason why you should establish a solid strategic planning process for your company. But strategic planning won’t succeed without the right data. Data gathering may sound simple, but you should not underestimate it. Why does it matter and how should you gather your company’s performance data?

Performance monitoring is a systematic process taken by the management in order to track the company’s performance and drive results and continuous growth. Performance monitoring could also send signals to top management which part of their business  operations are failing or working below expectancy. This process plays an important part in the strategic planning initiative.

In order to successfully monitor company performance, the management should be able to gather corporate performance data swimmingly. 

Data Gathering 

Data gathering in general should start with KPI activation. This KPI activation consists of four different steps: meeting with the data custodians, securing the activation budget, designing the data gathering template, and communicating the template to the data custodians. KPI activation is a step that allows management to develop infrastructure for capturing and managing data.

After KPI activation is done, the next step is the ongoing data gathering process. This is where the management or the performance management team sends the KPI data gathering notification to the KPI custodians and receives the data relevant to performance monitoring. For this step, it is imperative for the performance management team to gathers and centralize the relevant data before checking the data quality.

After sending the KPI data gathering notification, the management or the performance management team could also send the KPI custodians a reminder via email to make sure the data custodians prepare the data needed.

Once the relevant data is gathered, the performance team should check the quality of the data before calculating the KPI results and analyzing the data. The quality of the data should be checked based on multiple dimensions. The main dimensions are Accuracy, Completeness, Consistency, Conformity, Timeliness, and Uniqueness. In reality, the performance management team may find the relevant data does not meet those requirements/quality. When the data does not meet a certain quality, it is preferred for the top management or the performance management team to clarify the data to the data custodians. 

Data analysis is a set of processes of examining, transforming, and modeling data to generate relevant business insights that can be used in the decision-making process. In analyzing KPI results, the performance team should use analytics.

The final step of data gathering is to generate a performance report. In this phase, data custodians, the report generator, and the strategy performance team are collectively responsible for compiling all performance results, business insights, and analysis in a certain format for the decision-makers.

In conclusion, a solid data gathering enables decision-makers to set the right company’s objectives for the next period. A solid data-gathering process will help the performance management team provide the performance report required by the top management faster,  making the top management adjust the company’s strategy and objectives properly. If you want to learn more about how you could establish a solid data gathering process, sign up for The KPI Institute’s Certified KPI Professional and Practitioner course.

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