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What are the common mistakes in identifying variables and how can you avoid them?

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In Business Intelligence (BI), identifying the right variables is crucial for accurate data analysis. However, common mistakes can lead to flawed insights and poor decision-making. Understanding these pitfalls and learning how to avoid them is key to leveraging BI effectively. By paying close attention to the data selection process and questioning the relevance of each variable, you can significantly improve the quality of your BI projects. Remember, the goal is to transform raw data into meaningful information that can drive strategic business decisions.

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1 Misguided Focus

When selecting variables for analysis, a common mistake is focusing on what's easy to measure rather than what's important. This can result in a misguided analysis that doesn't address the core business questions. To avoid this, prioritize variables based on their relevance to your objectives. Engage with stakeholders to understand their needs and ensure that the chosen variables directly contribute to answering the pivotal questions at hand.

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SAQUIB KHAN

Lead Gen Specialist @ ZI Systech | Data Scientist
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Avoid mistaking a discovered relationship between variables as a causal one. It is essential to understand that correlation does not imply causation, and further analysis is needed to establish causal relationships. Be cautious of including variables that are highly correlated with each other, as this can lead to multicollinearity issues. To avoid this, assess the independence of variables and exclude highly correlated ones to ensure the uniqueness of each variable's effect on the dependent variable. Avoid including variables that do not have a significant relationship with the dependent variable, as this can lead to overfitting. Ensure that all variables included in the analysis are relevant and contribute meaningfully to the model.

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2 Overlooking Context

Another frequent error is ignoring the context in which the data exists. Variables can have different meanings and implications depending on the situation. To prevent misinterpretation, always consider the broader context of your data. This includes understanding the industry standards, seasonal trends, and other external factors that may influence the data. By doing so, you'll ensure that your analysis reflects the real-world scenario more accurately.

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Edward H K Ng

Risk Advisory Director
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Variables like financial ratios can be very misleading. Most financial modelers are trained in Finance, Statistics, Research Methodology etc but may not have sufficient grasp of Accounting standards. It's legitimate under such standards to report negative Total Assets, Cash, Current Assets etc. This throws a spanner into models where lower values associate with positive inference eg Debt/Asset (negative TA means negative ratio). It's essential for modelers to understand the raw data before employing variables.

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Gustavo Goldacker

Financial Coordinator | Process Optimization and Strategic Management
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Realizando uma análise de tickets em nossa empresa, inicialmente, utilizamos apenas métricas básicas, como tempo de resolu??o e número de tickets. Ao revisar nossa estratégia, passamos a considerar o contexto mais amplo, incluindo fatores sazonais, como picos de demanda durante o ano e após lan?amentos de novas funcionalidades. Por exemplo, ao observar um aumento nos tickets após uma atualiza??o de software, investigamos mais a fundo para confirmar se havia uma rela??o de causa e efeito. Isso nos ajudou a evitar distor??es nos resultados e a obter insights mais precisos. Essa mudan?a nos permitiu entender melhor as tendências por trás dos tickets e tomar decis?es mais informadas.

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3 Data Duplication

Data duplication can skew your analysis and lead to incorrect conclusions. This happens when the same information is counted multiple times as separate variables. To avoid duplication, thoroughly review your datasets for overlapping information. Use data cleaning techniques to merge or eliminate redundant variables, ensuring that each one represents a unique aspect of the data you're examining.

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4 Ignoring Correlation

Neglecting the correlation between variables is a mistake that can obscure the true drivers behind the trends you're observing. Variables may be correlated due to causation or coincidence, and it's crucial to discern the difference. To avoid this pitfall, conduct correlation analysis and consider the causality between variables. This will help you identify which factors are genuinely influencing the outcomes and which are merely correlated.

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5 Variable Granularity

Choosing variables with inappropriate granularity can severely impact the insights you derive from your BI tools. Too granular, and you might miss out on broader trends; too broad, and you might overlook critical nuances. To maintain the right balance, match the granularity of your variables with the level of detail required for your analysis. This ensures that your findings are both comprehensive and relevant.

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6 Misinterpretation Risk

Lastly, there's always a risk of misinterpreting variables, especially when dealing with complex datasets. To minimize this risk, validate your assumptions with subject matter experts and use visualizations to aid in understanding the relationships within your data. Consistent validation and cross-referencing with other data sources can also help in ensuring that your interpretations are on the right track.

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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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What are the common mistakes in identifying variables and how can you avoid them?

1

2

3

4

5

6

7

1 Misguided Focus

2 Overlooking Context

3 Data Duplication

4 Ignoring Correlation

5 Variable Granularity

6 Misinterpretation Risk

7 Here’s what else to consider

Business Intelligence

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What are the common mistakes in identifying variables and how can you avoid them?

1

2

3

4

5

6

7

1 Misguided Focus

2 Overlooking Context

3 Data Duplication

4 Ignoring Correlation

5 Variable Granularity

6 Misinterpretation Risk

7 Here’s what else to consider

Business Intelligence

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