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Despite its effectiveness, data analysis is susceptible to the impact of minor errors and faults. This is evidenced by the observation of seven frequent issues in data analysis and the subsequent formulation of strategies to circumvent these issues. This problem is psychological in nature, and the larger the dataset is, the more errors one tends to commit; however, the knowledge or skill of the analyst is what makes the difference in the efficacy of these strategies.
1. The first of these issues involves the observation of data integrity.
What mistake was made
In the analysis of data, one fails to recognize the importance of its integrity. Messy, vague, incomplete, or erroneous data will only provide faulty insights.
How to avoid it
- Always examine the attributes of the data and ascertain whether it is clean before analysis.
- Examine the data to identify missing values, duplicates, and ambiguous data to ensure there is no data bias.
- Automate the data cleaning process or have a team specializing in data cleaning.
Key Takeaway
- You cannot have a sound analysis and faulty data foundation. Always ensure that the data integrity is present, else it will undermine all subsequent steps, and may falsify the expected analysis.
2. Overlooking the Right Variables
What mistake was made
Capitalizing on irrelevant data attributes in the construction of variables that pertain to a specific field is a mistake that individuals often commit. This is evident in the absence of the contextual background of the data itself, where one tends to fixate over certain data points, or attributes, and lose sight of the abstract.
How to avoid it
- You must have a comprehensive knowledge base that relates to the problem at hand, and is also relevant contextual data from which one is trying to derive a metric value.
- Use data from the relevant field and employ it judiciously in the analysis to avoid errors originating from irrelevant variables or attributes. This knowledge base is essential to making a good decision in your analysis.
- Try multiple data models until you find one with results that suit your needs.
Key takeaway
- Pick variables that align with your objectives so that your analysis measures correctly.
3. Assuming Correlation Implies Causation
What’s the problem?
Mistaking correlation as causation is a common trap when dealing with data.
How To Avoid It:
Always consider the variables considered and if they can or can’t logically correlate. Employ statistical techniques, such as regression analysis, to answer questions of causation rather than correlation alone. Avoid making assumptions and look for more data to support your claims.
Key takeaway
- Never assume correlation equals causation.
4. Failing to Validate Your Results
What’s the problem?
Failing to validate models is a common problem among analysts that can lead to results that are misleading.
How To Avoid It:
Employ validation methods that cue the user to select adaptive cross-validation for your data. Assessment of findings, when possible, should be conducted against data that is actual and sourced from the real world/or other previously conducted research. Avoid single data sources. Rather, for consistency, test your model against multiple data sets.
Key takeaway
- Results must be validated to establish reliability and accuracy.
5. Overfitting the model
What mistake is being made?
Overfitting occurs when your model is too complex and begins capturing too much of the noise in the data. Although this appears to produce great results during training, underfitting results in bad outcomes when the model is presented with new data.
How can it be avoided?
- Overfitting can be avoided by keeping the model simple, as well as using regularization techniques.
- Evaluating your model with out-of-sample data is also good practice when checking it for overfitting.
- It is also essential to your model’s performance to ensure that it is overfitting.
Key takeaway
- Make sure your model is not overly complex.
6. Ignoring outliers
What is the mistake being made?
Disregarded outliers can not only affect the results of your analysis, but can also lead to incorrect conclusions.
How can this be avoided?
- Refrain from ignoring outliers by applying certain statistical techniques such as the IQR (interquartile range), or Z-scores to detect outliers early.
- Depending on the purpose of your analysis, determine whether the outliers should be kept, transformed, or removed.
- Examine the impact of outliers on your results or model.
Key takeaway
- Make sure to manage outliers correctly so that your analysis remains valid.
7. Lack of Understanding of the Business Context
What is the mistake?
If you try to analyze the data without any background, it is like trying to complete a jigsaw puzzle without the picture on the box. If you don’t have an idea about the business problems that you are trying to solve with the analysis, you are more likely to miss the target.
What to do to avoid it?
- Try to understand the goals of the business and the objectives first.
- Engage with the stakeholders to understand the important goals that you want to focus on and what are you trying to accomplish.
- Realign your analysis with the business context and iterate based on feedback.
Key takeaway
- You have to understand the business context to make sure that your analysis is pertinent and will have a real impact.
Conclusion
If you avoid these data analysis mistakes, you will increase your chances of improving your data-driven decisions as well as increasing your outcomes. With the right focus on data quality, context, and findings validation, you will be able to get the complete range of your data.
Are you looking for someone to help you with your data analysis? Take a look at our data analytics services and see how we can help you make intelligent, data-driven decisions!
FAQs
What is data cleaning?
The process of data cleaning involves finding and fixing inaccuracies and inconsistencies in your data to make sure it is reliable and precise before you analyze it.
How do I handle outliers?
Statistical techniques can be employed to find outliers, then you can evaluate their effects and decide to exclude, modify, analyze, or leave them as they are.
Why is it important to validate my results?
Validation measures whether the results you achieved are accurate, consistent, and universally applicable, and the extent of the impacts you may face does not have to be a risk.
