Data analytics mistakes

Data analysis is a rapidly evolving field. As businesses rely more on data to make informed decisions, data analysts become more important. For a Data Analysis Company, contact https://shepper

Even if you are a seasoned analyst there will be challenges. There is a mistake around every corner. There are some common potential pitfalls:

Data and metrics misunderstood

The misunderstanding may take many forms. For example, it can be a result of unclear variables, an inability to align data objects around them, or not knowing from where the data comes.

Different teams in an organisation might have different definitions for churn. A data analyst creates a model to predict the likelihood that customers will not make a purchase in 90 days. If, for example, the business team defines churn as not making a purchase in 180 days, then these predictions will be useless.

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Data collection and validation is poor

Data integrity and quality are the foundations of any data analysis. Data collection methods that are insufficient or not reliable can lead to inaccurate conclusions and insights. Insufficient data cleaning, lack of validation techniques and inconsistent data are all common problems. If you don’t check your data for outliers or improper formatting, it can ruin your analysis.

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Contextualisation is not taken into account

Context is essential for extracting meaningful insights and assessing their significance. You can’t draw conclusions without understanding the context of the data and the scope. Imagine, for example, that you calculated the average annual income of each age group among your customers. You failed to take into account factors such as educational background or employment. A lower average income may misrepresent the actual financial situation of a certain age group. Maybe the group with lower incomes has a larger proportion of students. Ignoring context may lead to incorrect conclusions and, ultimately, misguided decisions.

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