The Problem with Analytics

Oct 13, 2025 by wbrum.org 18 views

Introduction

In today's data-driven world, analytics play a crucial role in making informed decisions. However, when analytics are incorrect, it can lead to misguided decisions, wasted resources, and a significant impact on business outcomes. In this article, we will delve into the world of analytics and explore the reasons behind incorrect analytics. We will also examine a specific query that highlights the issue of incorrect analytics and provide a solution to rectify the problem.

The Problem with Analytics

Analytics are only as good as the data they are based on. If the data is incorrect, incomplete, or biased, the analytics will be too. There are several reasons why analytics can be incorrect, including:

Data quality issues: Poor data quality can lead to incorrect analytics. This can be due to errors in data collection, data entry, or data processing.
Sampling bias: Sampling bias occurs when the sample used to collect data is not representative of the population. This can lead to incorrect analytics and conclusions.
Modeling errors: Modeling errors can occur when the model used to analyze data is flawed or incomplete. This can lead to incorrect analytics and conclusions.
Lack of context: Analytics can be incorrect if they are not taken in context. For example, a company may have a high sales revenue, but if the cost of goods sold is also high, the profit margin may be low.

The Query that Exposed the Issue

The query below is used to show the usage of aliases in a database:

select
	a.id_alias,
	count(a.id_alias) as usage,
	an.name
from 
	alias_usage	a
	inner join alias_name an on a.id_alias = an.id_alias
where strftime('%Y', time_stamp) = '2025'
group by
	a.id_alias
order by usage desc;

This query joins the alias_usage table with the alias_name table on the id_alias column. It then filters the results to only include data from the year 2025 and groups the results by id_alias. The count function is used to count the number of times each alias is used.

The Issue with the Query

When running this query, the results showed different usage counts for the same alias. This was unexpected, as the query was designed to show the usage of each alias. Further investigation revealed that the issue was due to the way the query was written.

The Solution

To fix the issue, the query needed to be modified to correctly count the usage of each alias. The modified query is as follows:

select
	a.id_alias,
	count(*) as usage,
	an.name
from 
	alias_usage	a
	inner join alias_name an on a.id_alias = an.id_alias
where strftime('%Y', time_stamp) = '2025'
group by
	a.id_alias
order by usage desc;

The only change made to the query was replacing count(a.id_alias) with count(*). This tells the database to count the number of rows in each group, rather than counting the number of times each alias is used.

Conclusion

Analytics are only as good as the data they are based on. If the data is incorrect, incomplete, or biased, the analytics will be too. In this article, we explored the reasons behind incorrect analytics and examined a specific query that highlighted the issue. We also provided a solution to rectify the problem. By understanding the importance of data quality and taking steps to ensure that data is accurate and complete, we can ensure that our analytics are correct and provide valuable insights into our business.

Best Practices for Ensuring Correct Analytics

To ensure that your analytics are correct, follow these best practices:

Verify data quality: Ensure that the data used for analytics is accurate, complete, and unbiased.
Use robust models: Use models that are robust and can handle errors and outliers.
Take context into account: Take into account the context of the data when analyzing it.
Test and validate: Test and validate your analytics to ensure that they are correct.
Continuously monitor and improve: Continuously monitor and improve your analytics to ensure that they remain accurate and relevant.

By following these best practices, you can ensure that your analytics are correct and provide valuable insights into your business.

Common Issues with Analytics

Some common issues with analytics include:

Data quality issues: Poor data quality can lead to incorrect analytics.
Sampling bias: Sampling bias occurs when the sample used to collect data is not representative of the population.
Modeling errors: Modeling errors can occur when the model used to analyze data is flawed or incomplete.
Lack of context: Analytics can be incorrect if they are not taken in context.

How to Identify and Fix Common Issues with Analytics

To identify and fix common issues with analytics, follow these steps:

Verify data quality: Ensure that the data used for analytics is accurate, complete, and unbiased.
Check for sampling bias: Check if the sample used to collect data is representative of the population.
Review modeling errors: Review the model used to analyze data to ensure that it is robust and can handle errors and outliers.
Take context into account: Take into account the context of the data when analyzing it.

By following these steps, you can identify and fix common issues with analytics and ensure that your analytics are correct and provide valuable insights into your business.

Conclusion

Introduction

In our previous article, we explored the reasons behind incorrect analytics and examined a specific query that highlighted the issue. We also provided a solution to rectify the problem. In this article, we will answer some frequently asked questions (FAQs) related to analytics and provide additional insights into ensuring that your analytics are correct.

Q: What are the most common reasons for incorrect analytics?

A: The most common reasons for incorrect analytics include:

Data quality issues: Poor data quality can lead to incorrect analytics.
Sampling bias: Sampling bias occurs when the sample used to collect data is not representative of the population.
Modeling errors: Modeling errors can occur when the model used to analyze data is flawed or incomplete.
Lack of context: Analytics can be incorrect if they are not taken in context.

Q: How can I ensure that my data is accurate and complete?

A: To ensure that your data is accurate and complete, follow these best practices:

Verify data quality: Ensure that the data used for analytics is accurate, complete, and unbiased.
Use robust models: Use models that are robust and can handle errors and outliers.
Take context into account: Take into account the context of the data when analyzing it.
Test and validate: Test and validate your analytics to ensure that they are correct.
Continuously monitor and improve: Continuously monitor and improve your analytics to ensure that they remain accurate and relevant.

Q: What are some common issues with sampling bias?

A: Some common issues with sampling bias include:

Non-representative sample: The sample used to collect data is not representative of the population.
Biased sampling: The sample used to collect data is biased towards a particular group or characteristic.
Insufficient sample size: The sample size is too small to provide accurate results.

Q: How can I identify and fix sampling bias?

A: To identify and fix sampling bias, follow these steps:

Verify the sample: Verify that the sample used to collect data is representative of the population.
Check for bias: Check if the sample used to collect data is biased towards a particular group or characteristic.
Increase sample size: Increase the sample size to provide more accurate results.
Use weighting: Use weighting to adjust the sample to reflect the population.

Q: What are some common issues with modeling errors?

A: Some common issues with modeling errors include:

Overfitting: The model is too complex and fits the noise in the data rather than the underlying pattern.
Underfitting: The model is too simple and fails to capture the underlying pattern in the data.
Model selection bias: The model selection process is biased towards a particular model or technique.

Q: How can I identify and fix modeling errors?

A: To identify and fix modeling errors, follow these steps:

Verify the model: Verify that the model used to analyze data is robust and can handle errors and outliers.
Check for overfitting: Check if the model is too complex and fits the noise in the data rather than the underlying pattern.
Check for underfitting: Check if the model is too simple and fails to capture the underlying pattern in the data.
Use cross-validation: Use cross-validation to evaluate the model's performance on unseen data.

Q: What are some best practices for ensuring that my analytics are correct?

A: Some best practices for ensuring that your analytics are correct include:

Verify data quality: Ensure that the data used for analytics is accurate, complete, and unbiased.
Use robust models: Use models that are robust and can handle errors and outliers.
Take context into account: Take into account the context of the data when analyzing it.
Test and validate: Test and validate your analytics to ensure that they are correct.
Continuously monitor and improve: Continuously monitor and improve your analytics to ensure that they remain accurate and relevant.

Conclusion

In this article, we answered some frequently asked questions (FAQs) related to analytics and provided additional insights into ensuring that your analytics are correct. By following the best practices outlined in this article, you can ensure that your analytics are accurate and provide valuable insights into your business.