Data Analyst 101 - Blog #UseCasesData Analyst 101 - Blog #UseCaseshttps://www.dataanalyst101.com/blogs/tag/usecasesThu, 25 Sep 2025 20:30:09 -0700http://zoho.com/sites/<![CDATA[W3Schools Statistics]]>https://www.dataanalyst101.com/blogs/post/w3schools-statistics

W3Schools.com started tracking some statistics from 2002. 


  • Browser popularity change as well as versions over a period of time
  • Operating System popularity change
  • Browser Display usage change
  • Mobile Devices popularity change
Please check out these statistics here

Recommendation Actions
  • You can use this dataset to implement a visualization as a practice


]]>Sat, 17 Oct 2020 13:17:45 -0700<![CDATA[Data Quality - Testing Tools]]>https://www.dataanalyst101.com/blogs/post/data-quality-testing-tools

Data Quality is a challenge that needs to be owned by Data Professionals. Given the volume of growing data we deal with and complexity of data transformations we implement in our jobs, we always need options to reduce tedious effort in testing and ensuring data quality. 


  • Data Validation needs to be thorough (100%) as spot checks are not sufficient. 
  • Data Quality is more important for business critical functions
  • Data Quality monitoring in production requires need to be automated as manual checks are not scalable. 


We recommend checking out following tools. Use the trial version to implement your scenarios before you recommend adoption for your organization

QuerySurge

Datagaps

If you want to list any other tool or options you have used on this page, please let us know

]]>Sat, 17 Oct 2020 09:57:45 -0700<![CDATA[PowerBI Training Material]]>https://www.dataanalyst101.com/blogs/post/powerbi-training-material

PowerBI from Microsoft provides Visualization features to convert data to action. 


PowerBI Training Links

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]]>Sat, 03 Oct 2020 13:05:47 -0700<![CDATA[Predicting Customer Churn]]>https://www.dataanalyst101.com/blogs/post/predicting-customer-churn

As a growing SaaS company Acme was focused a lot in growing customer base. Sales and Marketing took lot of time and assuming existing customers are happy with product was a reasonable thing to do.


However, Acme had usage analytics in place to monitor login activity every week. During one of these reviews the team noticed gradual drop in usage by the largest customer. Operations team reached out to Client/Account team to assess the situation. They learned that client users have started using an alternate product and yearly contract will not be renewed.


Analytics helped ACME predict customer churn and be prepared for the impact much before the event happened. This also presented an opportunity to recover customer.


Action:

Analytics may not deliver insights when they are built but regular monitoring will provide early warning signs to something that can be prevented. 


Note: This is real use case and name of the company changed for confidential purposes. 

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]]>Sat, 03 Oct 2020 09:33:22 -0700<![CDATA[Big Data of Human Events]]>https://www.dataanalyst101.com/blogs/post/Big-Data-of-Human-EventsIs Astrology/Horoscope a science? With increasing confidence in predictive algorithms and machine learning, will we think about it differently? Imagine ]]>

Is Astrology/Horoscope a science? With increasing confidence in predictive algorithms and machine learning, will we think about it differently?


Imagine a repository of Big Data that contains all human events along with their information.

  • Birth date, place of birth, time of birth, gender, life events both positive and negative captured for billions of people along with age of the individual when the event occurred
  • personality traits that are observable including preferences
  • Also add place of birth, date of birth of parents as parameters to include


Imagine enriching that data with planetary positions for each and every second

  • Time dimension of data which repeats every few years


Run algorithms to correlate all the above information, you may have enough sample size for every individual to see a pattern of events.


Currently we don’t have access to algorithms that were used to come up with predictions but only predictions with some limited parameters like date and time of birth. This is leading us to come up with general predictions which have higher statistical error?

It will be interesting to see what happens when big data predictions mature and someone does a research in this area.


Can this be extended to palmistry as well? Use visual data to correct above big data to see patterns?


We will continue to update this article with more references and please feel free to comment if you come across any relevant articles/research.


Related Articles:

Astrology Checklist

Astrology on Wikipedia



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