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What are the key steps to Data Validation?

What are the key steps to Data Validation?

What are the key steps to Data Validation?

First of all - What is Data Validation? 

Data validation provides the mechanism for checking the accuracy quality and authenticity of your data, typically performed prior to moving or migrating data.
Data validation can also be considered as a form of data cleansing, by assuring that your data is complete. Cleansing highlights blank or null values and assures that data unique and contains distinct values that are not duplicated.

Often, data validation is used as a part of processes such as ETL (Extract, Transform, and Load) where you move data from a source database to a target data warehouse so that you can combine and merge it with other data for analysis. Data validation’s key role is to help ensure that when you perform analysis, your results are as accurate as they can be. 

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What is Data Extraction?

What is Data Extraction?

What is Data Extraction?

Data extraction involves retrieval of data from many different sources. It is common for companies to extract data in order to process it further, migrate the data to a data repository such as a new CRM, data warehouse, data lake or to further analyse it.

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What is effective Data Preparation and why is it so important?

What is effective Data Preparation and why is it so important?

What is Data Preparation and why is it so important?

Data preparation is a critical process which involves gathering, cleansing, and organizing data so that it can be analysed.  Going through the preparation of data ensures your data is complete with no blanks or null values, contains distinct values that are not duplicated and that the range of values is consistent with expectations. This step is key because data may have missing values, or it may have inaccuracies and/or errors. This process often takes place as a preliminary step when moving data through an ETL (Extraction, Transformation, Loading) process or tool so that data can be loaded into a data warehouse in preparation for a data migration, for example.

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What are the key steps to take to achieve Data Validation? How can you overcome common challenges? lnkd.in/dvSMVe2 #iData #datavalidation #analytics #quality
According to a recent #IBM estimate the U.S. economy loses $3.1 trillion a year because of poor data quality. How can loss of revenue through poor quality data be avoided? bit.ly/iDataqualityTS #iData #GlobalDataStrategy

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