Self-Serve Data Prep and ETL for Business Users!

Self-Serve Data Preparation and Self-Serve ETL!
Why is Self-Serve ETL and Self-Serve Data Prep Important? If your business spends a lot of time and money on the task of extracting, transforming and loading data (ETL) and preparing that data for analysis, you might want to consider the advantages of self-serve data ET. Self-Service ETL can and should be easy enough for business users so that your business can enjoy the benefits of advanced analytics without hiring a team of data scientists or IT professionals.
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ETL and Self-Serve Data Prep for ALL!

ETL Need Not Be Daunting!
Don’t Be Intimidated by ETL and Data Prep. It’s EASY! When you hear the term ‘Extract, Transform and Load’, does it make you want to run in the opposite direction? ETL, as it is called, refers to the process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.
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Can Business Users Leverage Self-Serve Data Prep?

What Is Self-Serve Data Preparation?
How About Giving Your Business Users the Power to Prepare Data for Analysis? Can Your Business Achieve Self-Serve Data Prep? Lots of my friends talk about the difficulty of preparing data for analysis and how long it takes to get IT or data scientists or analysts to take on the project, get the data prepared and run reports or perform analytics. Frankly, this problem is a puzzle to me!
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Self-Serve Data Preparation Doesn't Mean Traditional ETL is Dead!

Self-Serve Data Preparation Doesn't Mean Traditional ETL is Dead!
Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting. It offers high quality data, which otherwise resides in poorly structured heterogeneous, complicated data sources.
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