When businesses have heaps of data to deal with, it can be challenging to structure and manage every source to collect valuable insights and drive decision-making. With data coming from multiple sources, businesses need to take all this data and load it into a service like data warehousing that is easily accessible to all the users in the company.
Data warehouses have evolved from costly, cumbersome, and painful projects into a more efficient and agile process that minimizes the complexity of data pipelines. The standardized structure of data warehouses enables enterprises to build scalable workflows that can adapt to the ever-evolving demands of data – the capacity stretches when the requirements increase.
Data warehousing benefits revolve around one central premise – it solves the ongoing issue of analyzing separate data and converts it into actionable information that businesses can use. It also helps in processing massive amounts of complex data in an efficient way.
Mark Chamberlain, 
Vice President of Product Development at ADP, says, “When it comes to data warehousing, more and more companies are now storing data, and they want that data to be good, they want it to be usable and provide value, and they don’t want the data to provide any inconsistencies from a results perspective. A lot of companies are now putting business logic into their ETL process to be able to go clean it up. It will also help with redundancies and will also help businesses understand their data better.”
Listen to the Podcast: Interview with Mark Chamberlain Vice President, Product Development at ADP
Benefits of ETL and Data Warehousing
Let’s look at a few ways that businesses can leverage ETL and data warehousing to increase profitability, efficiency, and overall success.
Reporting and Analytics
A good data warehouse is where an organization’s data is stored in a way that lends itself well for reporting – it changes the structure in a form that is specifically suited for analytics and reporting.
Leading data warehouse tools perform ETL (Extract, Transform and Load) processes that involve extracting the transactional data from the database of applications, transforming it into a structure that is enhanced for reporting and then saving it in a data warehouse.
In the ETL process, a good data warehouse application can access and utilize the metadata from the transactional database, and it tells the user working with the data what the data is.
Quite often, it can be challenging to analyze data directly from the transactional database since it isn’t in a structure or language that can be understood by the person doing the analysis. Therefore the person preparing the data for reporting needs to have a good understanding of the data to produce meaningful reports. But, with good data warehouse software, this process becomes much easier.
Reporting from More than One Data Source
Even though organizations may have one particular data source that is the biggest and the most important one, like an ERP or CRM, they have other data sources as well, which are beneficial to include in reporting. They could be necessary line-of-business applications or flat files created according to company-specific formats for exchanging information.
A good data warehouse, with the help of the ETL process, can transform multiple data sources that are structured in multiple ways into a common data structure which allows them to be combined into one data set. The resulting unified data can then be leveraged to produce reports from multiple data sources. For instance, users can create sales and marketing analytics that utilize data from both CRM and ERP.
Also Read: Strategies to Minimize the Cost of Data Collection and Storage
Lesser Reporting Errors
ELT data warehouse automation software makes it easier and swifter to produce business reporting. With a data warehouse, the arduous task of accessing data from individual systems and preparing it for analysis is no longer needed.
Regularly refreshing data from all business systems in the data warehouse and setting reports to run on a schedule removes the time and effort organizations spend on creating reports. It also ensures that the resulting reports are trusted and accurate and that there is no human error involved.
Data warehousing benefits revolve around one central premise – it solves the ongoing issue of analyzing separate data and converts it into actionable information that businesses can use. It also helps in processing massive amounts of complex data in an efficient way.
Mark Chamberlain, Vice President of Product Development at ADP, says, “When it comes to data warehousing, more and more companies are now storing data, and they want that data to be good, they want it to be usable and provide value, and they don’t want the data to provide any inconsistencies from a results perspective. A lot of companies are now putting business logic into their ETL process to be able to go clean it up. It will also help with redundancies and will also help businesses understand their data better.”
Leading data warehouse tools perform ETL (Extract, Transform and Load) processes that involve extracting the transactional data from the database of applications and transforming it into a structure that is enhanced for reporting and then saves it in a data warehouse.
