It’s nearly impossible to talk about the benefits of ETL (Extract, Transform, Load) data processing without first considering the alternatives. So let’s set the stage: you’re a SaaS provider, and you’ve got data in different formats coming in from a variety of sources—SQL Server, MySQL, PostGres—and business users who need to report off that data. Your choices are:
- To have users connect directly to these data sources and report off the raw data.
- To consolidate the data in one database, hand-coding programmable data objects like views and stored procedures to standardize the data formats, and then have users report off that.
- Hand-code an ETL process or program that extracts the source data, transforms it, and loads it into a data warehouse for reporting.
Option A is deeply inefficient, as the amount of server-side processing required to join and process all that data would mean major hits to performance. Additionally, business users would struggle to navigate and understand the raw transactional information and be forced to build all that elided processing into their business reports.
Option B could function as an interim solution but would ultimately be prone to operational instability. If the source data’s schema required any significant changes, an IT specialist or DBA would have to edit the underlying programmable objects manually, potentially introducing new issues or inconsistencies in the process. (There are also often limitations to using programmable data objects, assuming the reporting solution in question can accept that input stream at all.) Option C also necessitates that a specialist maintain the code, effectively creating a knowledge bottleneck.
ETL applications, however, streamline data integration and processing, making it possible for your team to prime data for reporting without being fluent in a host of programming and/or querying languages. And if you don’t have a database expert on your team who could hand-code a data warehouse, the choice between raw data and processed data is an easy one. Below are the five greatest benefits of going with an ETL tool instead of relying on unprocessed or hand-processed data.
Firstly, ETL solutions promise a considerable performance boost to BI users, as much of the processing those users would otherwise do server-side happens before the database is even queried for reporting.
Say, for example, that you make an HR application and are joining data submitted by application users (stored in SQL Server) with IRS data on those same employees. ETL would give you the opportunity to join those records, assign new primary keys, standardize formatting and naming conventions, filter out irrelevant records (or apply tenant columns), and even perform calculations, such as the difference between what employees earned this year and what they earned the previous year. Handling all that processing beforehand will make reports run significantly faster than they otherwise would.
2. Operational Resilience
Not only do you not have to rely on your IT team for all data processing, but you can also rest assured that your ETL tool’s built-in error-handling functionality will alert you to problems that need your attention. Solutions built for this purpose leave less room for human error, making your processing more efficient and less likely to cause data integrity issues downstream.
3. Data Quality
The Transform stage of ETL allows you to apply universal formatting standards to all data sets as you integrate them, paving the way for clean and efficient data models. It’s also an opportunity to make your data consistent semantically, which is key for helping teams with different nomenclature coordinate with one another.
The data can also be made more useful and approachable to stakeholders. How this might be done depends heavily on your business context, but let’s say for example that your SaaS application serves the medical community, which makes frequent use of HCPCS, CPT, and ICO codes. You might join code definition tables with patient record tables to make it easier for medical personnel to understand and sort through the codes.
4. Ease of Use
The majority of modern ETL applications permit users to interact with it through a GUI (graphical user interface), making it possible to design ETL processes with comparatively little programming knowledge. This increases the number of people upon which a company can rely to develop and maintain its data warehouse.
All of the above benefits culminate in higher ROI. With increased speed, efficiency, and data quality comes an ongoing return on your initial investment in an ETL tool and data warehousing. Fewer mistakes, fewer personnel, faster querying, and better results will not only help you accomplish more with less but also hone your competitive edge.
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