Extract, transform, and load is referred to as ETL. This process automates the extraction of data from various sources and transforms it so that it can be entered into the required database system. The final step is to load this data into other tools or systems that need it. Without the hassle of building and maintaining ETL scripts, the Stitch LinkedIn Ads integration can LinkedIn Ads ETL data to your warehouse, providing you access to raw customer data. In this article, we’ll discuss some lesser-known benefits of ETL programming and why you should consider implementing it in your organization.
Data Quality is Important
Data quality is important because it determines the effectiveness of the processes that come after it. For example, extracting data from the wrong source will be useless in a certain context. While the importance of data quality is widely accepted, many companies still don’t implement proper data quality processes. They extract data from different sources, transform it, and load it into their database. But, they don’t have a single system responsible for data quality. They don’t even have a data quality process. And, as a result, they lose a lot of time and money.
Data Freshness is Crucial
Data freshness is crucial because you can’t accept stale data in a business environment ever. You can’t rely on data that’s been in the system for months and even years. So, if you’re using an extract and load tool, ensure you don’t wait a long time to load it. You will benefit from this in at least two ways. First, you can assess the freshness of the data you’re loading. If you’re getting data from a few months ago, you know that you can’t actually use it for business purposes. And that’s a huge problem.
Automation Helps You Maintain Accuracy
Automation is another important benefit of ETL. As we said before, a single system can extract, transform, and load data into the database. Now, the data can’t be transformed manually. It has to be automated. And simple automation can help you maintain accuracy in the next steps. Let’s say you have a system that sends emails to various recipients whenever a certain event happens. You can use the information from these emails to create a new record in your database. If you manually enter the information from these emails into your database, you’ll have a high probability of making a mistake. (And we all know how bad that is, right?) You’ll likely enter the data with a zero or a one instead of the actual decimal number. But, with automated data entry, you can be 100% sure that you enter the data correctly. And, if you notice that you made a mistake, you can easily rectify it.
Standardization is required for Operational Efficiency
Standardization is another important aspect of data quality. You should have standards for the data you’re extracting and transforming. Then, you can restore the data if it has an issue. If you’re extracting data from various systems, you can standardize the information according to the requirements of your organization. For example, you can have standards for the data format. The format can be standard, i.e., The data must follow a specific format. You can also standardize the format according to your standards. This reduces manual work, improves data accuracy, and creates consistency in your organization. This will ultimately help you save time, money, and resources.
Data-lineage and impact analysis
Right-clicking on a number in a report would allow us to see exactly how it was arrived at, where the data was kept in the data warehouse, how it was transformed when the data was most recently refreshed, and from what source system(s) the numbers were pulled. Impact analysis is the opposite of lineage: if a structural change is required, we would like to look at a table or column in the source system and determine which ETL operations, tables, cubes, and user reports might be impacted. Hand-coded systems that depend on other vendors could conform to the lack of ETL standards.
Performance
It might surprise you that performance is listed as the last benefit of the ETL tools. You can create a high-performance data warehouse whether or not you employ an ETL solution. Whether you use an ETL tool or not, it is still feasible to create a data warehouse that is a complete failure. The question of whether an exceptional hand-coded data warehouse performs better than an excellent tool-based data warehouse has never been tested. Still, we think the answer is that it depends on the circumstances. However, an ETL platform’s structure makes it simpler for a (novice) ETL developer to create a high-quality system. Additionally, many ETL systems include innovations that improve performance, like Massive Parallel Processing, Symmetric Multi-Processing, and Cluster Awareness.
Compliance Drive is Required Once in a while too.
Data quality is important. You can’t accept bad data in the business environment. And in many cases, data quality issues are the reason for fines and penalties. For example, you’re using software that extracts data from your system. Now, the data in that system is really bad. You get fined for using that software. But, the data quality issues didn’t happen because your organization didn’t have a data quality process. The data quality issues happened because you have poor data quality. And the only way to fix those issues is to have better data quality. That’s why you should have a data quality process in your organization. You need to follow standards, process data, and check data. And, if there’s any issue with the data, you should rectify it.
Summing up
We’ve seen how data quality is important, how fresh data is crucial, and how automation helps you maintain accuracy. But how do you implement these benefits in your organization? First, you need to understand the importance of data quality. Once you get that right, you’re good to go. But don’t wait for someone to tell you that data quality is important. Implement data quality processes in your organization with the help of saras. It will help you save time, money, and resources.