Importance of ETL and Business Intelligence (BI)

In order to drive a data-driven business, you need to first understand how to gather and act on data you trust. The first step of any data process is called Extract, Transform, Load (ETL). This is the heart of data transformation, providing a process to combine and transform data from disparate sources. This allows businesses to make better decisions faster.

Traditional methods of data transformation are no longer adequate in today’s market. Data is coming in at an ever-increasing rate and in a variety of formats. Data has reached volumes where none of it can be processed manually. ETL provides the technical framework and necessary process to quickly and easily move this data to where it needs to be. It also allows for the transformation of the data so that it is in the correct format for the downstream applications.

Data is increasingly becoming interconnected with each other through your company’s various systems. This is valuable because it allows you to share impactful data across business units, but it also adds layers of complexity to your data. This interconnectedness means your ETL processes need to focus on transforming all data, no matter the source, into a normalized format that can be combined with data from other sources, giving you a comprehensive view into many different areas of your business that can overlap. By standardizing on common platforms and data models, companies can unlock a new level of insights and intelligence.

ETL is playing a leading role in this transformation by providing the necessary capabilities for data ingestion, cleansing, transformation, and loading into target systems. It enables businesses to quickly get value from their data by helping to:

• Filter out noise and redundancy in data from multiple sources
• Create a unified view of data from different systems
• Integrate data from legacy applications

ETL bridges operational, transactional data sources with big data for analytics or business intelligence. In this article, we will take a closer look at how ETL is being used to power digital transformations and the benefits that it brings.

What is ETL?

Extract, Transform, and Load (ETL) is a process for extracting data from various sources, cleaning and transforming it into a unified format, and loading it into a target system. The ETL process can move data between different systems or load data into a data warehouse or big data platform.

The three main steps in the ETL process are:

• Extract: Extracting data from source systems into a staging area.
• Transform: Cleaning and transforming the data into a unified format.
• Load: Loading the data into a target system.

Together, these three steps make up the core of an Extract-Transform-and-Load (ETL) solution.

The ETL process enables organizations to extract, cleanse, and load data across different systems. It uses a staging area as a “pre-production” environment where data is transformed and prepared for loading into a target system.

What is ETL in Business Intelligence?

ETL serves as the foundational framework for business intelligence (BI) by extracting raw data from diverse databases, transforming it into a standardized and coherent structure, and loading into a data warehouse or data tool that allows your team to then visualize and interpret the data. 

As you build out your ETL processes, keeping actionable insights for BI in mind ensures your ETL processes are transforming the data into usable information further down the decision-making process. As your ETL processes are built to support BI, your company can harness the full power of your data, uncovering trends, patterns, and relationships that drive strategic decision-making and ultimately contribute to the overall success of the organization.

The Importance of ETL in Business Intelligence

ETL enables companies to get more value out of the data assets they already have. It helps them integrate existing systems, enable analytics, and increase performance management capabilities by standardizing on common platforms and data models. By harnessing the power of ETL and business intelligence, businesses can:

Filter out noise and redundancy in data from multiple sources

One of the main benefits of ETL is that it helps companies to filter out noise and redundancy in data from multiple sources. This can be done by extracting data from source systems into a staging area to be cleaned and transformed.

Take, for example, a fast-food restaurant chain that wants to use historical data to perform analysis on foot traffic patterns. There are multiple data sources available for this purpose:

• POS transactions: Register transactions are the main source of information about customer behavior within a store. But these records are limited in what they can tell you–they don’t include data about customers who used a credit or debit card, for example.
• Customer loyalty cards: Loyalty cards can provide valuable insights into customer behavior, such as purchase frequency and average spending.
• Survey data: A survey of customers can give you information about why they visit the store, what they buy, and how often they visit.

The restaurant chain can extract data from all of these sources and cleanse and transform it into a unified format by using ETL. This will help them get a more complete view of customer behavior and make better business decisions.

Create a unified view of data from different systems

Another benefit of using modern data transformation (ETL) tools is that it helps to create a unified view of data from different systems. This can be done by extracting data from source systems into a staging area, where it can be transformed and loaded into the target system.

For example, let’s say that a business wants to create a view of customer data that combines information from multiple sources. These sources may include transactional systems (such as ERP software), marketing automation systems, and other internal and external sources. By using ETL, the company can get the unified view of customer data they need while still allowing access to source data systems.

Enable analytics and business intelligence

ETL enables business users to more easily perform their own analytics on the data they have access to, whether or not it is stored in a centralized location. This is because ETL provides a more unified view of data from multiple systems and sources that can be transformed into a format that is easier for people to understand.

In addition, ETL is used to support business intelligence (BI) visualizations. A leading BI application like Domo allows users to connect, transform, and visualize data, and so much more, all within a single product. Using a BI application that lets you do all of this in one system allows businesses to get more value from their data and make better decisions based on that data.

Enable performance management

ETL can also be used to help businesses track and improve the performance of their business processes. This is done by standardizing a common data model across different systems.

By using ETL, businesses can create benchmark metrics within their datasets that can be used to compare actual performance against desired performance. This helps to identify areas where performance can be improved.

Integrating ETL into your business

To maximize the benefits of using ETL, it’s essential to find a modern BI tool that allows you to standardize on a common platform and data model for all of your internal and external systems.

Many BI tools do not include robust ETL tools, but ETL within your BI tool is critical for modern businesses. ETL tools enable businesses to benefit from their data in a number of ways: it can be used to integrate data from different systems into a single view, help users perform analytics and BI on the data that’s available to them, and enable businesses to track and improve performance.

By using ETL, businesses can get the most value from their data and make better decisions based on that data.

Tools for ETL in business intelligence

There are any number of ways your company can incorporate ETL into your organization. While building an ETL pipeline is always something you can consider, there are a number of tools already available that will help you manage the process efficiently and get you up and running with your data quickly. Here are some effective ETL tools your company can consider to help you build an ETL process:  

1. Domo

Domo is a cloud-based business intelligence and data integration platform that streamlines ETL processes to deliver real-time insights and data visualization, offering a user-friendly interface for both novices and technical users. Domo’s drag-and-drop Magic ETL tool works for users without advanced SQL expertise. But they also have advanced features that allow more technical users to do advanced data transformation. 

Domo’s key strengths lie in its pre-built data connectors, simplifying the ETL process, and its support for the entire data lifecycle, from connection to sharing insights.

Because Domo provides a comprehensive end-to-end business intelligence solution, its extensive capabilities may exceed the needs of companies solely focused on ETL.

2. Apache NiFi 

Apache NiFi is an open-source data integration tool facilitating automated data flows between systems. It emphasizes secure data governance, offering routing, transformation, and connectivity to diverse data sources, supporting various formats. While free, scalable, and extensible, it requires some technical expertise for setup and configuration, and has limited built-in data transformation capabilities.

3. Talend

Talend, a Qlik company, is an end-to-end data platform known for its comprehensive suite in data integration, transformation, and quality. Users need to build custom data pipelines for each source, but once created, offer flexible and powerful data utilization. Key features include a wide range of connection abilities, robust data transformation capabilities, and a user-friendly graphical interface. Pros include strong community support, a rich feature set, and compatibility with big data and cloud integration. Some advanced features may require a paid version, and managing complex workflows can be challenging.

4. Alteryx

Alteryx simplifies data blending and analysis through a user-friendly drag-and-drop interface. It offers a robust suite for data manipulation, predictive analytics, and seamless integration with popular visualization tools. While known for its accessibility and thriving user community, there’s a learning curve for advanced features, and some functionalities may require a premium version.

5. Informatica

Informatica, an enterprise-grade ETL tool, excels in data integration, quality, and governance, earning Gartner’s recognition. Key features include a comprehensive suite supporting cloud and on-premises deployments. Pros encompass enterprise-grade capabilities, robust data quality features, cloud integration, and scalability. However, enterprise-level tools entail a higher cost, and the product may be complex for smaller organizations.

6. Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL Server Integration Services is an ETL tool integrated with SQL Server, focusing on data integration and transformation. Key features include tight integration with the Microsoft ecosystem, a visual design interface, and support for various data sources. Pros include inclusion with SQL Server, user-friendliness for Microsoft users, and robust data transformation capabilities. However, it is Windows-centric, and licensing for SQL Server may be required.

No matter the tool you decide to use, having robust ETL processes in place ensures your company can build and act on your data.