Good business managers don’t make internal and external business decisions out of thin air. More often than not, these business strategies and tactics are based on empirical and numerical data. This is where business intelligence or BI comes in.
Simply defined, business intelligence is a group of technologies, architectures, processes, methodologies and theories that are aimed to cull, interpret and convert business data into relevant and useful information. This information is used for an array of business purposes such as identifying new opportunities, crafting of new business policies and creating strategies for business sustainability.
The Basics of ETL
One of the quintessential and vital tools that are vital to the success of your business intelligence projects is ETL, short for extract-transform-load. Basically, ETL is a software that allows businesses to consolidate disparate data and transfer it from one source to another application or from different sources to a single data warehouse for interpretation and analysis.
As its name suggests, an ETL software involves a three-pronged process:
1. Extract. The software pinpoints the relevant data sets based on a query and extracts the information from different internal or external, and structured or unstructured sources.
2. Transform. This step involves preparing and modifying the extracted data for loading to the designated data warehouse or data warehouses. The level of transformation the extracted data needs vary. There are data that needs little to no transformation while there are those that have to be cleaned, restructured and organized. The most common types of data transformation include:
3. Loading. A number of experts agree that this is the least complex step in the ETL process given that the extraction and transformation of data were completed without a glitch. However, depending on the size of your datasets a partitioning strategy may need to be implemented. Based on the various loading options available, data loading may overwrite current data, append the existing data with the new data or merge information.
Key Benefits of ETL in Business Intelligence Projects
The success of your business intelligence project is highly dependent on the quality and integrity of data you have and the efficiency of how you collect, manipulate and consolidate your business information. This is where the benefits of ETL tools primarily lie.
ETL tools are especially advantageous in situations wherein you need to integrate data from different source systems that are usually in different formats. Since you can program an automated data processing schedule, ETL is beneficial when you need to process data repeatedly such as on a real time, hourly or daily basis.
Below are the more specific benefits of using ETL tools in your business intelligence projects:
1. Collect and integrate data from different locations. If you’re a company with operations spanning different geographical locations, ETL can be used to consolidate data into a central data warehouse for easy aggregation. One of the best examples to demonstrate this benefit is Motorola. The company used ETL to mine data from 30 geographically disparate systems and load the information into a single supply chain management data warehouse. This enabled the technology company to see its total procurement expenses without difficulty. Imagine if this data in a multitude of different formats had to be aggregated manually. It could have easily taken hundreds of man hours to complete.
2. Sharing of business information across different business functions. Another basic function that ETL tools play in your business intelligence projects is sharing of information across your different operational units. For example, your CRM system will define your customers using specific parameters. What if your accounting department needs this data to improve how it handles billing? Chances are, different units in your business will require a different data format. With ETL, extracting this information, converting it to a new format and loading it to the new data warehouse can be done seamlessly and with minimal room for error.
3. Ease of migrating, cleansing and warehousing bulk data for more comprehensive business intelligence projects. While it is common practice in business intelligence to choose data samples within bigger data populations, logic still dictates that the more information you have, the more informed and educated your business decisions are. ETL’s capability to extract, cleanse and write data with ease will allow you to use as much data as you can with ease.
Information is prerequisite to intelligence. Transitively speaking, the amount, quality and integrity of business information you get is directly proportional to the success of your business intelligence projects. There are only a few tools that can rival the efficacy of ETL software in terms of data processing for business intelligence. Review your business intelligence needs and identify gaps that ETL tools can bridge.