Since lean six sigma management requires firms to measure, track, and analyze data about their products, processes, and customers, effective data management is integral to limiting waste and meeting the goal of continuous improvement.
Overview of ELT
The ELT process is a three-stage data pipeline that consists of the following stages:
1. Extract
The first step is identifying and reading data from a source system stored as a database, files, archives, CRM, ERP, or any other form of useable data.
2. Load
The second stage is to load the data onto the target server.
3. Transfer
The third stage of the ELT process involves transferring data from the source server and converting it from its source format into the format required for analysis. Also known as data transformation, this step is based on rules about converting data for use and analysis in the target server. ELT is a data integration method that obtains data from a source server and moves it to an intermediate staging area or database. Data integrity checks and any business rules about data management take place in this intermediate area before transferring the data to the target server. Then, any data transformation required for analysis and use takes place on the target server.
Why Is ELT Important to Understand?
Data integration used to be for IT nerds. However, today it is a critical component of tracking your business processes and helping provide the services today’s customers expect to come with anything they purchase.
Helps Provide Insight Into Your Customers – Collecting then analyzing customer data enables the measurement and analysis required for lean six sigma management. Unless you can efficiently collect, transfer, and analyze data about your products and customers, you will not be able to identify problems and find solutions.
Streamlines the Management Process – By separating the loading and the data-transforming tasks, ELT reduces the interdependency of target and source servers. This independence simplifies project management because it eliminates many sources of delay or data interruption.
Reduces Cost of Ownership – ELT reduces overall costs because it doesn’t require as much upfront hardware. You limit hardware requirements by extracting only the necessary data from the source (usually an outside entity) and moving it to your server.
Three Benefits of ELT
Using ELT data integration has three main benefits:
1. Can Create Future-Proof Data Sets
Creating data sets that become unusable with time limits analysis and creates waste. Collecting raw data from various sources and transforming that data on the target server can prevent information from becoming stale over time.
2. Reduces Transit Time vs. ETL (Extract, Transform, Load)
Other data integration models (ETL) took longer for data to transit to the target server, which usually means lower cost and enables more real-time decision-making.
3. More Flexible Analysis
Since ELT stores the raw data on the target server, you have access to this information, unlike in the ETL model, where data is transformed before moving it to the target server. The raw data allows you to use analytics you didn’t envision when you loaded the data from the source.
Industry Example of ELT
Suppose a commercial software firm wants to be responsive to customer needs. However, customers have different desires about what features they want to be added to the product. How does the firm’s management team decide which additions to address first? Project managers would likely wish to look at which SaaS product the consumer has purchased, has the customer renewed their subscription and the potential benefits of making a feature change. This information resides on multiple databases, and the firm must integrate this data to help make decisions.
Three Best Practices When Thinking About ELT
The following three tips will help you efficiently use an ELT solution in business decision-making:
1. You Need Robust Data Security
Any ELT system involves frequent data transfers, which means your servers will be vulnerable to hackers. You must protect this data to prevent making decisions based on inaccurate or corrupt data, and you could also face liability for failing to protect customer information.
2. Users Must Understand SQL, Databases, and BI
Without a strong technical foundation in these data storage protocols, any ELT system will be prone to failure.
3. Use ELT When You Need “Big Data” For Analytics
ELT truly shines when firms need to use large amounts of data from many different sources for good business intelligence.
Frequently Asked Questions About ELT
Managers often ask the following questions about ELT data integration systems:
What is the difference between ELT and ETL?
ELT and ETL are data integration methods that collect data from a source server and transfer it to a target server. The critical difference between these two data management models is that ETL transforms the data before moving it, while ELT transforms the data on the target server.
When is an ELT solution not suitable for data management?
The biggest problem with any ELT system is security. Therefore, any system which requires high levels of data protection isn’t the best fit for an ELT approach.
Why is ELT a good fit for “Big Data” Analysis?
ELT is the best solution for handling large amounts of unstructured data because it requires less processor power in the target server. Furthermore, ELT doesn’t require long load times because it doesn’t transform the data in an intermediate storage location. Finally, because it has faster communication speeds, ELT can integrate data from many sources without a lot of lag time.
ELT Is A Essential Tool For Business Intelligence
Making data-driven decisions is the new buzzword in business for a reason. In the past, managers often made inefficient decisions based on “common sense” or “intuition” rather than information. Good ELT data management can help managers eliminate ineffective decision-making and provide a quantitative basis for their choices.
