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The amount of big data that enterprises churn out is simply staggering. All this information is worthless unless organizations unlock its true value for analytics. This is where ETL proves useful. Traditional ETL (extract, transform, and load) remains the most popular method for moving data from point A to point Z. It takes disparate data sets from multiple sources, transforming that data to the correct format and loading it into a final destination like a data warehouse.

Leveraging the Internet of Things (IoT) allows you to improve processes and take your business in new directions. But it requires you to live on the edge. That’s where you find the ability to empower IoT devices to respond to events in real time by capturing and analyzing the relevant data.

Analysts have found that roughly 50% or more enterprise resource planning (ERP) projects fail, and approximately half of all projects are considered challenging.

ETL and ELT refer to two patterns of data storage architecture within your data pipelines. The letters in both acronyms stand for: So both ETL (extract, transform, load) and ELT (extract, load, transform) processes help you collect data, transform it into a usable form and save it to permanent storage, where it can be accessed by data scientists and analysts to extract insights from the data. What is the difference?

Data is the new oil. It’s a phrase we’ve heard a lot in recent years, and it’s not hard to understand why. We’re generating more data every day than ever before, and companies are scrambling to find ways to store that information without running out of space.

The scientific method is a proven route to successful, tested and verified improvement. Here’s how to combine it with BI.