There is a famous saying that goes by: Coincidently, this is also true for data in modern times. The information which we see in pretty reports and charts or is displayed to users via an application has actually experienced a long run of data processing and transformations. These transformations are a result of well-planned ETL pipelines and data management strategies. Originating from different touchpoints, data witnesses several alterations throughout its lifecycle, such as.

Many organizations are still under the impression that data governance can be achieved by implementing a data governance tool. This is false.

There have been many stories going around about why people hate the data warehouse. My favorite story is from my old friend, Biaggio McPhee. Read about it below.

In the competitive market of ETL solutions, platforms like Hevo, Airbyte, and Integrate.io are amongst the top contenders. While they all are ETL/ELT/Reverse ETL platforms, each has its unique set of features to offer. The best ETL tool for your business is the one that best fits in your modern data stack and is aligned with your unique requirements. So how do you decide which tool meets your business needs?

Irrespective of what it’s called, the market has talked about what amounts to data mesh for several years. The concept of decentralized data management that is driven by business domains helps support the need for business-focused data outcomes. It also helps place value on where the value of data projects should be - on business needs. Data driven organizations need to look at business domains as a way of organizing the various desired outcomes of analytics and data movement initiatives.

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes.

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.

Customer experience is one of the most critical concerns for any organization—but also one of the most challenging for companies to perform concrete improvements. When they better understand the customer experience, businesses can define a clear, actionable roadmap to optimize the customer journey. In turn, this will pay dividends in terms of greater employee productivity, lower costs, and higher profits.

Okay, I’ll admit, I am pretty biased when it comes to how people within organizations work together to ensure successful data projects. I have been involved in too many projects that failed to take into account the importance of collaboration across departments and functions. They were stuck on data and only the data.

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes. Data science is immature. This statement is not pejorative; it is simply a statement of historical fact. As such, it is not arguable.