White Paper

To keep reading or download the pdf

Fill out the Form


Read Time:

10 min.


Prepared for:


Font Size:


Font Weight:

Data Warehouses Meet Data Lakes

Lakehouses Evolve to Support Many Use Cases

The Value of Data Warehouses and Data Lakes

For decades, organizations have recognized the need to perform analyses that draw upon information from various parts of an organization. Product profitability analyses require production costs, selling costs and customer service costs. Financial plans require sales information, operational information, marketing information and workforce information. Bringing these diverse sources of information together makes it easier to perform rich analyses on consistent sets of information. An overwhelming majority (91%) of organizations in our research report that analytics have improved their activities and processes. Because of the clear benefits, data warehouses have long been a foundational component of enterprise information architectures. As the collection and storage of big data has become standard for many organizations, the concept of consolidating data into a central repository has been extended to include the creation of data lakes.

Data lakes, like data warehouses, have many benefits. Our research shows that the most common benefit organizations report from their data lake is that it enables them to achieve a competitive advantage. They also report improving customer experiences and an improved bottom line due to increased sales and lower costs. Organizations further report that data lakes help them respond faster to opportunities and threats in the market. A primary reason for these benefits is that the detailed information available in a data lake enables analyses that wouldn’t otherwise be possible. For example, many predictive analyses require detailed data and cannot be performed accurately on the aggregated data that is typically available in data warehouses. One global technology and media company with millions of customers collects raw telemetry data from video and voice applications in their data lake. Performing analysis on this data using artificial intelligence and machine learning (AI/ML) techniques has helped them create an award-winning personalized viewer experience.



Fill out the form to continue reading