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The Modern Data Warehouse: One Place for All Your Analytics Data (Free Webinar)



You’re invited to our latest live webinar!

Your data and data sources are changing rapidly. On one hand, you have your master data, defined by formal measures and dimensions. Master data is highly curated, and ready for operations and business intelligence. 

But increasingly, your business also collects high volumes of semi-structured data from multiple sources. This new data can contain new insights and improve data-driven decision making in the broadest sense. 


Are you using all of your data?

Your traditional data warehouse was built to handle all kinds of structured data. But it faces significant challenges in working with semi-structured data. It’s one of the reasons that so much of the data that organizations collect lies dormant. According to Forrester, up to 73 percent of business data goes unused for analytics.* 

A dual approach to data management, integrating both structured and semi-structured data, is the hallmark of a modern data management strategy. And a modern data strategy needs a modern, cloud-based data warehouse that can handle both structured and semi-structured inputs. 


Where do you start?

Join us for The Modern Data Warehouse: One Place for All Your Analytics Data, a free, one-hour webinar hosted by GigaOm Research. 


Wednesday, August 7, 2019

12pm CT 



  • GigaOm analyst Andrew Brust, an expert in Big Data and Analytics
  • Matillion’s own Dan D’Orazio, Solution Architect and author of our ebook Modernize Your Data Warehouse


Learn about:

  • Why high-volume data and master data are better together
  • How the data warehouse is more relevant than ever
  • Unique optimization points for loading semi-structured data and transforming structured data
  • ETL tools in a modern data architecture


Discover how to make the most of all your data to drive better business decisions, better serve your customers, and innovate with new products and ideas.

Register now on the GigaOm site. 

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*The Forrester Wave (TM): Big Data Hadoop Distributions, Q1 2016