An estimated 80 to 90 percent of the data in an enterprise is text. Sadly, this rich information is mostly neglected for analytical purposes.
Textual data is typically full of information, but also very complex to interpret computationally and statistically. Why?
Because textual data is both content and context. The same words and sentences can have very different meanings depending on the context.
Textual data is truly a goldmine, but how can we mine it without being digital superpowers like Google, Microsoft or Facebook?
To answer this question and many more relating to interpretation of textual data, I recently spoke to Bill Inmon.
Bill is the Founder, Chairman and CEO of Forest Rim Technology and author of more than 60 books on data warehousing. He is often described as the Father of Data Warehousing due to his pioneering efforts in making data and data technologies available to organisations across all industries and sizes.
In this episode of Leaders of Analytics, we discuss:

How Bill became the Father of Data Warehousing
The history of data warehousing and the most exciting developments in this space today
The typical challenges holding us back from extracting value from textual data
The concept of the “Textual ETL” and it’s benefits over other text data storage and analytics approaches
Why NLP is not the best approach for textual data analytics
The biggest opportunities for textual analytics today and in the future, and much more.

Connect with Bill:
Forest Rim Technnology: https://www.forestrimtech.com/
Bill on LinkedIn: https://www.linkedin.com/in/billinmon/

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