Today, Big Data is all the rage. Everybody is being sold on the great
strategic value of collecting huge amounts of data. And everybody talks
about the wonderful business advantages that harnessing Big Data will
But notice this – no one tells you exactly how to turn all that
unstructured data that comes with Big Data into actionable analytics.
Sometimes they say you need to get a “data scientist”. Or that you can
write thousands of lines of custom code in Map Reduce. Usually they
ignore the subject altogether.
Why? Because data scientists and thousands of lines of custom code in
Map Reduce are simply not the answer.
We know how to turn all that unstructured data into actionable
analytics. We are working with Bill Inmon, the Father of Data
Warehousing, who is leading the way with a technology called “textual
disambiguation”. With textual disambiguation you can start to make
sense of your unstructured data.
Textual ETL will give you a decided edge over your competitors by
accessing the unstructured textual data in your entire organisation
quickly and easily.
This technology is available today for improving your bottom line,
learning more about your customers and employees, and for more
Textual ETL is the technology that allows an organisation to read
unstructured data and text in any format and in any language and to
convert the text to a standard relational data base (DB2, Oracle,
Teradata, SQL Server et al) where the text is in a useful meaningful
format. Textual ETL does not put text into a blob. Once text is placed in
a blob it is essentially not useful. Instead Textual ETL creates a textual
data base in a relational format that is fit for analytical processing.
The applications for this cutting edge technology are truly endless.
Here are just a few that we have identified:
Loan portfolio analysis
Pre-emptive litigation preparation
Social Media sentiment analysis
Protected by nine patents that have been filed, and based on the
research and design of Bill Inmon of Forest Rim Technology, Textual ETL
includes the ability to:
Read any source of text – email, Hadoop, .doc. data base, etc.
Handle text in any common language (English, Spanish, French,
German, et al)
Interpret both “standard” text (the language your English teacher
taught you) and shorthand (the notes your doctor makes when you
have a checkup)
Transform the text into any standard relational data base
management system (Oracle, DB2, Teradata, SQL Server, et al)
Scale up to handle large amounts of data
Address the issues of multiple terminology for the same term
Apply taxonomies and ontologies to raw text
Recognize and manage logical sub divisions of text as it resides in
Manage the entire range of repetitive, non repetitive and mildly
Perform both document fracturing and named value processing
Order multiple forms of text – standard text, doctor’s notes,
comments, shorthand, tweets, and so forth
Visually display the clustering of words and terms
Back reference a document
Accomplish homographic resolution
Locate and recognize patterns of text
And many other features too numerous to mention.
Textual ETL reads electronic data from any source. Some of the typical
Standard Microsoft formats
Hadoop and Big Data, and more.
To find our more, call us today or email at
In any given organisation,
around 80% of all the
information is unstructured.
Textual ETL makes that
information accessible via any