README

README.md

@@ -14,11 +14,16 @@ _Are your data meeting your expectations?_
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-Data Expectations is a Python library which takes a delarative approach to asserting qualities of your datasets. Instead of tests like `is_sorted` to determine if a column is ordered, the expectation is `column_values_are_increasing`. Most of the time you don't need to know _how_ it got like that, you are only interested _what_ the data looks like.
+Data Expectations is a Python library which takes a delarative approach to asserting qualities of your datasets. Instead of tests like `is_sorted` to determine if a column is ordered, the expectation is `column_values_are_increasing`. Most of the time you don't need to know _how_ it got like that, you are only interested _what_ the data looks like now.
 
-Expectations can be used alongside, or in place of a schema validator, however Expectations is intended to perform validation of the data in a dataset, not just the structure of a table. Records should be a Python dictionary (or dictionary-like object) and can be processed one-by-one, or against an entire list of dictionaries.
+Expectations can be used alongside, or in place of a schema validator, however Expectations is intended to perform validation of the data in a dataset, not the structure of a table. Records should be a Python dictionary (or dictionary-like object) and can be processed one-by-one, or against an entire list of dictionaries.
 
-[Data Expectations](https://github.com/joocer/data_expectations) was inspired by the great [Great Expectations](https://github.com/great-expectations/great_expectations) library, but we wanted something lighter and easier to quickly set up and run. Data Expectations can do less, but it does it with a fraction of the effort and has zero dependencies. Data Expectations was written to run as a step in data processing pipelines, testing the data before it is committed to the warehouse.
+[Data Expectations](https://github.com/joocer/data_expectations) was inspired by the great [Great Expectations](https://github.com/great-expectations/great_expectations) library, but we wanted something lighter and easier to quickly set up and run. Data Expectations can do less, but it does it with a fraction of the effort and has zero dependencies. 
+
+## Use Cases
+
+- Use Data Expectations was as a step in data processing pipelines, testing the data conforms to expectations before it is committed to the warehouse.
+- Use Data Expectations to simplify validating user supplied values.
 
 ## Provided Expectations