Great Expectations

Make better business decisions

Data Quality Testing


Improve the quality and reliability of your data, increase productivity, reduce operational risk and ultimately make better business decisions with Great Expectations – a powerful data quality testing solution for all data-driven businesses.

Why is data quality testing so important?

Do not assume your data is correct!

In order to make the right data-driven business decisions it is crucial that any analysis conducted is based on accurate data, but in many cases – despite the best efforts to build reliable data assets – the data may not be correct.

Often inaccurate data goes unnoticed for a long time, until much later a problem rears its ugly head and opens a can of worms. At this stage, although you know there is a problem, it can be very challenging and time-consuming to detect where the problem originates from. Worse still, inaccuracies may go completely undetected, leading to decisions based on incorrect intelligence that open the business up to operational risk.

Typical reasons for inaccurate data

  • Accuracy of the data inputting process
  • Consolidation or interconnecting legacy data assets
  • Previously accurate data systems have evolved, new data tables have been added and new teams are working on the data
  • Source systems have changed
  • More reasons

What is Great Expectations?

Great Expectations is an open-source data quality testing tool, available on GitHub that monitors data quality, automates the verification of new data and simplifies the debugging process. It reduces productivity drain and operational risk by improving the data quality and the trustworthiness of your analytics.

How it works

Step 1.
New raw data is imported

Step 2.
The data is validated through Great Expectations and each row of data is updated either with a 0 or 1 depending on whether it fails or passes the business rule

Step 3.
The invalid rows (those that ‘fail the test’) are moved into a separate ‘quarantine’ table, while the valid rows are moved into a clean table where the data pipeline can continue

Step 4.
All data is removed from the raw ‘staging table’, making it ready to take the new data and repeat the flow on a monthly basis.

Benefits of Great Expectations

  • Identify data quality issues
  • Improve your CI/CD processes
  • Save time in the data cleansing process
  • Set up data pipelines more quickly
  • Accelerate ETL and data normalisation
  • Easily manage the complexity within your data pipeline

And overall make better business decisions which is the ultimate goal!

Ready to find out more? Our team are excited to be able to show you what Great Expectations can do for your business!
Arrange a demo today or speak to one of our team for more information