Undoubtedly they are both great products, however they are expensive.
It is likely in many organisations it would take significant time to approve a big purchase purchase.
The price point will not deter the majority of Financial Services organisations.
In addition the ongoing administration is inexpensive.
Data Quality checks are bespoke
Most data quality checks are highly specific.
A business rule against a specific data point in a specific data set is a unique check.
For example look at an ISIN.
Depending on the country code (first 2 letters of the ISIN) the remainder of the ISIN may have a specific format and a relationship to other data points, such as a CUSIP.
For something as simple as an ISIN there are many business rules to identify whether it is correct. Each business rule is another quality check and each quality check requires writing.
Data Quality checks are really an ETL process
From a technical perspective Data Quality checking is an ETL process.
- Extraction: the source data needs bringing into the quality check
- Transformation: check the source against the business rule, transforming the source data into a check result
- Load: the capture of the quality check results
In Alteryx, a single workflow can perform a number of quality checks against the same data set.
Input a source data set to the workflow then write the business rules using Formula tools.
Finally Output the results.
Templates can accelerate the quality check writing time
Reduce the time to write each quality check in Alteryx by using templates.
A good Data Quality Check template workflow should rapidly accelerate writing new checks.
The inputs and checks (formula tools) are easily modified and the outputs should be standard for all checks. This simplifies the reporting of the results.
Using Alteryx to set up your Data Quality checks is a quick way to achieve results.
In addition, Alteryx is easy to automate.
Get in touch if you would like to learn more about our tactical Data Quality solution.