Powerful Analytics. Simplified
Whether it's helping to streamline the tax process for an entire country or turbocharging the evolution of an energy sector powerhouse's Audit process, Arbutus is the engine that powers change. Find out why the world's leading organizations rely on Arbutus technology to support their Audit, Fraud Detection, Risk, Compliance and IT efforts.
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Learn how you can use Arbutus Analyzer to test your data quality. This article explains step-by-step instructions on how to use commands and functions to analyze numeric, date, and character fields for validity, consistency, and completeness.
Author: Michael Kano
The problems of Data Quality (DQ) are well encapsulated in the phrase "Garbage in, Garbage out". In this age of 'Big Data', DQ has never been more important, and at the same time more challenging, particularly as it relates to legacy systems. Read the full article here about Data Quality - https://bit.ly/2XyHO5w. This article describes the problem and symptoms and suggests an approach to identifying DQ issues.
Author: Grant Brodie
Article
Audit Analytics at home - Working from home could be the new normal for some time to come, but how does that impact the analysis of sometimes sensitive data? Data security is certainly a challenge now that working from home is the norm. This challenge is potentially magnified when you imagine that the above scenario might involve a number of data analysts, who all need data on their PC/Laptop, but who might also need to share results with one another. You've now got multiple copies of sensitive data being stored in potentially unsecured environments and sent back and forth via email so that colleagues can collaborate - not ideal.
Read the full article Author: Ben Mitchell
Article
Learn how to normalize addresses and detect hidden duplicates using functions in Arbutus Analyzer. Also included are files, scripts and instructions for you to run this yourself in Analyzer. We'll take a look at two examples: first, using a single file, and second, comparing normalized addresses across two files.
Author: Michael Kano
