ANALYTICS THAT DELIVER

ALL your data | better Insights | lightning Speed

From self service desktop and team-based analytics to fully automated solutions, Arbutus technology empowers you to use ALL of your data to gain better business insights at speeds you never thought possible.


 

Access All Your Data

Access your Data:  Big, Small, Complex, Disparate.  Systems:  Financial, Operational, IT.  Platforms:  On Premise, Cloud, PC, Mainframe
 

Explore

Discover what data you have before you get started.  Unlock new analytic opportunities based on what you find.
 

Verify & Prepare

Ensure your data is accurate and complete.  Consolidate, Normalize, Cleanse, Discover.
 

Analyze

Engineered for Analysis.  Unlock the Power to ask any question of all your data and process at millions of records per second.
 

Communicate & Remediate

Systematically Manage Your Results.  Share Your Findings across your organization within Arbutus or within your Data Visualization or other Applications.
 

AUTOMATE

Automate Your Analytics.  Scheduled, Centralized, Continuous.  Focus your efforts where they're needed the most.
 

OUR CUSTOMERS ACHIEVE

1,000S OF SUPPORTED USERS | OVER 60 COUNTRIES | 6 LANGUAGES

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.

your path to success

Innovate.

Drive Efficiency & Performance.

Automate.

Work Better Together.

Reduce Risk.

Stamp Out Fraud, Waste & Abuse.

Power your Applications with Arbutus Analytics.

 

 

REQUEST TRIAL

 

AnalyticS focus & Experience

Originally architected by a pioneer who launched the self service Audit Analytics movement, Arbutus is focused on delivering high performance analytics technology combined with  comprehensive functionality.  Throughout our company, we provide unparalleled experience to help our customers achieve and maintain success with analytics.

Artificial Intelligence
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community & career enhancement

Dedicated Customer Portal.  Virtual, Onsite, & Open Enrolment Training.  Online Learning.  Live and On Demand Webinars.  AuditNet Premium Membership.  Designations and CPEs.  LinkedIn Group.  Experienced Technical Product Support & Consulting Services.

Our Latest Articles

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Article

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

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Article: 

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

May 26th post

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

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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