60% of your risk for a data breach
"The average cost of a data breach in 2016 was $7 Million."
Source: Ponemon Institute, 2016 Cost of Data Breach Study
Acceletest is an enterprise solution designed to work within large-scale and complex data testing environments. WE MAKE IT SIMPLE for you to efficiently and securely test with live production data.
Acceletest was developed FOR USE BY TESTING AND BUSINESS ANALYSTS – not by technology staff in support of testing. That means you don’t have to understand how complex database functions work, or how to write SQL, to be able to create test data.
ACCELETEST TAKES THE COMPLEXITY OUT OF RIGHT-SIZING YOUR PRODUCTION DATA. While we can subset data on simple criteria such as the number of records needed, Acceletest also utilizes rules-based technology to intelligently create subsets of production data that meet all of your complex test criteria.
By intelligently subsetting your data based on business rules, Acceletest dramatically reduces your data storage costs and test cycle time. THE RESULT IS BETTER DATA FASTER AND AT LOWER OVERALL COST.
ACCELETEST ACCELERATES THE TESTING PROCESS through automated data comparison that ensures differences in data sets can be quickly identified and properly analyzed.
ACCELETEST IMPROVES EFFICIENCY WITH automated and repeatable processes for the creation and management of data subsets.
"43% of data breaches are caused by insiders."
Source: Intel Security "Grand Theft Data.”
Acceletest employs a requirements driven rules engine to create real test data from live production data. Simultaneously subset, transform, and protect using a single automated, repeatable process.
Acceletest's compare is an innovative test data comparison utility that compares millions of records across multiple platforms simply and quickly. DESIGNED FOR USE BY NON-TECHNICAL USERS, COMPARE ALLOWS ORGANIZATIONS TO:
"Organizations should use data masking to protect sensitive data at rest and in transit from insiders' and outsiders' attacks."
Source: Gartner Magic Quadrant for Data Masking Technology Worldwide. Dec 2015