Our solutions leverage machine learning algorithms to parse and process structured and unstructured data to identify the presence of the sensitive information. We use multi-node parallel computing paradigm to achieve lightening fast speeds.
Data creates value in today’s information-driven organizations. Much of the value can be lost in the absence of a well-defined data governance program. We define data governance as the processes and procedures to ensure quality, availability, usability, security and privacy of the data.
In today’s interconnected economy, organizations are constantly exchanging large volume of data in a variety of formats through a number of channels (e.g. cloud, B2B) with its customers, vendors, and partners. While significant investment has been made to ensure quality, availability, the usability of these data exchanges, we believe that the current approach towards ensuring security and privacy is inadequate.
During the course of our professional career spanning over 20 years, we have helped numerous Global 1000 organizations to establish data governance programs more specifically programs to ensure data quality.
With the accelerating adoption of customer specific big data, increasing reliance on automated systems for critical business decisions, and an expanding number of privacy-related regulations, detecting and protecting sensitive information is no longer an option but an imperative.
The cost of ignoring data privacy and security issues could be significant. We have estimated that US businesses lost approximately $37 billion in 2015 to address the aftermath data privacy and security issues – this does not take into account the reputational damage these businesses suffered as a consequence.
Leveraging our 20+ years’ experience in data governance and machine learning algorithms, we have been able to create a set of solutions to address data privacy issues. More specifically, we offer solutions to detect and protect sensitive information when large volume of data in a variety of format moves at a very high speed.
Advisor and Member of the Board
Paul is an expert in optimizing and developing effective processes and programs that drive sales, marketing and business development initiatives through an integrated multi-channel strategy. He prides himself on being a great advocate both internally for employees and externally to customers. Paul attended DePaul University with a concentration in Finance and Accounting.
Founder and CEO
He earned a Bachelor of Technology degree in engineering from the Indian Institute of Technology, Kharagpur, an MS in computer science from the Illinois Institute of Technology and an MBA in Analytical Finance and Strategy from the University of Chicago.
Co Founder and VP, Customer Service and Support
ShellyThomas is an experienced data modeler and has extensive experience in leveraging data analytics to solve data governance problems. She has helped numerous Fortune 500 companies including HCSC, American Express, TIAA-CREF, TSYS, Mass Mutual to successfully implement data governance software solutions.
Shelly earned an MS in computer science from the Illinois Institute of Technology and Ph.D. in engineering from the Purdue University. She has published a number of articles in peer-reviewed journals and won numerous research awards and fellowships.
Clouderadelivers the modern platform for data management and analytics. We provide the world’s fastest, easiest, and most secure Apache Hadoop platform to help you solve your most challenging business problems with data.
Doug Cutting, co-creator of Hadoop, joined the company in 2009 as Chief Architect and remains in that role today. Cloudera has over 1100 employees (as of 1/2016) across the globe. We’ve won numerous awards and accolades from industry watchdogs—including the 2014 and 2015 Database Trends and Applications Magazine Readers’ Choice Award for best analytical platform.
Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Brazil, Singapore, Japan, and Australia,