Events

MIM Speaker Series: The Nature and Impact of Hidden Data Errors on Information Risk and Data Science

Event Start Date:
Monday, February 01, 2021 - 12:00 PM
Event End Date:
Monday, February 01, 2021 - 01:00 PM
Location
Virtual
Add to Calendar 2021-02-01 12:00:00 2021-02-01 13:00:00 MIM Speaker Series: The Nature and Impact of Hidden Data Errors on Information Risk and Data Science You're invited to join us next Monday, February 1 from 12-1pm ET for our MIM Speaker Series event on The Nature and Impact of Hidden Data Errors on Information Risk and Data Science. RSVP Here! Talk Abstract: Information Risk is an important field that encompasses multiple existing disciplines and overcomes the boundaries among them that has impeded knowledge sharing and effective management of integrated business projects. These projects often span multiple organizational groups and use different structured frameworks of information, data, computing, and security management. The practical realities intrinsic to performing this work leads to gaps in complying with regulations, ensuring secure operations, satisfying auditors, and even meeting program objectives for data analysis and business uses. This talk describes how deeply embedded data disparities that remain hidden to typical data methods lead to high error rates in project results. Lessons learned from assessing and correcting these situations is presented with examples of the problems and methods to detect and fix them.  Bio: Geoffrey Malafsky has a PhD in Chemistry from Penn State University and was a research scientist studying fundamental Nanotechnology at the Naval Research Laboratory in Washington, DC. He subsequently joined SAIC and was Director Technology Innovation supporting diverse clients in DARPA, ONR, and DoD as technology expert on advanced programs. He then formed his first company, TECHi2, providing technical expertise to large Govt and corporate clients in data integration, metadata implementation, semantic technology, Enterprise Architecture, and Knowledge Management. Lessons learned from this work led him to form his second company Phasic Systems Inc. which developed a custom enterprise grade product, DataStar, that blended the critical components of these disciplines into an integrated framework working on top of Hadoop for Big Data normalization and custom business logic high performance computing. He is now CEO and Chief Scientist of two startup companies: TechnikInterlytics and SafeHaven. TechnikInterlytics refines the technology and methods established with the enterprise data normalization product into an automated expert system that performs deep inspection of diverse data sets to discover and correct in real-time the complicated oft hidden data disparities that prevent high quality data science processing. SafeHaven provides B2B API based identity management and consolidated parental controls for youth eSports and online gaming with strong security, ease of use, and adherence to industry standards and government regulations.  Virtual America/New_York public

You're invited to join us next Monday, February 1 from 12-1pm ET for our MIM Speaker Series event on The Nature and Impact of Hidden Data Errors on Information Risk and Data Science.

RSVP Here!

Talk Abstract: Information Risk is an important field that encompasses multiple existing disciplines and overcomes the boundaries among them that has impeded knowledge sharing and effective management of integrated business projects. These projects often span multiple organizational groups and use different structured frameworks of information, data, computing, and security management. The practical realities intrinsic to performing this work leads to gaps in complying with regulations, ensuring secure operations, satisfying auditors, and even meeting program objectives for data analysis and business uses. This talk describes how deeply embedded data disparities that remain hidden to typical data methods lead to high error rates in project results. Lessons learned from assessing and correcting these situations is presented with examples of the problems and methods to detect and fix them. 

Bio: Geoffrey Malafsky has a PhD in Chemistry from Penn State University and was a research scientist studying fundamental Nanotechnology at the Naval Research Laboratory in Washington, DC. He subsequently joined SAIC and was Director Technology Innovation supporting diverse clients in DARPA, ONR, and DoD as technology expert on advanced programs. He then formed his first company, TECHi2, providing technical expertise to large Govt and corporate clients in data integration, metadata implementation, semantic technology, Enterprise Architecture, and Knowledge Management. Lessons learned from this work led him to form his second company Phasic Systems Inc. which developed a custom enterprise grade product, DataStar, that blended the critical components of these disciplines into an integrated framework working on top of Hadoop for Big Data normalization and custom business logic high performance computing. He is now CEO and Chief Scientist of two startup companies: TechnikInterlytics and SafeHaven. TechnikInterlytics refines the technology and methods established with the enterprise data normalization product into an automated expert system that performs deep inspection of diverse data sets to discover and correct in real-time the complicated oft hidden data disparities that prevent high quality data science processing. SafeHaven provides B2B API based identity management and consolidated parental controls for youth eSports and online gaming with strong security, ease of use, and adherence to industry standards and government regulations.