Data Governance Reality Check

DATE: February 18, 2020

TIME: 2 PM Eastern / 11 AM Pacific

PRICE: Free to all attendees

This webinar is sponsored by:

About the Webinar

It’s been almost two years since the General Data Protection Regulation shook up how organizations manage data security and privacy, ushering in a new focus on Data Governance. This complex but critical practice still has most enterprises grappling to master it for a myriad of reasons.

In this webinar, we’ll examine how Data Governance attitudes and practices continue to evolve and discuss what new research reveals as the most predominant challenges. We’ll delve into technology trends, including how adding certain capabilities will benefit your organization in terms of data asset availability, quality, and usability, including data consumer literacy and confidence.

When you attend this webinar, you will learn about:

  • The requirements for a successful and sustainable Data Governance program
  • Increasing confidence in data analytics for faster speed to insights
  • How to automate data preparation and intelligence and where to start

All registrants will receive a copy of the new erwin white paper, The 2020 State of Data Governance and Automation, which is based on a recent survey conducted by erwin and DATAVERSITY.

About the Speaker

Danny Sandwell
Director of Product Marketing, erwin, Inc.

Danny Sandwell.jpg

Danny Sandwell is an IT industry veteran with more than 30 years of experience. As Director of Product Marketing for erwin, he is responsible for communicating the technical capabilities and business value of the company’s data modeling and data intelligence solutions. During Danny’s 20+ years with the company, he has also worked in pre-sales consulting, product management, business development, and business strategy roles – all giving him opportunities to engage with customers across various industries as they plan, develop, and manage their data architectures. His goal is to help enterprises unlock the value of their data assets to produce the desired results while mitigating data-related risks.

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