Post-Conference Tutorials - June 30, 2016

Thursday
June 30
7:00–8:30
Registration and Continental Breakfast
8:30 - 4:15 POST-CONFERENCE TUTORIALS

Thursday
June 30
8:30-4:15

 

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T13: Implementing Agile Data Governance
Tami Flowers, Director, Governance Solutions, MetaGovernance Solutions LLC

This tutorial provides in-depth real-world examples, templates, how to’s and lessons learned for implementing data governance in an Agile framework. You’ll leave with knowledge, examples, and artifacts you can use to implement an Agile Data Governance framework.

Agile principles align very well with the keys to success for Data Governance. Using Agile methodology for Data Governance focuses on deliverables that are valuable to the business and continuously introduced into the organization. These are delivered through close collaboration of motivated individuals. Through continuous improvements to the Data Governance process, an organization can remain flexible, compliant, and responsive; and end up with an integrated data governance program that enables the delivery of accurate and timely information for operational or financial disclosure needs.

This tutorial provides experiences and examples of Agile Data Governance.  During this session you will learn:

  • Different approaches to Agile and Lean and how each can be used with Data Governance.
  • What an Agile Data Governance framework looks like.
  • How to align the principles of Agile with the Data Governance framework.
  • In-depth how to’s, real-world examples, and hands on practice for implementing or maturing Data Governance using Agile.
  • Using an Agile framework to be more responsive to Data Governance issues, regulatory changes, audit/exam findings, and the like.
  • Examples of artifacts and templates you can take with you and use.  

Level of Audience:
Intermediate

Speaker:
Tami Flowers Tami Flowers
Director, Governance Solutions
MetaGovernance Solutions LLC

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Thursday
June 30
8:30-4:15

 

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T14: Assessing Your Existing Data Governance Program
Robert S. Seiner, President / Publisher, KIK Consulting / TDAN.com

Your Data Governance is now a couple of years old. You engage your Data Stewards. You make good use of your Data Governance Council. And you demonstrate consistent value to your organization. Or do you? Is this the state of your Data Governance program? Have you climbed every mountain? Do you feel like you are running out of places to add value? Perhaps there are there aspects of the program that can be improved.

This tutorial with Bob Seiner will focus on how to assess your existing data governance program, articulate strengths and address specific opportunities to improve. Bob will share advanced data governance techniques used to expand the focus from the disciplines you already formally govern into disciplines such as information quality, data protection, metadata or master data management, or even the lauded Big Data discipline. The session will help you move from routine to progressive.

In this session Bob will talk about:

  • Steps to assess an existing program
  • Measuring and focusing on results over time
  • Using existing roles to address new tasks
  • Expanding into new areas of discipline
  • Maintaining a progressive attitude and direction

Level of Audience:
Advanced

Speaker:
Robert S. Seiner Robert S. Seiner
President / Publisher
KIK Consulting / TDAN.com

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Thursday
June 30
8:30-4:15

 

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T15: Developing and Implementing a Data Quality Framework Supporting Data Governance
David Loshin, President, Knowledge Integrity

A Data Quality Framework is intended to institute best practices for proactive assessment, measurement, inspection, monitoring, notification, tracking, and resolution of data quality issues. Identifying and implementing the associated componentry coupled with best practices in data quality management will help retool existing workflow processes with automated systems that can help reduce the need for manual data inspection while rapidly alerting data stewards to identified issues.

This tutorial looks at leveraging an organization’s existing data management infrastructure, available development tools, and practical data quality best practices to develop a formal framework for data quality management that organizes practices for:

  • Data quality rules management: Soliciting and managing data quality requirements and validation rules for data verification.
  • Data quality measurement and reporting: Enabling and invoking services to validate data against data rules and report anomalies and data flaws, both through notifications and through scorecards.
  • Standardized data validation: Validate existing processes while integrating services for data verification within newly-developed processes.
  • Source data quality assessment: Source data assessment and evaluation of data issues to identify potential data quality rules.
  • Incident management: Standardized approaches to data quality incident management (reporting, analysis/evaluation, prioritization, remediation, tracking).

Level of Audience:
Intermediate

Speaker:
David Loshin David Loshin
President
Knowledge Integrity

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