Presented by Kelle O’Neal

Is Data Governance a project, team, or program? Ideally, it’s a (shared) service.

Data is everywhere in the enterprise and is an asset just like your employees. Essentially, it strives to ensure organizations have reliable and consistent data sets to assess performance and enable data-driven decision-making. As a shared service, governance should also aim to create guidelines or policies on how data should be created, managed, shared, used, and ultimately archived or deleted. But even in a small organization, it’s impossible for governance to create or manage all of the data in the enterprise.

By adopting a shared service approach, Data Governance can be even more deeply ingrained in your company’s key processes, mindset, and culture, facilitating a transformational shift for not only achieving essential mandates and objectives, but also establishing governance as an enterprise-wide service – and capability.

In this session, Kelle will share framework approaches for:
• Integrating Data Governance at the project level with a data-centric development life cycle lens
• Aligning Data Governance to critical business processes such as data acquisition
• Establishing roles and responsibilities that support a service mindset and the tools, templates, and
  artifacts that focus on enablement vs. control
• Leveraging Data Governance to coordinate cross-functional collaboration between data legal,
  privacy, and compliance

Presented by John R. Talburt

Accurate business reporting and data analytics can only be achieved using high-quality data. Yet many organizations either do not have a Data Quality program, or they just focus on standardizing source data. Having a complete, ongoing program to measure, monitor, and improve the quality of data is a competitive advantage for an organization in today’s data-driven economy. This tutorial is primarily for participants starting a comprehensive Data Quality program or wanting to assess and improve the capabilities of an existing Data Quality program.

Participants will learn:
• The definition of Data Quality
• The principle of managing information as a product
• What we can learn from manufacturing Total Quality Management (TQM)
• Supply-side versus demand-side Data Quality management
• The ISO 8000-61 Model for Data Quality management

Presented by Robert S. Seiner

Do you have strategy around data for your entire enterprise? Does it include Data Governance? A Data Strategy is a thorough plan for moving an organization toward a more data-driven culture. A Data Strategy is about more than just data. It is a roadmap that defines the people, process, and technology associated with the data.

Join Bob Seiner for this lively session where he will demonstrate that the more detailed and comprehensive a Data Strategy is, the better the chance that the business and technical parts of the organization will fully understand each other. Data Governance plays a huge role in becoming data-driven. And it is time for a Data Strategy that defines governance at its core.

In this session, Bob will share:
• Why a Data Strategy is needed
• The core components of a Data Strategy
• The strategic difference between Data Governance and Data Management
• How Data Governance must be an integral part of the strategy
• How to convince management that a Data Strategy is essential

Sessions will resume for our keynote presentation following this 30-minute break.

Presented by JuneAn Lanigan

Data Governance is key to Data Management, without a doubt. For the past couple of years, our mission at Western Digital has been to create a “culture of accountability for Data Quality” through our Data Governance program, and we are making great progress! But with that progress, we also realize that Data Governance isn’t “The Why,” nor is data analytics. The real reason we put effort into managing our data is to ensure data health, which is much more complex. In this talk, I will share with you the multi-dimensions of data health, including “data fitness,” the future focus of Data Management. Data Governance remains a key discipline, but data health unlocks business results.

Presented by Anthony J. Algmin

Most of the time we think we are talking about data we’re actually talking about metadata. Metadata, or “the story of data,” is anything that gives context to data – and needs to be governed just as much as data itself. Metadata governance complements Data Governance and should be on your radar. Similarly, “the story of data over time” is data lineage. Data lineage shows where data comes from and how data moves and transforms throughout its lifecycle. Documenting data lineage is a complex undertaking that needs both coordination and constraint, as without care it can quickly spiral out of control.

Adding these to your Data Governance strategy will help your organization make the most of its data by giving your people the best insights possible. During this session we will give you some foundational principles and techniques to get started with metadata governance and data lineage.

Key Takeaways Include:
• The symbiotic relationship between metadata governance, data lineage, and Data Governance
• Getting started with metadata governance and data lineage
• Understanding the kinds of tools that facilitate metadata governance and data lineage
• Sustaining all of these programs alongside Data Governance
• Why creating data value is the most important thing

Presented by Amirah Fayek

All too often, organizations focus on measuring Data Quality and neglect the importance of measuring the overall Data Governance program. Taking a step back to identify the desired outcomes and benefits of your program, and how they align to your organization’s strategic goals, is critical. Working from short-term all the way up to long-term desired outcomes will help you demonstrate value in data initiatives, gain buy-in across the organization, and ensure decision-making alignment.

In this tutorial, participants will learn:
• The importance of measuring the performance of your Data Governance program
• How to assess the maturity of your program and measure progress
• How to identify desired outcomes and develop a performance measurement framework, including
  examples of measures
• Development of a logic model, linking your measurement to strategic and business outcomes
• Reporting and engaging with stakeholders to demonstrate the value of your program