Conventional Wisdom The Business Case. Return on Investment. Data as an Asset. Data is the New Oil. What these concepts have in common is a conceptualization of data governance as something that helps businesses create financial value from collected data. There is nothing wrong with this. But is does not necessarily apply to data in the public sector, and efforts to apply the same concepts without tailoring them specifically for the public sector environment risks missing the entire point of public sector governance.

  Reimagining Data Governance from a Public Sector Perspective In this presentation, we will look at the goals, inputs, and outputs that are specific to the governance of data in the public sector, including civilian and defense federal agencies, as well as state and local governments. Specifically, we will explore:

• Mission Case vs. Business Case: In the public sector, data can and should be a democratizing force, an enabler of good government, and a contributor to national security;
• The Data Lifecycle: We will look at the data lifecycle as completely separate from the software development lifecycle, since data governance from acquisition/creation to archiving in the government arena has implications far beyond any IT project or program;
• Laws and Regulatory Frameworks: Governance of data in the public sector can be excruciatingly complex, and we will cover just a few examples;
• Data Owners vs. Data Stewards: A popular data governance concept in recent years is to disparage data “ownership” in favor of “stewardship,” but we will see why both roles are important in the public sector realm.

View the presentation, When Data is NOT the New Oil: Public Sector Data Governance and Getting Beyond The Bottom Line

Now that we are in the post-big data era (according to Gartner), it is important to take a step back from the hype that has characterized the Big Data Scientist movement of the past few years because two trends have become evident. First, Big Data projects have been about as successful as other IT projects – about 29% successful according to the 2015 Standish Group Chaos Report. Second, Data Scientists are generally assessed to be about 20% productive. The reason for both of these dismal statistics is simple—organizations are terrible about understanding how to use data as a strategic organizational resource. In fact, considering the data is our sole, non-depletable, non-degrading, durable strategic asset, it is really mind boggling how poorly it is managed. Having an actionable data strategy is the first, most critical step in exerting positive control over data and leveraging it in support of your organization’s business strategy. This talk will simply describe how you can: 1) Reduce the amount of data in your organization that is redundant, obsolete, or trivial (ROT); 2) Develop an inventory of the data that you have; 3) Determine how to prioritize among investments in data quality and improvement. Once understood, your organization will be better positioned to support its mission and take advantage of new and existing data sources while complying with relevant laws, regulations, and policies.

View the presentation, Data Strategy: It should be concise, actionable, and understandable by both business and IT!

The Building Blocks…
Big data has challenged our notion of conventional data management and data warehousing practices. In this session, we will discuss why some of the key data management and data governance disciplines such as Data Architecture, Data Quality and Meta Data Management are still relevant. We argue that they are even more critical than ever before for the integration of big data into the larger analytics ecosystem.

Making It Real!
Furthermore, we will compare and contrast best practices in conventional data management and how they can be adapted to the big data world. Specifically, we will explore:

• Data Architecture: Some of the best practices around standardization and modeling for building RDBMS ‘schema-on-write’ are still relevant for NoSQL and ‘schema-on-read’
• Meta Data Management: This is a critical component for discovery, cataloging, consistency of use, context and meaning of big data
• Data Quality Management: As big data matures, the ‘veracity’ dimension of big data is becoming more critical. Although, there may be slightly different interpretations of exactly what ‘veracity’ of big data means, for the purposes of this discussion, we will discuss it in the context of quality and relevance of the underlying data impacting analytics results.

View the presentation, Big Data Management: A Modern Take on Key Data Management Best Practices

This session focuses on how one of the world’s largest education companies implemented business value through data governance across the enterprise. Attendees will learn how we turned challenges into successes, and how to apply our lessons learned to their own organizations.

• Identifying and demonstrating the need for enterprise data management practices
• Obtaining executive sponsorship
• Partnering with industry experts for guidance
• Choosing the path between a top-down and bottom-up approach
• Designing and implementing a pilot data quality program that demonstrates the business value of governing data through quality
• Identifying and training data stewards
• Collaboratively working with the business to continuously focus on the business value add of data governance.

View the presentation, Case Study: Enterprise Data Governance and Stewardship at a Worldwide Education Company

Given the explosion of personal health data creation, how can organizations leverage the context of data capture and use to drive secure and effective information management processes across the realm of healthcare support delivery?

View the presentation, Managing the flood: Using information context to drive health data management and delivery