Presented by Donna Burbank

With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.

Presented by Chris Bradley

In this session, we will cover a high-level exploration of the different disciplines essential to Data Modeling including:

Presented by Robert S. Seiner & Anthony J. Algmin

Data Architecture and Data Governance are the same thing! Aren’t they? Most people would say that this line of thinking is absurd — or even worse. There is NO WAY that they are the same thing. Or are they?

In this session, Bob and Anthony will discuss:

Presented by John Ladley & Laura Sebastian-Coleman

A few years ago several influencers in the data world got together to declare a manifesto: that data is key to organic growth and improvement of the human condition, but we have a long way to go (dataleaders.org/manifesto/read-manifesto).

Since then this group has grown and is now providing tools and insight into the data industry. Along the way they discovered the importance of moving away from just technical data leaders and getting in front of organization leadership to make them data leaders.

But this is not easy. Words matter, and we struggle to get engagement. Please join this session as members of the Leaders Data Organization (LDO) present why your words and communications are important, and what you need to do. In addition, we will provide an update on the research and current tools we offer as well as others in the pipeline.

Presented by Uzma Khan

In this session we will discuss Boeing’s journey of building a data lake.

Presented by Elisa Kendall & Dean T Allemang

Knowledge graph technology enables question answering across data silos at scale. In order to provide an understanding of the data and explain the results returned by complex queries, machine learning, and other applications, a common vocabulary is essential.

Industry-standard models are often criticized due to their complexity, including significant breadth and depth. This is a consequence of the unique position that an industry-level ontology plays with respect to other data models. In short, an industry ontology has to anticipate a wide range of design options in enterprise data models – and include elements that reflect those options – to mediate viewpoints.

How can an industrial ontology manage this complexity and make it usable at an enterprise level? Through modeling patterns that allow for the range of variation required while allowing the model to be understandable and applicable to enterprise data modelers.

We illustrate some of these patterns and this effect with specific examples from FIBO (Financial Industry Business Ontology) mappings to enterprise data models. Lessons learned in developing and using these patterns not only in finance, but for automotive, retail, pharmaceutical, and industrial applications, provide a level of confidence in their general applicability.