Whether by choice for reasons of profitability or because of emerging privacy laws, more and more companies are becoming aware of the important role data plays in their overall business practices. As a result, we are seeing more demand and the continuing creation of Data Management personnel roles along with entire Data Management divisions within the organizations. So who should companies hire as they expand? What’s the difference in responsibility between a Data Architect, a Data Modeler, and a Data Engineer?

Data Governance can have a varied definition, depending on the audience. To many, Data Governance consists of committee meetings and stewardship roles. To others, it focuses on technical Data Management and controls. Holistic Data Governance combines both of these aspects, and a robust Data Architecture and associated diagrams can be the “glue” that binds business and IT governance together. Join us for this session where we will uncover practical tips and hands-on exercises for aligning Data Architecture and Data Governance for business and IT success.

• Data Governance and Data Architecture overview
• Data Architecture for business-centric governance
• Data Architecture for IT-centric governance
• How to build quick wins through Data Architecture and Governance
• Case studies

Which comes first, your Data Architecture or your Data Strategy? Conventional business-school wisdom says that strategy always comes first, and certainly, that is the ideal, but unless you have the luxury of starting from scratch with your Data Architecture, then you probably have to "work with what you have," at least for a while. And yet the difficulty of implementing new data initiatives often goes under-appreciated, particularly the common organizational readiness and core competence challenges facing most organizations. Beyond that, there are common cultural and structural barriers that must be eliminated in order to leverage data effectively. This talk will discuss these barriers, how to overcome them, and how to get your strategy and architecture into alignment.

Last October, Anthony led a live panel discussion on the future of Data Architecture for the closing keynote at DATAVERSITY's Data Architecture Summit conference. At the end of four days of Data Architecture education on the latest trends, technologies, and best practices, Anthony asked his panel of experts what it all meant for the future. Now, he will explore whether those thoughts have been confirmed over the last few months, and what is still changing in the future of Data Architecture.

Some of the anticipated questions he will cover are:

• Are the boundaries of Data Architecture expanding or shrinking?
• What about the role of Data Architects – are they changing?
• What's the relationship between Data Architecture and Data Governance?
• What's the role of these technologies in the future of Data Architecture?
  • Artificial Intelligence and Machine Learning
  • Blockchain and distributed ledger technology
  • Data-Centricity
  • Ontologies and Graphs
  • Cloud
  • Stream Processing

Google has popularized the notion of a Knowledge Graph, loosely defined as a network of entities and their interrelationships. While the phrase has become popular recently, and in some ways appears to be everywhere, semantic networks and supporting semantic technologies have existed for many decades. We may, however, be at a tipping point in the field with the combination of an explosion in big open data collections and significant advances in data mining and Machine Learning techniques. Now, many more organizations are deploying Big Data applications and are trying to make sense of their results. In this talk, we will introduce some trends we see in Knowledge Graphs through examples of complex, integrated systems. We will discuss some potential avenues for growth, and ways that Knowledge Graphs are becoming essential components in current and potential Artificial Intelligence and Data Science applications that need to expose their methods and explain their results.