Stephen Brobst

Stephen Brobst is the Chief Technology Officer for Teradata Corporation. Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management. Stephen is a TDWI Fellow and has been on the faculty of The Data Warehousing Institute since 1996. During Barack Obama’s first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD). In 2014 he was ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.

Data Science and Big Data Analytics: Leveraging Best Practices and Avoiding Pitfalls

2019-10-17T00:15:45+00:00Categories: Data Governance Level 2, Data Engineering Curriculum Electives, Data Science Curriculum, Data Science Level 2, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Engineering Level 2, Data Management, Executive Level 2, Big Data, All Academy Courses|Tags: , , , , , , |

Data science is the key to business success in the information economy. This workshop will teach you about best practices in deploying a data science capability for your organisation. Technology is the easy part; the hard part is creating the right organisational and delivery framework in which data science can be successful in your organisation. We will discuss the necessary skill sets for a successful data scientist and the environment that will allow them to thrive. We will draw a strong distinction between “Data R&D” and “Data Product” capabilities within an enterprise and speak to the different skill sets, governance, and technologies needed across these areas. We will also explore the use of open data sets and open source software tools to enable best results from data science in large organisations. Advanced data visualisation will be described as a critical component of a big data analytics deployment strategy. We will also talk about the many pitfalls and how to avoid them.

Stars, Flakes, Vaults and the Sins of Denormalisation

2019-10-18T03:01:05+00:00Categories: Data Governance Level 2, Innovation & Tech (CTO) Curriculum Electives, Data Governance Curriculum Electives, Executive Curriculum Electives, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Data Management, AI Engineering Curriculum, Executive Level 2, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , |

Providing both performance and flexibility are often seen as contradictory goals in designing large scale data implementations. In this talk we will discuss techniques for denormalisation and provide a framework for understanding the performance and flexibility implications of various design options. We will examine a variety of logical and physical design approaches and evaluate the trade offs between them. Specific recommendations are made for guiding the translation from a normalised logical data model to an engineered-for-performance physical data model. The role of dimensional modeling and various physical design approaches are discussed in detail. Best practices in the use of surrogate keys is also discussed. The focus is on understanding the benefit (or not) of various denormalisation approaches commonly taken in analytic database designs.

Best Practices in Enterprise Information Management

2019-10-24T04:45:22+00:00Categories: Data Culture Level 1, Data Culture Curriculum, Innovation & Tech (CTO) Curriculum Electives, Data Governance Curriculum, Stephen Brobst, Fraud and Security, Executive Curriculum, Data Engineering Curriculum, Data Governance Level 1, Data Management, Executive Level 2, Big Data, Data Engineering Level 1, All Academy Courses, Innovation & Tech (CTO) Level 3|Tags: , , , , , |

The effective management of enterprise information for analytics deployment requires best practices in the areas of people, processes, and technology. In this talk we will share both successful and unsuccessful practices in these areas. The scope of this workshop will involve five key areas of enterprise information management: (1) metadata management, (2) data quality management, (3) data security and privacy, (4) master data management, and (5) data integration.

Overcoming Information Overload with Advanced Practices in Data Visualisation

2019-10-24T04:46:56+00:00Categories: Data Culture Electives, Innovation & Tech (CTO) Curriculum Electives, Data Science Curriculum, Data Science Level 1, Data Culture Level 2, Innovation & Tech (CTO) Level 2, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, AI Engineering Level 1, All Academy Courses|Tags: , , , , , , , |

In this workshop, we explore best practices in deriving insight from vast amounts of data using visualisation techniques. Examples from traditional data as well as an in-depth look at the underlying technologies for visualisation in support of geospatial analytics will be undertaken. We will examine visualisation for both strategic and operational BI.

The Future of Analytics

2019-10-24T04:44:27+00:00Categories: Predictive Analytics & AI, Data Science Level 2, Data Science Curriculum Electives, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses, Data Governance Level 3, Innovation & Tech (CTO) Level 1|Tags: , , , , , , |

This full day workshop examines the trends in analytics deployment and developments in advanced technology. The implications of these technology developments for data foundation implementations will be discussed with examples in future architecture and deployment. This workshop presents best practices for deployment of a next generation data management implementation as the realization of analytic capability for mobile devices and consumer intelligence. We will also explore emerging trends related to big data analytics using content from Web 3.0 applications and other non-traditional data sources such as sensors and rich media.