Big Data

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.

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.

Agile Data Management Architecture

2019-10-24T04:46:19+00:00Categories: Predictive Analytics & AI, Data Culture Curriculum, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Fraud and Security, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Governance Level 1, Data Management, AI Engineering Curriculum, Executive Level 2, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , |

This full-day workshop examines the trends in analytic technologies, methodologies, and use cases. The implications of these developments for deployment of analytic capabilities will be discussed with examples in future architecture and implementation. This workshop also presents best practices for deployment of next generation analytics.

Innovating with Best Practices to Modernise Delivery Architecture and Governance

2019-10-24T04:48:00+00:00Categories: AI Engineering Curriculum Electives, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, Data Governance Level 1, Data Management, Executive Level 2, Infrastructure & Technologies, Big Data, Data Engineering Level 1, AI Engineering Level 2, All Academy Courses|Tags: , , , , , |

Organisations often struggle with the conflicting goals of both delivering production reporting with high reliability while at the same time creating new value propositions from their data assets. Gartner has observed that organizations that focus only on mode one (predictable) deployment of analytics in the construction of reliable, stable, and high-performance capabilities will very often lag the marketplace in delivering competitive insights because the domain is moving too fast for traditional SDLC methodologies. Explorative analytics requires a very different model for identifying analytic opportunities, managing teams, and deploying into production. Rapid progress in the areas of machine learning and artificial intelligence exacerbates the need for bi-modal deployment of analytics. In this workshop we will describe best practices in both architecture and governance necessary to modernise an enterprise to enable participation in the digital economy.