Training Courses, Workshops and Seminars

Data Governance II

2022-09-16T07:41:39+00:00December 2nd, 2020|Tags: , , , |

This one day course builds on the foundation of Data Governance I, and dives deeper into selected areas that are designed to provide the most practical and real-world applications of data governance. It includes the change management journey to the “data-driven” organisation, and implications of the necessity of model governance in the context of data science, AI/ML initiatives and RPA/IPA .

Fraud and Anomaly Detection

2021-03-12T03:57:53+00:00February 11th, 2019|Tags: , |

This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the inter-operation of human, rule-based, predictive and outlier-detection methods. Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data. A basic knowledge of R and predictive analytics is advantageous.

Stars, Flakes, Vaults and the Sins of Denormalisation

2021-07-23T01:03:47+00:00May 13th, 2019|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

2021-07-23T00:58:54+00:00May 17th, 2019|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

2021-07-23T01:02:29+00:00May 14th, 2019|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

2021-07-23T01:04:45+00:00May 18th, 2019|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.

Harnessing Mobile Intelligence in an Omni-Channel World

2021-07-16T02:02:26+00:00June 24th, 2019|Tags: |

The proliferation of mobile devices and a new breed of consumers is driving a revolution in requirements for pervasive access to data for marketing analytics. A new breed of consumers has emerged with a DIY (Do It Yourself) mindset related to technology and unprecedented sophistication in using data for personal decisions. This change in consumer behavior is creating demand for consumer intelligence capability in which direct access to data is required for personal decision making. We will discuss the implications of this phenomena for deployment of omni-channel marketing and illustrate leading edge case studies in the delivery of marketing intelligence integrated across all channels.

How to Innovate in the Age of Big Data

2021-07-16T02:09:58+00:00June 24th, 2019|Tags: |

The era of Big Data presents exciting opportunities for leveraging analytics to create competitive advantage and new sources of revenue. To maximize business value, however, an enterprise must innovate in the context of good governance models to avoid technology projects for the sake of technology. In this workshop we describe a framework developed at the Massachusetts Institute of Technology for maximizing business value with the right combination of innovation and governance. We will examine new scenarios for monetising from Big Data sources. We will also explore opportunities for exploiting non-traditional data types from both internal and open data sources.

Advanced Implementation of Big Data Analytics with Graph Processing

2021-07-16T02:17:01+00:00June 24th, 2019|Tags: |

There are a significant number of big data analytics opportunities where graph processing is an effective model of computation for problem solving. In this workshop we present a programming model for implementation of graph algorithms and explain how the execution model works. We also provide example applications in the area of social network analysis, product cross-selling, and fraud detection.

Big Mistakes to Avoid When Performing Big Data Analytics

2021-07-16T05:44:20+00:00June 24th, 2019|Tags: |

Mark Twain, a famous American author, once stated that there are “Lies, Damned Lies, and Statistics.” This phrase is used to describe the persuasive power of numbers, particularly the use of statistics, to lead people to draw incorrect conclusions. This workshop describes the subtle mistakes that can easily be made when interpreting the results from an analytic study or report. We describe logically sound processes for deciphering data using methods designed to illuminate actionable information for data scientists without distracting or misleading the knowledge worker from the relevant facts needed for effective decision-making.

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