This two day course provides an informed, realistic and comprehensive foundation for establishing best practice data governance in your organisation. Suitable for every level from CDO to executive to data steward, this highly practical course will equip you with the tools and strategies needed to successfully create and implement a data governance strategy and roadmap.
The Data-Driven Decision-Making course is for executives and managers who want to leverage analytics to support their most vital decisions and enable better decision-making at the highest levels. It empowers senior executives with skills to make more effective use of data analytics. It covers contexts including strategic decision-making and shows attendees ways to use data to make better decisions. Attendees will learn how to receive, understand and make decisions from a range of analytics methods, including visualisation and dashboards. They will also be taught to work with analysts as effective customers.
Many people today have been developed emotionally and mentally for an era that no longer really exists. This has created a critical soft-skills gap between current workforce ability and business requirements today. In this course participants learn to ‘readapt’ their soft skills so that they are aligned with a thriving 21st century business. They are also given a simple framework from which to continue the self-development so that the training instigates sustainable change.
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 .
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.
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.
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.
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.
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.
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.
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