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 .
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.
This course provides an overview of how to evaluate an enterprise’s digital investment portfolio and then how to enable digital transformation via organisational change. In a context where global disruption is the norm, enterprise agility is more important than ever. To build enterprise agility you need a solid foundation of appropriate organisational structure and governance, as well as financial and human capital and a strong technical platform. This course covers how to evaluate and prepare an enterprise for digital transformation.
This course provides a practical framework for identifying digital change initiatives with enterprise analysis to highlight opportunities where real change is needed within the organisation. The framework covers how to appropriately prioritise these opportunities, identify missing competencies and resource them, and then how to evaluate and propose digital change solutions. The course concludes with how to devise and plan an appropriate implementation strategy and trajectory.
This course presents a process and methods for an agile analytics delivery. Agile Insights reflects the capabilities required by any organisation to develop insights from data and validate potential business value. Content presented describes the process, how it is executed and how it can be deployed as a standard process inside an organisation. The course will also share best practices, highlight potential tripwires to watch out for, as well as roles and resources required.
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.
Blockchain is one of the most disruptive and least understood technologies to emerge over the previous decade. This course gives participants an intuitive understanding of blockchain in both public and private contexts, allowing them to distinguish genuine use cases from hype. We explore public crypto-currencies, smart contracts and consortium chains, interspersing theory with case studies from areas such as financial markets, health care, trade finance, and supply chain. The course does not require a technical background.
This course is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.
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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