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 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 [...]
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 [...]
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 [...]
The use of Big Data is a lynch pin in the deployment of smart cities of the future. The potential of Big Data goes far beyond the efficiencies that can be created in areas [...]
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.
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.
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.
This full-day workshop examines the emergence of new trends in data warehouse implementation and the deployment of analytic ecosystems. We will discuss new platform technologies such as columnar databases, in-memory computing, and cloud-based infrastructure deployment. We will also examine the concept of a “logical” data warehouse – including and ecosystem of both commercial and open source technologies. Real-time analytics and in-database analytics will also be covered. 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 using AI and machine learning.
Optimiser choices in determining the execution plan for complex queries is a dominant factor in the performance delivery for a data foundation environment. The goal of this workshop is to de-mystify the inner workings of cost-based optimisation for complex query workloads. We will discuss the differences between rule-based optimisation and cost-based optimisation with a focus on how a cost-based optimization enumerates and selects among possible execution plans for a complex query. The influences of parallelism and hardware configuration on plan selection will be discussed along with the importance of data demographics. Advanced statistics collection is discussed as the foundational input for decision-making within the cost-based optimiser. Performance characteristics and optimiser selection among different join and indexing opportunities will also be discussed with examples. The inner workings of the query re-write engine will be described along with the performance implications of various re-write strategies.
Big Data exploitation has the potential to revolutionise the analytic value proposition for organisations that are able to successfully harness these capabilities. However, the architectural components necessary for success in Big Data analytics are different than those used in traditional data warehousing. This workshop will provide a framework for Big Data exploitation along with recommendations for architectural deployment of Big Data solutions.
Social networking via Web 2.0 applications such as LinkedIn and Facebook has created huge interest in understanding the connections between individuals to predict patterns of churn, influencers related to early adoption of new products and services, successful pricing strategies for certain kinds of services, and customer segmentation. We will explain how to use these advanced analytic techniques with mini case studies across a wide range of industries including telecommunications, financial services, health care, retailing, and government agencies.
Real-time analytics is rapidly changing the landscape for deployment of decision support capability. The challenges of supporting extreme service levels in the areas of performance, availability, and data freshness demand new methods for data warehouse construction. Particular attention is paid to architectural topologies for successful implementation and the role of frameworks for Microservices deployment. In this workshop we will discuss evolution of data warehousing technology and new methods for meeting the associated service levels with each stage of evolution.