Big Data

Training Courses, Workshops and Seminars

big data – extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. These data sets are typically growing exponentially.
much IT investment is going towards managing and maintaining big data

Big data training is around methods, software and platforms that provide ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Data Science and Big Data Analytics: Leveraging Best Practices and Avoiding Pitfalls

2021-07-23T01:00:38+00:00May 13th, 2019|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

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.

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.

Smart Cities Using Big Data

2021-07-19T01:46:01+00:00June 24th, 2019|Tags: |

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 such as energy consumption, health care, education, and law enforcement. The true potential of big data is directly engaging society in governance through increased transparency and delivery of services using access to data as a foundation. This workshop will provide mini-case studies in a range of settings from developed countries such as the USA and developing countries such as Pakistan. An emphasis of the discussion will be on how city governments can use data to improve quality of life for their citizens.

Innovating with Best Practices to Modernise Delivery Architecture and Governance

2021-07-23T01:01:04+00:00May 20th, 2019|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.

Modernising Your Data Warehouse and Analytic Ecosystem

2021-07-23T01:01:37+00:00May 20th, 2019|Tags: , , , , |

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. 

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