Today, with most people connected to the Internet, the power of the customer is almost limitless. The Internet has given them freedom to choose in a way that business could never have imagined. They can browse your competitors’ web sites with ease. They can compare prices, they can view sentiment about your business, and they can switch loyalty in a single click any time anywhere all from a mobile device. In addition, the emergence of social media sites means that customers also have a voice. They can express opinion and sentiment about products and brands on Twitter, Facebook, and review web sites and create social networks by attracting followers and following others.
For many CEOs, customer retention, loyalty, service and growth are top of their agenda. In addition, improving operational effectiveness is also high on their priority list. The only way they can achieve this is to acquire more data and use AI to help make this possible. CMOs also want access to new data to enrich what they already know about customers. New data is needed to provide insight on customer on-line behaviour for better segmentation and to understand the value of a customers’ social network and not just the customer. In addition, COOs want more data to become more effective in operations. Instrumentation is therefore being added so that operations can capture new data. With etc. so much demand we are now in an era where data has never before been so important to business in helping to create competitive advantage.
This new 2-day seminar looks at the need to capture new data sources to add to what we already know and use machine learning to automatically discover, profile and catalog what is in these data sources. It then looks at how machine learning and advanced analytical techniques, such as text analyses, sentiment analysis, graph and streaming analytics, can be used at scale on big data to provide new insight that helps foster growth, reduce costs and improve effectiveness for competitive advantage.
Business Analysts, data scientists, BI Managers, data warehousing professionals, enterprise architects, data architects CIO’s, IT Managers
Attendees to this seminar will learn:
• How data and analytical characteristics can dictate the approach taken and tools needed to conduct exploratory analytics
• How to develop analytical models using supervised and unsupervised machine learning
• How to develop machine learning models at scale on Apache Spark and Hadoop
• Tools for building machine learning models
• Tools for deploying, monitoring and re-training machine learning models • Tools and techniques for discovery, analysis and visualisation of multi-structured data
• Text and sentiment analysis
• Scaling text analysis to run on Hadoop and Spark
• Clickstream analysis
• Graph analysis – 4 graph analytical techniques to identify shortest path, analyse connectivity, identify communities, determine influencers and important people in social networks, etc.
• Scale graph analysis on Apache Spark
• Analyse fast data in real-time using streaming analytics
• Deep learning with multi-layer neural networks
• Leverage machine learning and advanced analytics quickly and easily from self-service BI reports and dashboards for access over the web and on mobile devices
MODULE 1: AN INTRODUCTION TO DATA EXPLORATION, DISCOVERY AND VISUALISATION
This session introduces data discovery and visualisation and looks at why businesses now need
MODULE 2: GETTING STARTED WITH PREDICTIVE ANALYTICS AND MACHINE LEARNING
As we move into the era of smart business, looking back in time is not enough to make good decisions. Companies have to also model the future to forecast and predict so that they can anticipate problems and act in a timely manner to compete. Predictive analytics is therefore a key part of any BI initiative and should be integrated into analysis, reporting and dashboards. This session introduces predictive analytics and shows how it can be used in analysis and in business optimisation
MODULE 3: ADVANCED ANALYTICS FOR MULTI-STRUCTURED DATA
This session looks at emerging analytical technologies for multi-structured data and explores how you can use them to improve business insight. Not all analytical projects are implemented using relational database technology, especially when it comes to very large data volumes with unstructured content, semi-structured JSON or XML data, sensor data, and clickstream. This session looks at the emergence of advanced analytics using Big Data NoSQL Platforms like Spark and Cloud storage or Hadoop. It looks at the approaches to analysing complex unstructured and social content and the challenges of creating valuable business insight from multiple sources of unstructured content.
MODULE 4: SEARCH, BI & BIG DATA
This session will examine the growing role of search in an analytical environment both as an information consumer tool for self- service BI and as a way of analysing both structured and unstructured data. Search has been incorporated into BI tools for some time, but with the emergence of Big Data as a platform for analysing unstructured information, it is taking on a major new role. Search is a simple mechanism that is familiar to most people and opening up the interactive use of BI via search can have enormous business benefits. Search can be used to grow the use of BI to a much wider group of users and also provide a way to extract additional insight from unstructured content. Topics that will be covered include:
MODULE 5: DEPLOYING AND USING SELF- SERVICE BI TOOLS
Self-service BI tools are frequently sold into business departments so that local business analysts can build their own BI reports, dashboards and applications and do ad-hoc analysis without having to wait for IT. This session looks at how to maximise business benefit of machine learning by integrating self-service BI tools with predictive and advanced analytics deployed in containers, in-database, in-Hadoop, in-Spark and in- streaming analytics platforms to leverage on-demand and event-driven analytics at scale. It also looks at OLAP on Hadoop to enable scalable multi-dimensional analysis.
Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an independent analyst and consultant he specialises in data management and analytics. With over 38 years of IT experience, Mike has consulted for dozens of companies. He has spoken at events all over the world and written numerous articles.
Mike is Chairman of Big Data LDN – the fastest growing Big Data conference in Europe, and chairman of the CDO Exchange. Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of Database Associates. He teaches popular master classes in Analytics, Big Data, Data Governance & MDM, Data Warehouse Modernisation and Data Lake operations.