An Introduction to Data Exploration, Discovery And Visualisation
This session introduces the relatively area of data discovery and visualisation and looks at why businesses now need
• New data sources – Structured versus multi-structured data
• What are the different analytical workloads that dictate the need for data discovery and visualisation?
• The data discovery and visualisation process
• What is exploratory analysis?
• What is Data Discovery and Visualisation?
• Why do businesses need this new capability? – Example use cases
• Skills required for Data Discovery and Visualisation
• Types of Data Discovery and Visualisation tools?
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 a therefore a key part of any BI initiative and should be integrated into analysis, reporting and dashboards. This session introduces predictive analytics and how shows how it can be used in analysis and in business optimisation
• What is predictive analytics?
• Technologies and methodologies developing predictive analytical models
• Using supervised learning to develop predictive models for automatic classification
• Popular predictive algorithms, e.g. Linear regression, decision trees, random forest, neural networks
• Implementing in-Hadoop, in-memory analytics using Spark and SAS LASR server
• Data Science Workbooks using Databricks Cloud and Apache Zeppelin
• Accessing data in HDFS using SQL to build models
• Accessing in Hadoop machine learning algorithms from data mining tools
• Deploying predictive analytical models in analytical databases and in Hadoop
• Integrating predictive analytics with event stream processing for automated analysis of high velocity events in every-day business operations
• Accessing predictive analytics from self-service BI tools and spread sheets
• Clustering data using unsupervised learning algorithms
Exploratory 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, sensor data, and clickstreams.
This session looks at the emergence of big data analytics using NoSQL Platforms like 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.
• Techniques for producing insight from unstructured content
• Tools and techniques for analysing text
• Voice of the customer and social Media analytics
• Examples of content analytics products in the marketplace
• Clickstream analysis
• Streaming analytics
• Graph analysis
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:
• Why Search and BI?
• The growing importance of analysing unstructured content
• The implications of Big Data on search and BI
• Creating search indexes on multi-structured data
• Building dashboards and reports on top of search engine indexed content
• Using search to analyse multi-structured data
• The integration of search with traditional BI platforms
• Using Search to find BI content and metrics
• Guided analysis using multi-faceted search
• The search based analytical tools marketplace: Apache Solr (Lucene), Attivio, Connexica, HP IDOL, IBI WebFocus Magnify, IBM Watson Explorer, Microsoft, Oracle Endeca, Quid, SAP Lumira, Splunk
Deploying and Using Data Discovery and Visualisation Tools
Data Discovery and Visualisation tools are frequently sold into business departments so that local business analysts can start building their own BI applications without having to wait for IT. These new tools offer the attraction of agile development and much faster time to value. When business areas buy them it often means that development starts without any IT guidance and quickly spreads to other parts of the business with little thought for integration or re-use. The result is that inconsistency and chaos can quickly set in. This session looks at best practices in deploying Data Discovery and Visualisation tools to maximise business benefit through data management, re-use and integration with existing BI/DW environments to facilitate consistency
• The Data Discovery and Visualisation tools marketplace – Tableau, SAS Visual Analytics, SAP Lumira, Platfora, MicroStrategy Visual Intelligence, Qlik, Information Builders WebFOCUS Visual Discovery, etc.
• Key features of data discovery and visualisation tools
• Automated charting, visual exploration and analysis and advanced visualisation
• Automated data discover versus manual data discovery
• Outside-in Versus Inside-out BI application development
• Personal and team based self-service development
• Key requirements for successful self-service BI development using data discovery and visualisation
• Best practice steps in deploying self-service BI applications
◦ Simplifying data access and understanding via data management, data governance and information services
◦ Removing complexity of data access using data virtualisation
◦ Steps to developing self-service BI applications
◦ Types of self-service analytical processing
◦ Using templates and components for rapid self-service BI application development
◦ Ensuring aggregate consistency
◦ Prototyping and bookmarking valuable insight
◦ Simplifying information delivery and making content easy-to-consume
◦ Building report components and dashboards
◦ Publishing dashboards and self-service BI applications for business use
◦ Handing over self-service applications to IT for hardening
◦ Securing access to dashboard based self-service BI applications