Data Warehouse Automation & Real-time Data – Reducing Time to Value in a Distributed Analytical Environment
Smart Infrastructure & Smart Applications for the Smart Business – Infrastructure & Application Performance Monitoring
Enterprise DataOps – Curating Trusted Data as a Service from Data Lake to Data Marketplace (25-26 March 2019, Helsinki)
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. 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 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 and techniques for discovery, analysis and visualisation of multi-structured data
- Text and sentiment analysis
- Scaling text analysis to run on Hadoop, MapReduce 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 GraphX
- Analyse fast data in real-time using streaming analytics
- 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