Big data and data science can provide a significant path to value for organizations.  These technologies, methodologies, and skills can help organizations gain additional insight about their customers and operations; they can help make organization more efficient, be a new source of revenue, and can make organizations more competitive.  Organizations are more often making use of more advanced analytics as part of this evolution.  This includes making use of machine learning for predictive analytics to better understand and predict customer behavior.  It includes analyzing more data in real-time to take action on analytics.  The use cases are wide and varied.

As part of this evolution is also the move to utilizing open source technologies.  Open source provides a large community for innovation and some powerful tools for big data.  Projects such as Spark are gaining attention for its in-memory processing engine that is known for its speed as well as its sophisticated analytics library and support for streaming.
In this talk, Fern Halper will provide some insight about the market move to adopt big data and open source and how organizations can build a path to value.  Topics include:

• Big data, real time use cases for advanced analytics
• Open source and Spark as an environment for advanced analytics
• Market opinions of open source technologies
• Building a strategy for success

Ka'ching. The ultimate customer interaction with an enterprise is the almighty transaction. That is the very moment when customers' say "yes, let's do business". It’s also the very moment that enterprises have the opportunity to influence the value of that and future transactions. Enter data, analytics and real-time predictive models to influence customer experience. That's why 90% of enterprises surveyed by Forrester Consulting, and commissioned by IBM, say they have implemented, expanded, or plan to implement predictive systems.

Apache Spark has emerged as a game-changer in the area of advanced predictive analytics. IBM z Systems offer an enterprise-grade Apache Spark running natively on z/OS. It provides data abstraction and virtualization to enable efficient access to a broad set of structured and unstructured data sources through Spark APIs. This allows analyzing data in place, securely and at scale, while avoiding costly data movement between servers. This session provides an architecture overview with a demo highlighting a client insight use case for financial institutions.

In this information age, there is greater emphasis by organizations to use data and analytics for offering better products, services and exception experience to their customers. The most effective way to manage these expectations is to gain “timely” insights from immediately "actionable" data to the businesses and make a competitive difference.

In this talk, DataFactZ showcases the potential of real time analytics on transactional data in various industry verticals. For example: Detecting credit card fraud in real time. We will also explore answers to questions such as: How would businesses react to product lifecycles that are quickly vanishing? Can they manage inventory turnover which needs to be fluid in this competitive market? And, how can we apply science to manage these dynamic situations? It will be revealed in real time analytics.