Analytics and Data Summit 2018

If you’re like most people shifting to a cloud strategy, your learning curve is steep, and even the basics such as migrating data to the cloud are worrisome. Whether megabytes or petabytes, you need your cloud migration to be an efficient process - continuous, requiring no downtime, and seamless without data loss. The long delays inherent with traditional migration methods, the need for coordinated ingestion across multiple environments and various geolocations, stale, vulnerable, or inconsistent data often leave projects stuck in the starting gate. Even worse, when your business’s data is driving competitive advantage, unless you can be sure of the availability of your data, you and your business are at serious risk. Now it is possible to have guaranteed consistent data everywhere, even when data is growing and changing. This presentation will explain ‘LIVE DATA’. And present architecture and solutions that show how a ‘LIVE DATA strategy’ allows you to surmount common and sometimes scary cloud use-cases across a wide variety of industry scenarios.

Oracle recently expanded its Data Management Cloud portfolio with the addition of the Oracle Autonomous Data Warehouse Cloud Service to help customers and partners with the deployment of data-mart or data warehouse environments to gain faster insights from their critical business data. Oracle Autonomous Data Warehouse Cloud is the first of many cloud services built on the “self-driving” Oracle Autonomous Cloud Database technology. This service is integrated with applied machine learning to deliver unmatched high performance, reliability, and instant elasticity.
Oracle Autonomous Data Warehouse Cloud uses the same Oracle Database software and technology found in existing on-premises data warehouse deployment, and is compatible with Oracle’s in-database machine learning algorithms, analytics and BI tools, as well as popular 3rd party offerings. Oracle Autonomous Data Warehouse Cloud is simple to deploy, enabling traditional data mart environments or new monetization use cases.\

Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.

Cloud services change the way we provision, deploy and perform analytic application development. They can simplify the development and deployment experience and make it possible for developers and data scientists to use analytics in which they had little or no expertise.  

Two new planned cloud services from Oracle for spatial and graph analysis offer low-code and no-code capabilities. The spatial cloud services include no-code spatial analysis, automated geo-enrichment of data, visual map analysis, and simplified spatial application creation for analysts and developers. The graph cloud services provide an analyst and developer studio with dozens of prebuilt, low-code graph algorithms with wizards to automate modeling, loading, and visualizing graph data and analysis results.

These mapping and graph analysis services offer over hundreds of geospatial and graph analysis functions.  Data access will be available through rich APIs, along with SQL-based spatial and graph query languages.

Modern global supply chains are highly complex ecosystems. Despite decades of innovations to streamline and optimize operations, companies still consistently face challenges with end-to-end visibility, product tracking, bootlegging, fraud, compliance, safety, and delivery speed. Supply chain-related crimes and disruptions cost companies billions of dollars annually across the globe.

This session showcases how blockchain, IoT and machine learning can be harnessed to fight some of the key supply chain risks and challenges of today.  When implemented right, this solution improves customer trust, minimizes losses and optimizes end to end operations. The session will cover the use cases, technical architecture and implementation considerations.

In this session learn about Oracle's soon to be released prepackaged "SaaS on PaaS" cloud applications called "Adaptive Intelligent Applications for Manufacturing". This product is built directly on top of several Oracle’s PaaS components to collect, store, and analyze massive amounts of Operational Technology (OT) data coming from shopfloor systems such as MES, quality systems, LIMS, Equipment, Test stations and contextualizes this data with Information Technology (IT) data residing in HCM/CRM/SCM/ERP applications in a single consolidated operational data store and analyzed by applying advanced analytics, data mining/data science, and AI techniques to discover key patterns and correlations from historical data related to manufacturing efficiencies and also provide actionable predictive analytics to improve yield, reduce defects, reduce scrap, minimize cycle times, manufacturing costs, and equipment downtime.