Analytics and Data Summit 2018

Join Oracle Engineering to hear real world use cases of how Oracle Customers have utilized Data Integration Platform Cloud – Governance Edition, DIPC-GE, as the foundation for data governance across their big data enterprise. This will include use case patterns, known pitfalls, as well as tips and tricks for future proofing your data governance investment. Oracle Engineering will review how our customers have utilized DIPC-GE to provide a proactive, trusted big data platform, which enables collaboration and enforces compliance across the enterprise. At the core of DIPC-GE is the data catalog which is used to create comprehensive catalog of data entities as well as business terms. The data catalog enables many advanced use cases within data governance, such as; data classification, data quality insights, data profiling, data lineage, impact analysis, AI/ML linkage of data assets to a business glossary, all with rich collaboration and built in flexible workflows and auditing. Join us!

You have databases you want to move to the Oracle Database Cloud? There are several ways to complete such a migration - but which one is the best for you? We'll showcase the different paths, tell you how to choose the right method for your situation, and give you straight forward step-by-step instructions to ensure your successful and simple migration to the Oracle Database Cloud. This will include specific client use cases, migration scenarios, and benefits of migrating to Oracle Database Exadata Cloud Machine. In addition, we show how to lift and shift onto Exadata Cloud Service using the Database Consolidation Advisor.

This is a real world use case from Grupo Bimbo, a 15B$ 32-country FMCG enterprise, where we present (a) how to create a Data lake on DBCS (a PaaS offering) from a SaaS application like Oracle ERP Fusion and (b) how to automate the extraction from Fusion, Ingestion into Data Lake both with full and incremental load and (c) performing advanced analytics in BDCS to support cross-functional analysis spanning multiple countries and products. Attendees will learn step-by-step how to achieve this – a must requirement from a load of Oracle Cloud SaaS customer.

While AI/ML has matured enough to solve many real-world problems effectively, this process typically requires close interactions between three separate skill sets – the domain expert, the data scientist and the programmer. A typical IoT user is a line-of-business person such as a Plant Supervisor or a Fleet Manager. AutoML enables automatic selection of the most appropriate algorithm that suits the domain problem learned based on the interactions with domain expert. Also, AutoML automatically selects the hyper parameters and tunes the models without requiring data scientist skills. Furthermore, AutoML automates the repetitive analytics needed for retraining models warranted by the high velocity IoT data. Therefore, AutoML is the key enabler of analytics in the industrial IoT. Since the typical business benefits in IoT involve significant savings, IoT is the killer use case for AutoML. We will present how Oracle IoT Cloud applies AutoML for industrial IoT problems.