Analytics and Data Summit 2018

Dan Vlamis will introduce Analytics and Data Summit 2018 and Spatial Summit leaders will introduce the Spatial Summit during opening talks on Tuesday.

Oracle Autonomous Data Warehouse Cloud is the industry’s first self-driving, self-securing, self-repairing database cloud service, offering you high availability, performance and security without human intervention. Oracle Autonomous Analytics Cloud is the industry’s leading cloud analytics platform, empowering you to achieve broader, deeper, and faster insights with augmented analytics in a hands-free platform. Join Oracle executives Andy Mendelsohn and Waqar Hasan to hear how Oracle’s new Cloud services leverage AI and machine learning to redefine the state-of-the-art for data warehousing and analysis, so you can ask any question of any data and get better answers faster

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.\

This session answers architecture questions related to the overall structure and strategy for an enterprise analytics system. How do we design a data warehouse for analytics purposes? What do we integrate into a central warehouse or maintain in separate data marts? How do we integrate long term storage systems that we need to query only occasionally? Data lakes? Big Data?? What are the tradeoffs with different tools and how much do we invest in custom code? How do we integrate real-time data streaming? What ETL and integration strategies are we going to use? Where and how do we extend our raw data with calculated measures and analytic functions? How do we create a system that has a strong foundation, but can grow and evolve over time? This presentation will draw from real-world data architecture projects that were designed with analytics in mind.

Join us for our lightning round presentations by our sponsors. These are about 4-minute presentations by each sponsor, moving quickly between sponsors. Come have lunch with us! You can also download each of the sponsors' presentations.

Situation Awareness (SA) is arguably the best method to extract the highest value from data;  Oracle's KIDS (Knowledge Intensive Data-management Services) is a particularly efficient model for achieving SA.  The Multivariate State Estimation Technique (MSET) is an advanced prognostic pattern recognition method that was originally developed in the 1990's for high-sensitivity prognostic applications in complex safety-critical industries by discovering anomalies with the provably lowest-possible false positives/negatives, and with low resource consumption.  Oracle's new MSET-2 combined with SA solves issues that confuse humans in the loop, and confound the best conventional ML algorithmics. The combination of Oracle MSET-2 and SA achieves a compelling value proposition for Big Data life sciences and healthcare applications, as well as for newly emerging dense-sensor IoT applications and human-in-the-loop supervisory-control applications.

Long gone are the days when mass mails with the same message and offer were sent to all customers. With the amount of data companies have on their customers together with tools such as Oracle Advanced Analytics a new world of possibilities for personalized and proactive marketing opens up. A company with customer data in an existing datawarehouse built on Oracle databases have a short time to market for an advanced analytics platform using the licensed option of Oracle Advanced Analytics. In this session I will go through the learnings along the way from being reactive to proactive using the whole offering of R together with Oracle Advanced Analytics to empower data scientists and analysts to generate cutting edge customer insights inside the database.

This session will focus on the built-in capabilities of Oracle Analytics Cloud and OBIEE 12c to do predictive analytics and machine learning. Clustering, forecasting, anomaly detection are all available as single-click built-in functions using R. We’ll show not only the business use cases for these functions, but also how to extend and interpret the results sets from advanced analytic algorithms. We’ll cover how to leverage the power of R (the world’s most popular programing language for advanced analytics) through Oracle’s packages and through the thousands of open source packages available. We’ll also show the latest capabilities for building and scoring data using machine learning models in OAC and OBIEE 12c.

This presentation focuses on unlocking the power of Data Science for data-driven decision support. It details the pragmatics of using Machine Learning techniques in OBIEE for actionable BI and Predictive Analytics from a business standpoint. Two industry use cases are presented to illustrate the implementation of the same using OBIEE 12c.

The world of business analytics is rapidly changing. Technology is focused on bringing more and more functionality to end users and automating or obscuring many of the tasks that were the cornerstone of a number of technology careers. As the skills that were in extremely high demand 5 or 10 years ago become less valued in the marketplace what skills should you focus your efforts on today, that take advantage of the skills you already have, to continue to advance your career as a technologist? 
In this panel discussion thought leaders from the analytics industry will exchange views on the changing landscape of business analytics and technology and share their impressions of the current and future state of the industry. The panel, with participation from attendees, will try to separate the hype from reality about what skills really matter and how to stay relevant and advance your career in the ever-changing world of analytics.

Real life Manufacturing Data is not like iris or titanic. We cannot just .fit() and .score(). We need to effectively .transform() the data. In this presentation, we will look at the complexity of manufacturing data and various ways to feature engineer manufacturing data to derive meaningful variables to solve manufacturing problems. Specifically we will look at a. Methods to construct machine learning data set for different type of Manufacturing like Lot Controlled, Serial Controlled, Batch Manufacturing, Discrete Manufacturing b. Transformation method of time series data from Machine Sensor Readings c. Various binning transformations on transaction data

This session will cover a real world example of multiple Oracle products being used for data projects and marketing campaigns across an enterprise level business vertical. 

A major national partner in the telecom space - with 600 retail locations and 8 million customers - needed comprehensive analytic help which required the following cloud based Oracle products:
1) OCI - Bare Metal - Housing the entire US consumer database of 260 million records with 100's of household attributes
2) OAC - Geo-spatial audit of their retail trade area currently used for prospect canvassing
3) OAC - Using Advanced Analytics to create extensive profiles/personas of who was buying their product
4) OAC - Using advanced modeling techniques to build predictive audience files to power new customer acquisition programs
5) ODC and OnRamp Services - Pushing millions of prospect and customer records into Onramp Services for use in OMC
6) OMC - Monitor existing customer buying cycles for renewal and cross-sell opportunities - and suppression against new prospect audiences
7) OMC - Monitor behavorial triggers inside BlueKai DMP for optimal promotion of client brand
8) OAC - Effective post campaign reporting with ROI and other KPI’s used for better attribution of marketing spend.

Graph analysis is an effective methodology in data analysis which considers fine-grained relationships between data entities. This session presents an example of anomaly detection using graph analysis on data stored in Oracle Database with Oracle R Enterprise and the OAAgraph package, using a real-world public data set.
Specifically, we analyze the public information of medical transactions from CMS (United States Center for Medicare and Medicaid Services) for the year 2012. We load this data set as a graph into Oracle Database, and use the OAAgraph package to apply graph analysis algorithms whose results are be visualized using R's ggplot2 visualization package. 
Our analysis successfully identifies suspicious records in the data set -- medical providers who perform procedures far from their specialties. We also discuss techniques how to reduce false positive outcomes. 

Oracle Marketing Advanced Analytics team has been using Oracle R Enterprise(ORE) for propensity to buy modeling for the past few years. We have recently expanded our use of ORE to event registration. The models allow Oracle to target the right contacts for email marketing for product events. We have shown up to 4X increase in registration rates from model targets over the baseline. The presentation will cover an overview of the results of our modeling as well as some key insights we have learned on how to model using ORE.

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.