Summary

The information about how data is acquired and processed is often as important as the data itself. For this reason metadata management systems are built to track the journey of your business data to aid in analysis, presentation, and compliance. These systems are frequently cumbersome and difficult to maintain, so Octopai was founded to alleviate that burden. In this episode Amnon Drori, CEO and co-founder of Octopai, discusses the business problems he witnessed that led him to starting the company, how their systems are able to provide valuable tools and insights, and the direction that their product will be taking in the future.

Preamble

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Interview

Introduction How did you get involved in the area of data management? What is OctopAI and what was your motivation for founding it? What are some of the types of information that you classify and collect as metadata? Can you talk through the architecture of your platform? What are some of the challenges that are typically faced by metadata management systems? What is involved in deploying your metadata collection agents? Once the metadata has been collected what are some of the ways in which it can be used? What mechanisms do you use to ensure that customer data is segregated?

How do you identify and handle sensitive information during the collection step?

What are some of the most challenging aspects of your technical and business platforms that you have faced? What are some of the plans that you have for OctopAI going forward?

Contact Info

Amnon

LinkedIn @octopai_amnon on Twitter

OctopAI

@OctopaiBI on Twitter Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

OctopAI Metadata Metadata Management Data Integrity CRM (Customer Relationship Management) ERP (Enterprise Resource Planning) Business Intelligence ETL (Extract, Transform, Load) Informatica SAP Data Governance SSIS (SQL Server Integration Services) Vertica Airflow Luigi Oozie GDPR (General Data Privacy Regulation) Root Cause Analysis

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

With BigQuery business and organisations have a unique chance of taking there analytics data and start the transformation towards a data lake. By combining customer, analytics, marketing and CRM data here we not only get a repository where can have room to add or work with data as we see fit, we also open up for the opportunity to use machine learning to actually sift through our data to help determine the causality and relationship between the individual data points. This way we use the full power of data to define our segments and profiles based on their actual behavior and not our prejudice.

Nowadays businesses have too much customer data for a human to handle. Sifting through heaps of it manually trying to find insights is not only gruelling, sometimes it's no longer possible. Solution: let the self-learning algorithms find patterns in data that yield business value. We'll show how a major online retailer, a DIY store and an airline booking service cooked a conversion prediction system out of their various sources of data – CRM, web, and app. This won't hurt, almost anyone can do it – and we'll demostrate how.

In this podcast, Henry Eckerson and Stephen Smith discuss the movement to operationalize data science.

Smith is a well-respected expert in the fields of data science, predictive analytics and their application in the education, pharmaceutical, healthcare, telecom and finance industries. He co-founded and served as CEO of G7 Research LLC and the Optas Corporation which provided the leading CRM / Marketing Automation solution in the pharmaceutical and healthcare industries.

Smith has published journal articles in the fields of data mining, machine learning, parallel supercomputing, text understanding, and simulated evolution. He has published two books through McGraw-Hill on big data and analytics and holds several patents in the fields of educational technology, big data analytics, and machine learning. He holds a BS in Electrical Engineering from MIT and an MS in Applied Sciences from Harvard University. He is currently the research director of data science at Eckerson Group.

In this session, John Young, Chief Analytics Officer, Epsilon Data Management, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey to Chief Analytics Officer, life @ Epsilon, and discussed some challenges/opportunities faced by data-driven organizations, its executives and shared some best practices.

Timeline: 2:51 What's Epsilon? 5:12 John's journey. 9:24 The role of CAO in Epsilon. 12:12 How much John's role is in facing and out facing. 13:19 Best practices in data analytics at Epsilon. 16:15 Demarcating CDO and CAO. 19:52 Depth and breadth of decision making at Epsilon. 25:00 Dealing with clients of Epsilon. 28:48 Best data practices for businesses. 34:39 Build or buy data? 37:21 Creating a center of excellence with data. 40:01 Building a data team. 43:45 Tips for aspiring data analytics executives. 46:05 Art of doing business and science of doing business. 48:31 Closing remarks.

Podcast link: https://futureofdata.org/analyticsweek-leadership-podcast-with-john-young-epsilon-data-management/

Here's John's Bio: Mr. Young has general management responsibilities for the 150+ member Analytic Consulting Group at Epsilon. His responsibilities also include design and consultation on various database marketing analytic engagements, including predictive modeling, segmentation, measurement, and profiling. John also brings thought leadership on important marketing topics. John works with companies in numerous industries, including financial services, technology, retail, healthcare, and not-for-profit.

Before joining Epsilon in 1994, Mr. Young was a Marketing Research Manager at Digitas, a Market Research Manager at Citizens Bank, Research Manager at the AICPA, and an Assistant Economist at the Federal Reserve Bank of Kansas City.

Mr. Young has presented at numerous conferences, including NCDM Winter and Summer, DMA Annual, DMA Marketing Analytics, LIMRA Big Data Analytics, and Epsilon’s Client Symposiums. He has published in DM News, CRM Magazine’s Viewpoints, Chief Marketer, Loyalty 360, Colloquy, and serves on the advisory board of the DMA’s Analytics Community.

Mr. Young holds a B.S. and M.S. in Economics from Colorado State University, Fort Collins, Colorado.

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

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Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Learn SAP's real estate management integrated solution to effectively manage the real estate portfolio at your organization. You will configure SAP REFX for business scenarios covering solutions from master data to financial posting and reporting. You will address all phases of the real estate life cycle, including real estate acquisition or disposal, portfolio management, and property and technical management. To succeed in today's global and highly competitive economy, asset optimization in real estate management has become a strategic task. Organizations need to ensure insight into their property portfolio to make informed decisions, improve portfolio performance, and reduce compliance costs. Sophisticated solutions are needed to manage changing consumer demands and the global workforce as well as information management, compliance adherence, and leasing and property management. SAP Flexible Real Estate Management by Daithankar is a full-featured book that integrates REFX with Controlling (CO), Plant and Maintenance (PM), CRM, SAP AA (ssset accounting), and SAP PS (project systems). You will refer to real-world, practical examples to illustrate configuration concepts and processes, and learn in an interactive, hands-on way through the use of screenshots, menu paths, and transaction codes throughout the book. What You Will Learn: Understand the SAP REFX Solutions landscape and industry best practices for SAP REFX implementation Configure SAP REFX Integrate REFX with other modules Understand how processes are supported by SAP REFX Who This Book Is For: CIOs/CEOs of organizations with real estate portfolios, SAP REFX purchasing decision makers, SAP REFX pre-sales teams, SAP REFX implementation/AMS consultants

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Jos and Oliver will show a series of live demos of how to use Google Analytics and AdWords to the max - to track across devices as well as websites, physical stores, CRM and BI systems and how to best use these integrated customer data sets for bidding, creative decisions and ads targeting.

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

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Master Oracle Fusion Applications Design and Personalization Deliver highly adaptable business applications that bolster productivity and drive informed decision-making. Oracle Fusion Applications Development and Extensibility Handbook contains best practices, real-world case studies, and technical deep dives. Discover how to manage design- and run-time customizations, extend existing UIs and build new ones, secure your applications, and integrate with other systems. This Oracle Press guide offers complete coverage of the latest cloud and SOA-based features. Explore Oracle Fusion Applications components and architecture Plan, develop, debug, and deploy customizations Extend out-of-the-box functionality with Oracle JDeveloper Modify web applications using Oracle Composer Incorporate Oracle SOA Suite 11 g composites Validate code through sandboxes and test environments Secure data using authorization, authentication, and encryption Design and distribute personalized BI reports Automate jobs with Oracle Enterprise Scheduler Change appearance and branding of your applications with the Oracle ADF Skin Editor Extend and customize CRM with Application Composer

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This book is part of the SAS Press program.

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Updates to the second edition include four new chapters in Part 4, Chapters 13-16, that introduce new and advanced analytic techniques that can be valuable in many customer segmentation applications. In addition, Chapter 9 has a new section on using the Imputation node in SAS Enterprise Miner to accomplish missing data imputation, compared to PROC MI used in earlier sections of Chapter 9. Also included are business insights and motivations for selection settings and analytical decisions on many of the examples included in this second edition.

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This book is part of the SAS Press program.

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