Can Advanced Analytics Improve Manufacturing Quality?
|
|
- Maximilian Dennis
- 6 years ago
- Views:
Transcription
1 Can Advanced Analytics Improve Manufacturing Quality? Erica Pettigrew BA Practice Director (513) Ext. 210 Jeffrey Anderson Sr. Solution Strategist (513) Ext. 260 April 13,
2 OVERVIEW 15 Years experience with Fortune 100 CPG/FMCG Specialize in Business-centric, Enterpriselevel solutions, leveraging a variety of tools and technologies Focus on Partnership, Value, and Excellence, resulting in long-standing relationships with our Customers OUR KEY SERVICE AREAS & SOLUTIONS Business Analytics & Advanced Analytics Customer Relationship Management (CRM) Business Process Automation & Reinvention Collaboration and Content Management Systems (CMS) Custom Development Cloud & Mobile Computing Managed Services 2
3 Today s Agenda Complex Event Analytics Background: Complex Event Processing (CEP) and Complex Event Analytics (CEA) Project Management Approach for Discovery Projects Vertex Case Study: Steel Manufacturing One-Stop Data Shop Advanced Analytics Topics Quality Indicators: Signal vs. Noise Research Coil Quality Score Key Points & Takeaways 3
4 Background: Complex Event Processing (CEP) What is it & What can it do for me? 4
5 COMPLEX EVENT PROCESSING (CEP) The Concept Evaluation of a series of sensor, event, or state information to determine a relationship between events and provide situationally relevant assessments and action recommendations. The Technology Software & systems solutions which systematically relate and associate multiple (potentially disparate) events and data elements, deriving and applying context for business evaluation and action. Key Goals & Objectives: CONTEXT AWARENESS Enable Right-Time, Contextually Aware data understanding across multiple streams of information and inputs. CONNECTEDNESS Connect Systems and Audiences On a Single View of the Data & Understanding of Impacts. IMPROVED DECISION MAKING Discover, Identify, and Target challenges and opportunities, to support better business and operations decisions, moving toward data-driven frameworks to improve speed, reduce costs, manage risks, and more. OPTIMIZE IMPACT Create opportunities for impactful interactions with operational systems for maximum operational effectiveness. People are natural CEP processors. We constantly associate, derive, and apply context to the activities and occurrences around us. CEP technology works to automate this activity in a business context and make it actionable. 5
6 Traditional Complex Event Processing Event Flow and Complex Joins The traditional flow for Complex Event Processing is Event Capture, a set of Operations, and a resulting Action. Commercial off-the-shelf CEP products focus on Event Capture, Adapters, and providing interfaces to manually write and maintain business rules (models) for business processes. 6
7 Introduction to Complex Event Analytics (CEA) A Natural Evolution Complex Event Processing Implementing existing business rules with automation Transactional and operational reporting Real-time stream processing Context-awareness Business Intelligence (Traditional) Focus on reporting, information gathering, consolidation, & summarization Data warehouse copy data from transaction, legacy, and other systems onto single platform for reporting and analytics OLAP reporting slice & dice, drilldown to details Complex Event Analytics Discovery and refinement of rules using Data Science before, during, and after implementation Transformative approach with machine learning possibilities Real-time stream processing with threading and pre and post event analysis Business Analytics Range from single-point solutions to solve business challenges and pain points, to New holistic, business driven insights that address transformative challenges Solutions drive value by simplifying the messaging, reducing overhead and latency, building intelligence directly into solutions, and providing opportunity for automated and/or rapid action 7
8 Complex Event Analytics A Stronger Business Approach! An analytics-driven approach to Complex Event processing provides a shift to Datadriven solutions, by incorporating Data Science for Selective and Iterative Event Processing Operations and subsequent Analytic Opportunities. 8
9 BUSSINESS TRANSFORMATION Establishing Complex Event Analytics Moving from Islands of Data to an Intelligent Solution * Discovery Applications each take a lifecycle of their own and continue to bring unique benefits as the applications are applied, science is refined, and findings are iteratively embedded within the processes. Evaluate, Automate, Machine Learning Embed Data Science Thread the Data Discovery Applications * Exploratory Applications, Actions INCREASING ANALYTICS CAPABILITY Thread the Data Connections Connect and thread data; consider establishing contextual data lake for initial and ongoing analysis. Discovery Applications Indicators Discovery apps created / evaluate models offline, post event, and refine (where to look.) Discover initial root cause relationships. Exploratory Applications Actions Detailed root cause analysis, deploy operational system updates, increase velocity and variety of data included in models. Embed Data Science Deploy Deploy intelligent aggregation and threading into CEP system and enable business teams with visualization and predictive analytics tools. Evaluate, Automate, Machine Learning Continuous Improvement Measure models impact against KPIs for business processes. Refine models and consider Machine Learning 9
10 Project Management Approaches Difference Between Discovery and Systems Delivery Why the difference? Initiatives designed to extract information from existing systems or new sources of data must acknowledge how messy and complex that process is. Embed Data Science Evaluate, Automate, Machine Learning Thread the Data Discovery Applications * Exploratory Application, Actions System Driven Project Management: Initiating Planning Executing Monitoring & Controlling Closing Discovery Driven Project Stewardship: Develop theories Build hypotheses Identify relevant data Conduct experiments Refine hypotheses based on findings Repeat 10
11 VERTEX CASE STUDY Complex Event Analytics System Major U.S. Steel Manufacturer (& Recycler) 11
12 Case Study Summary Customer Highlights Highlights Profile Industry leading U.S. Steel Manufacturer Business Challenge Higher than acceptable unidentified late stage quality failures Solution Data Science-driven root cause analysis Threaded, accumulating CEA system Results Unified data view across mills (10+ TB) Advanced Analytic Insights and Applications Overview Identify events and factors affecting quality in the steel roll milling process, with a focus on Root Cause Analysis and Data-Driven Associations. Iteratively extend from current offline, manually intensive research and corrections, to data-driven statistical analysis, followed by online (real-time) monitoring of quality-affecting events that prevent defect propagation and increase quality, predictability, and product delivery confidence. > Establish unified, threaded, plant-wide product-level view of manufactured materials (start to finished good) for ad hoc, application, and advanced analytic consumption > > Explore new analytical methods, tools, and techniques to optimize quality improvements and target corrective actions Provide analytic applications, statistical methods, and visualizations for intuitive, effective plant operations 12
13 The Starting Focus: Two Primary Use Cases Building the Baseline and Eye on the Prize This project initiated with two primary focus points: Ability to view a single item across all mills, evaluate any point in the process (or across a series of processes), for any / all ad hoc uses. Incorporate current and emerging Statistical Methods on contextual information to support a range of use cases, from simplified Root Cause analysis to Predictive alerts. Use Case 1 1-Stop Data Shop Use Case 2 Advanced Analytics Merge and Connect Timestamp-based Item-level data from all mills for analytics consumption. Incorporate Statistical Methods with contextual Start-to-Finish dataset Provide Root Cause & Associative Analysis, Ability to Replay Across Mills Analytics to enable contextual, event-driven discovery, identification, targeting, and optimization. Supports improved decision making and creates opportunities for operational excellence. 13
14 Our Story: Complex Event Analytics for Mfg. The Journey to One-Stop-Data-Shop and Insights Through Advanced Analytics THE FUTURE Extend Analytics to support additional high-value use cases and audiences, ultimately incorporating Real Time Predictive Analytics to further optimize QA processes and improve yield while reducing costs and manufacturing delays. THE ANALYTICS Visualizing data, enabling end users to access the CEA data lake, adding and deriving context, tracing root causes, identifying patterns & outliers. Plant & Mill Operators, Engineers, Metallurgists. PROVING IT WORKS Beginning with two highly complex mills, and progressing to a fully loaded, performant, responsive, effective CEA system linking data from more than a dozen mills and manufacturing processes. DOWN WITH DATA & DESIGN From design to build, a custom solution created to house all key prime datasets in threaded fashion across mills, providing a CEA data lake for analytics. 14
15 The Data Layer: Unifying Entities Stream Processing and Batch Joins A Unified Data Warehouse was established to reconcile the various coil identification schemes and present the entirety of the coil s production history as a single joined view of the entities. A Unified Coil View was created using Accumulating Snapshot technique, incrementally expanding as the data from Melt, Slab, Hot Mill, and Cold Mill is incorporated. This traceable and threaded view, in addition to the atomic data, allows several Advanced Analytics techniques to truly transform the process and obtain valuable insights. 15
16 Solution Metrics Noteworthy Highlights 10+ TB TOTAL SIZE (Data Lake) Summarized sensor readings, organized, threaded and connected by batch and product ID across all mills, from scrap metal start to finished steel coil product. Thousands of attributes HIGH SPEED DATA PERFORMANCE Fact tables between 5-8 Billion Records with over a thousand columns per record. Stored procedures provide complex filtering using any number of combinations. 10+TB 2TB/hr SCALABILITY AND FLEXIBILITY Supports both columnar (relational) view of data, as well as Key Value Pairs optimizing the data layer for specific analytic use cases. VERTEX SOLUTION & VALUE: Flexible, Scalable, Efficient HIGH VALUE ANALYTICS From 2 months research for 2 persons (pre-vertex Complex Event Analytics solution) to 2 hours with a purpose-built application where speed, accuracy, value (and potential) is evident. TECHNOLOGY STACK: Microsoft Centric A scalable big-data responsiveness solution and capability, built within a standard stack ecosystem. 16
17 Quality Indicators: Signal vs. Noise Research OVERVIEW This application provides an Advanced Analytics example for systematically identifying differentiating factors in expansive datasets. Advanced Analytics Showcase analysis and comparison of coil attributes using proven statistical methods. Perform data-driven noise filtering and classification / ranking of attributes. Leverage Unified Coil View for threaded dataset across mills. Conduct Root Cause Analysis. Application Overview Data Selection Pre-filtered/selected data set with Coil ID s and attributes will provide input for Statistical Model. Dimension Reduction Statistical Model relates and evaluates available attributes, comparing Good vs. Bad, and reducing dataset to Top Informative Variables. Charts & Visualizations Evaluate model results with Scatterplots and Spotfire other charts in story-board Root Cause analysis fashion. 17
18 Results: Signal vs. Noise Research Application Separating Signal from Noise (Finding the Most Informative Variables in Large Datasets) BEFORE: Diagram shows k-means clustering using ALL variables Total of 8 clusters Blue marks are Good Coils Black marks are Bad Coils No evident patterns no obvious clustering according to good/bad status IMPACT 2 months 2 people AFTER: Ranked list of 20 most informative variables Variables known to be irrelevant can simply be removed, and analysis rerun. The emergent pattern: At least one group contains bad coils exclusively! 1 Application 2 Hours Repeatable 18
19 Coil Quality Score OVERVIEW Transform feature values into normalized functions of variance. Then combine the transformed values into a single omnibus measure of product quality. Ranking & filtering coils on the basis of this score is the first step to identifying and correcting quality problems related to process variance. Advanced Analytics Data table containing transformed attribute values + combined quality score Interactive table allows user to sort, filter & select coils based on score. Drill-down histogram & correlation plot based on user select/filter Linear regression plots for diagnostics; believability Application Overview Objective Attribute Selection Data Transform Charts & Visualizations Reduce the entire coil profile down to a single number, normalized to lie between zero and 1. Attributes are identified and selected as the basis for computation of the CQS. Many transformations are possible. This instance uses a standard normal (Gaussian) conversion to z-score probabilities. Addition of simple histogram and scatterplot can support drill-down view of CQS predictors. 19
20 Results: Coil Quality Score Summarizes Events Activity Across the Entire Line Then deep dive into questions such as What proportion of today s items were above the 96% mark? What is this month s trend on the total quality index? What effect did last week s installation of a have on the index? What effect did the most recent recipe change have on total quality score? How well does the index predict results from Test Lab and vice versa? What were common profile characteristics among the lowest percentile quality items (last week, last month, etc.)? 20
21 Complex Event Analytics Key Points Highlights & Takeaways Creating a unified view of the data allows for new insights across the entire process though Maintain the atomic data for analysis within the processes itself (and allow for replay and modeling) Embedding Data Science in the process to constantly refine the event processing rules (transformation, not just automation!) Root Cause Analysis Problem Identification Compare & Contrast Replay Trace Across Mills Evaluate Associations Project Management Takeaways Allow time for building domain expertise Be aware of the iterative nature of analytic modeling Account for times to fix data quality issues and other data related issues Worry more about improving business understanding than about deploying technology 21
22 Closing Thought - Don t fumble it! People don t think in a vacuum; they make sense of situations on the basis of their own knowledge, mental models, and experiences. They also use information in different ways, depending on the context. THANK YOU TIME FOR DISCUSSION & QUESTIONS 22
EMC IT Big Data Analytics Journey. Mahmoud Ghanem Sr. Systems Engineer
EMC IT Big Data Analytics Journey Mahmoud Ghanem Sr. Systems Engineer Agenda 1 2 3 4 5 Introduction To Big Data EMC IT Big Data Journey Marketing Science Lab Use Case Technical Benefits Lessons Learned
More informationOptimize your Service Cloud Data Best Practices, Data Extraction and NextGen Reporting
Optimize your Service Cloud Data Best Practices, Data Extraction and NextGen Reporting Kris Nichols - Sr. OSvC Solution Consultant Mehrzad Kootar - BI Solution Consultant Oracle Service Cloud Date: August
More informationQuantifying the Value of Investments in Micro Focus Quality Center Solutions
Dynamic Value Brief Application Delivery Management Quantifying the Value of Investments in Micro Focus Quality Center Solutions Manage software testing and IT quality management with consistent processes
More informationA complete service guide for MICROSOFT DATA ANALYTICS ENABLEMENT
A complete service guide for MICROSOFT DATA ANALYTICS ENABLEMENT Contact us today. 206-747-6930 A brief introduction exclusively for Microsoft employees and customers: In early FY12, Decisive Data decided
More informationIntegrated Social and Enterprise Data = Enhanced Analytics
ORACLE WHITE PAPER, DECEMBER 2013 THE VALUE OF SOCIAL DATA Integrated Social and Enterprise Data = Enhanced Analytics #SocData CONTENTS Executive Summary 3 The Value of Enterprise-Specific Social Data
More informationOracle Real-Time Decisions (RTD) Ecommerce Interaction Management Use Case
Oracle Real-Time Decisions (RTD) Ecommerce Interaction Management Use Case Nicolas Bonnet Senior Director Product Management Oracle Business Intelligence The following is intended
More informationOracle Adaptive Intelligent Apps for Manufacturing
Oracle Adaptive Intelligent Apps for Manufacturing Machine Learning and Artificial Intelligence (AI) Driven Analytical Cloud Applications for the Manufacturing Industry Machine Learning and AI for Manufacturers
More informationPOWER REAL-TIME TELCO NETWORK OPERATIONS WITH EXTREME ANALYTICS
SPECIAL REPORT POWER REAL-TIME TELCO NETWORK OPERATIONS WITH EXTREME ANALYTICS How OmniSci s accelerated analytics, intuitive data visualization, and extreme usability help telecommunications companies
More informationQlik Sense. Data Sheet. Transform Your Organization with Analytics
Data Sheet Qlik Sense Transform Your Organization with Analytics Qlik Sense is Qlik s next-generation platform for modern, self-service oriented analytics. It supports the full range of analytics use cases
More informationAdobe and Hadoop Integration
Predictive Behavioral Analytics Adobe and Hadoop Integration JANUARY 2016 SYNTASA Copyright 1.0 Introduction For many years large enterprises have relied on the Adobe Marketing Cloud for capturing and
More informationCCH Tagetik Modernizing Finance May 21, 2018
CCH Tagetik Modernizing Finance May 21, 2018 Agenda Overview Market Trends CFO Challenges CCH Tagetik Solution Benefits Wolters Kluwer at a Glance Since 1836 A rich 180-year heritage of strong values,
More informationNICE Customer Engagement Analytics - Architecture Whitepaper
NICE Customer Engagement Analytics - Architecture Whitepaper Table of Contents Introduction...3 Data Principles...4 Customer Identities and Event Timelines...................... 4 Data Discovery...5 Data
More informationRazvan IONITA 27 Oct 2016 UNIFORMANCE SUITE. Delivers New Process Intelligence Capabilities
Razvan IONITA 27 Oct 2016 UNIFORMANCE SUITE Delivers New Process Intelligence Capabilities Did you know the Uniformance Suite enables 1 A global oil and gas producer to manage over 10 million data points
More informationModern Payment Fraud Prevention at Big Data Scale
This whitepaper discusses Feedzai s machine learning and behavioral profiling capabilities for payment fraud prevention. These capabilities allow modern fraud systems to move from broad segment-based scoring
More informationAdobe and Hadoop Integration
Predictive Behavioral Analytics Adobe and Hadoop Integration DECEMBER 2016 SYNTASA Copyright 1.0 Introduction For many years large enterprises have relied on the Adobe Marketing Cloud for capturing and
More informationPORTFOLIO AND TECHNOLOGY DIRECTION ARMISTEAD SAPP & RANDY GUARD
PORTFOLIO AND TECHNOLOGY DIRECTION ARMISTEAD SAPP & RANDY GUARD FOCUS MARKETS SAS Addressable Market Size $US Billions $14.7 2015 2019 $10.6 $9.6 $7.0 $7.9 $5.0 $2.6 $3.7 $5.7 $4.4 $3.0 $4.2 BUSINESS INTELLIGENCE
More information+38% Your Path to Personalization: A Framework for Digital Marketing Advancement
Your Path to Personalization: A Framework for Digital Marketing Advancement White Paper +38% According to our survey, 38% more companies plan to increase investments in personalization in the next 12-18
More informationFinancial Services Compliance
Financial Services Compliance Amidst frequently changing regulations, heightened risk, and a growing volume of disparate data, compliance has become complex, inefficient, and costly. Mitigating new risk
More informationCustomer Experience and Analytics Maturity Model.
Customer Experience and Analytics Maturity Model 1 Topics Customer Engagement Maturity Model BI & Analytics Maturity Model 2 Customer Engagement Maturity Model 3 Your Customer s Journey / Lifecycle Listen
More informationInfoSphere Software The Value of Trusted Information IBM Corporation
Software The Value of Trusted Information 2008 IBM Corporation Accelerate to the Next Level Unlocking the Business Value of Information for Competitive Advantage Business Value Maturity of Information
More informationBy 2020, more than half of major new business processes and systems will incorporate some element of the IoT.
Trends in Analytics By 2020, more than half of major new business processes and systems will incorporate some element of the IoT. Gartner Unexpected Implications Arising From the Internet of Things report
More informationEvanios Capabilities. The Top 16 Things We Do Really Well TRUSTED BY
Evanios Capabilities The Top 16 Things We Do Really Well TRUSTED BY Introduction Thank you for considering Evanios! Because our platform offers so much interconnected functionality, we thought it would
More informationLouis Bodine IBM STG WW BAO Tiger Team Leader
Louis Bodine IBM STG WW BAO Tiger Team Leader Presentation Objectives Discuss the value of Business Analytics Discuss BAO Ecosystem Discuss Transformational Solutions http://www.youtube.com/watch?v=eiuick5oqdm
More informationBuilding a Single Source of Truth across the Enterprise An Integrated Solution
SOLUTION BRIEF Building a Single Source of Truth across the Enterprise An Integrated Solution From EDW modernization to self-service BI on big data This solution brief showcases an integrated approach
More informationIBM Business Intelligence and Business Analytics
IBM Business Intelligence and Business Analytics Ganesh 1 Kedari IBM India Software Labs, Pune #1 concern Business Analytics 83% Virtualization 76% Risk Management & Compliance 71% Mobility Solutions 68%
More informationTIBCO Industry Analytics: Consumer Packaged Goods and Retail Solutions
TIBCO Industry Analytics: Consumer Packaged Goods and Retail Solutions TIBCO s robust, standardsbased infrastructure technologies are used by successful retailers around the world, including five of the
More informationAnalytics for All Data
Analytics for All Data How Oracle Analytics Helps Agencies Improve Their Effectiveness FORCES 2017 Jim Penn Sr Manager, Public Sector Oracle Analytics & Big Data Agenda Oracle s Analytics Platform Overview
More informationDell EMC IT Big Data Analytics Journey. Nagesh Madhwal Client Solutions Director, Consulting, Southeast Asia, Dell EMC
Dell EMC IT Big Data Analytics Journey Nagesh Madhwal Client Solutions Director, Consulting, Southeast Asia, Dell EMC Agenda 1 2 3 4 5 6 Dell EMC IT Big Data Journey Building the Data Lake Marketing Science
More informationKnowledgeSTUDIO. Advanced Modeling for Better Decisions. Data Preparation, Data Profiling and Exploration
KnowledgeSTUDIO Advanced Modeling for Better Decisions Companies that compete with analytics are looking for advanced analytical technologies that accelerate decision making and identify opportunities
More informationMid-Atlantic CIO Forum
Mid-Atlantic CIO Forum Towson State University - March 17, 2016 Dave Rich CEO DBR & Associates Evolution of Analytics Batch Reportin g 1975 Static Reportin g Ad Hoc Query 1989 Data Warehousing Online
More informationBrochure. Application Lifecycle Management. Accelerate Your Business. Micro Focus Application Lifecycle Management Software
Brochure Application Lifecycle Management Micro Focus Application Lifecycle Management Software Brochure Technology users across the globe are unrelenting in their demand for faster delivery of engaging
More informationReal-time Digital Banking With Fast Data
Copyright 2015 TIBCO Software Inc. Real-time Digital Banking With Fast Data Kevin Pool - Asia CTO Business is Evolving. Copyright 2000-2013 TIBCO 2 Banking is Integrating into the Customers Life STUDENT
More informationArchitectural Perspective on future Dashboards in a Service Oriented and Event Driven World
Architectural Perspective on future Dashboards in a Service Oriented and Event Driven World Marc Peters Senior IT Architect IBM Deutschland GmbH Dashboards in SOA and EDA 26.06.2007 As a reminder Abbreviations
More informationENABLING DATA DRIVEN DECISIONS WITH REALTIME INTELLIGENCE
ENABLING DATA DRIVEN DECISIONS WITH REALTIME INTELLIGENCE Adrian Loong BI Manager Ryan Newsome Reporting Developer Note re data used in this presentation Please note that all data displayed in the dashboards
More informationReal-time Streaming Insight & Time Series Data Analytic For Smart Retail
Real-time Streaming Insight & Time Series Data Analytic For Smart Retail Sudip Majumder Senior Director Development Industry IoT & Big Data 10/5/2016 Economic Characteristics of Data Data is the New Oil..then
More informationEfficient Troubleshooting Using Machine Learning in Oracle Log Analytics
Efficient Troubleshooting Using Machine Learning in Oracle Log Analytics Nima Haddadkaveh Director, Product Management Oracle Management Cloud October, 2018 Safe Harbor Statement The following is intended
More informationDIGITAL BSS CORE Solution Overview
DIGITAL BSS CORE Solution Overview Open & Intelligent Foundation for Monetization of the Digital Era Qvantel Digital BSS Core Open and Intelligent for the Monetization Needs of the Digital Era Qvantel
More informationWHITE PAPER SPLUNK SOFTWARE AS A SIEM
SPLUNK SOFTWARE AS A SIEM Improve your security posture by using Splunk as your SIEM HIGHLIGHTS Splunk software can be used to build and operate security operations centers (SOC) of any size (large, med,
More informationUSING BIG DATA AND ANALYTICS TO UNLOCK INSIGHTS
USING BIG DATA AND ANALYTICS TO UNLOCK INSIGHTS Robert Bradfield Director Dell EMC Enterprise Marketing ABSTRACT This white paper explains the different types of analytics and the different challenges
More informationCognitive Data Governance
IBM Unified Governance & Integration White Paper Powered by Machine Learning to find and use governed data Jo Ramos Distinguished Engineer & Director IBM Analytics Rakesh Ranjan Program Director & Data
More informationSAP Predictive Analytics Suite
SAP Predictive Analytics Suite Tania Pérez Asensio Where is the Evolution of Business Analytics Heading? Organizations Are Maturing Their Approaches to Solving Business Problems Reactive Wait until a problem
More informationIBM Tivoli Endpoint Manager for Software Use Analysis
IBM Endpoint Manager for Software Use Analysis Rapid, granular inventory insights and always-on asset management enhance license compliance Highlights Identify licensed and unlicensed software with drill-down
More informationIs Your Company Ready for Digital Transformation? Introducing the Digital Readiness Index
Is Your Company Ready for Digital Transformation? Introducing the Digital Readiness Index Sight Machine regularly meets with large manufacturers who are embarking on journeys of digital transformation.
More informationAn Introduction to Oracle Business Intelligence (BI) Platform NYOUG Sep 21, Shyam Varan Nath Oracle Corporation
An Introduction to Oracle Business Intelligence (BI) Platform NYOUG Sep 21, 2006 Shyam Varan Nath Oracle Corporation 1 Agenda Introduction to Business Intelligence Oracle components of BI A case study
More informationData Ingestion in. Adobe Experience Platform
Contents The challenges with data Adobe Experience Platform Data Ingestion in Adobe Experience Platform Data Ingestion Service Data Lake Conclusion Adobe Experience Platform helps customers to centralize
More informationHortonworks Connected Data Platforms
Hortonworks Connected Data Platforms MASTER THE VALUE OF DATA EVERY BUSINESS IS A DATA BUSINESS EMBRACE AN OPEN APPROACH 2 Hortonworks Inc. 2011 2016. All Rights Reserved Data Drives the Connected Car
More informationAdvanced Root Cause Analysis for Product Quality Improvement using Machine Learning in TIBCO Spotfire
Advanced Root Cause Analysis for Product Quality Improvement using Machine Learning in TIBCO Spotfire GRADIENT BOOSTING MACHINE MODELING Gradient Boosting Machine (GBM) modeling is a powerful machine learning
More informationA technical discussion of performance and availability December IBM Tivoli Monitoring solutions for performance and availability
December 2002 IBM Tivoli Monitoring solutions for performance and availability 2 Contents 2 Performance and availability monitoring 3 Tivoli Monitoring software 4 Resource models 6 Built-in intelligence
More informationIBM Planning Analytics Express
Performance management and business intelligence for midsize organisations IBM Planning is a performance management (PM) and business intelligence (BI) solution for midsize organisations. It delivers the
More informationYour Financial Reporting problems solved by Oracle fusion!
Your Financial Reporting problems solved by Oracle fusion! In today s day and age with the multitude of information available, organizations depend largely on how strategically they use its financial information
More informationYour Top 5 Reasons Why You Should Choose SAP Data Hub INTERNAL
Your Top 5 Reasons Why You Should Choose INTERNAL Top 5 reasons for choosing the solution 1 UNIVERSAL 2 INTELLIGENT 3 EFFICIENT 4 SCALABLE 5 COMPLIANT Universal view of the enterprise and Big Data: Get
More informationFUELING FINANCE S NEEDS FOR INSIGHTS WITH SAP S/4HANA
FUELING FINANCE S NEEDS FOR INSIGHTS WITH SAP S/4HANA INTRODUCTION: PUTTING THE PIECES TOGETHER We are in a decade of data-driven businesses and new business models such as the sharing economy. Organizations
More informationAWS Digital Innovation Program
AWS Digital Innovation Program How customers can learn to innovate like Amazon Feras Alsamawi, Digital Innovation Programs EMEA May 18, 2017 2016, Amazon Web Services, Inc. or its Affiliates. All rights
More informationMVP Juan Rafael. Microsoft Dynamics 365 for Finance & Operations and Power BI : introduction to great analytics
MVP Juan Rafael Microsoft Dynamics 365 for Finance & Operations and Power BI : introduction to great analytics BIG Thanks to SQLSatLima sponsors Special thanks Session learning objectives At the end of
More informationTDWI Analytics Principles and Practices
TDWI. All rights reserved. Reproductions in whole or in part are prohibited except by written permission. DO NOT COPY Previews of TDWI course books offer an opportunity to see the quality of our material
More informationHow to improve your AML detection? Christopher Ghenne Principal Manager Fraud & Security Intelligence EMEA
How to improve your AML detection? Christopher Ghenne Principal Manager Fraud & Security Intelligence EMEA Years of 14,010 SAS employees worldwide 93 of the top 100 on the 40 #1 BUSINESS ANALYTICS companies
More informationMachine & Equipment Health from GE Digital. Part of our Asset Performance Management suite
Machine & Equipment Health from GE Digital Part of our Asset Performance Management suite Business challenges Modern industrial equipment generates gigabytes to terabytes of data every day. When organized,
More informationA Business Oriented Architecture. Combining BPM and SOA for Competitive Advantage
Combining BPM and SOA for Competitive Advantage Phil Gilbert Introduction In a recent survey of 1,400 CIOs by Gartner Executive Programs, the top business priority identified by CIOs was business process
More informationCOGNITIVE QA: LEVERAGE AI AND ANALYTICS FOR GREATER SPEED AND QUALITY. us.sogeti.com
COGNITIVE QA: LEVERAGE AI AND ANALYTICS FOR GREATER SPEED AND QUALITY ARTIFICIAL INTELLIGENCE vs. COGNITIVE COMPUTING Build a system that can generally perform any intellectual task so called Strong AI
More informationThe Five Essential Elements of Self-Service Data Integration
The Five Essential Elements of Self-Service Data Integration INTRODUCTION Firehoses of data are blasting the modern enterprise. From every direction they stream into the data center from warehouses, marketing
More information7 Steps to Data Blending for Predictive Analytics
7 Steps to Data Blending for Predictive Analytics Evolution of Analytics Just as the volume and variety of data has grown significantly in recent years, so too has the expectations for analytics. No longer
More informationREAL-TIME ACTIONABLE INTELLIGENCE TURNING DATA INTO BUSINESS VALUE
REAL-TIME ACTIONABLE INTELLIGENCE TURNING DATA INTO BUSINESS VALUE GET ACTIONABLE INSIGHTS FROM DATA In this modern, fast-paced, digitalized landscape, closing the gap between gathering data and taking
More informationData Cleansing - From Spreadsheets to Data Centres
Data Cleansing - From Spreadsheets to Data Centres Intela Business Activity Summary Professional Services Machine learning & AI solutions AI strategy advice Products - Intelligent Data Cleansing - PhD
More informationPredictive Decision-Making through the Power of AI Xavier Health AI Summit 2018
Predictive Decision-Making through the Power of AI Xavier Health AI Summit 2018 Imagine this is a medical device Under the case it s a Now imagine the processes to develop it Design and Design Controls
More informationSEIZE COMPETITIVE ADVANTAGE WITH NEXT-GEN BUSINESS INTELLIGENCE ANALYTICS
SEIZE COMPETITIVE ADVANTAGE WITH NEXT-GEN BUSINESS INTELLIGENCE ANALYTICS CRACKING THE CODE TO AGILE INTELLIGENCE Today s business jostle for competitive leadership in a dynamic and crowded space. They
More informationQlik Sense Enterprise
Data Sheet Qlik Sense Enterprise See the whole story that lives within your data Qlik Sense is a next-generation application for self-service oriented visual analytics, offering unique and powerful data
More informationIt s a NEW Day! A Framework for Digital Operations with an Analytics Foundation
It s a NEW Day! A Framework for Digital Operations with an Analytics Foundation It s a NEW DAY! Companies that will thrive today and tomorrow are transforming how they operate. An explosion of technology
More informationInformation Architecture: Leveraging Information in an SOA Environment. David McCarty IBM Software IT Architect. IBM SOA Architect Summit
Information Architecture: Leveraging Information in an SOA Environment David McCarty IBM Software IT Architect 2008 IBM Corporation SOA Architect Summit Roadmap What is the impact of SOA on current Enterprise
More informationTeradata IntelliSphere
Teradata IntelliSphere Name, Title of Presenter 1 2 Agenda More analytic tools & techniques The Reality Wide range of deployment choices Proliferation of departmentalized analytics Dynamically changing
More informationEnabling Asset Integrity Management for the Oil & Gas Industry
Enabling Asset Integrity Management for the Oil & Gas Industry THE CHALLENGE Ensuring operational integrity of critical assets and installations is of paramount importance to oil field operators and the
More informationSuccessful healthcare analytics begin with the right data blueprint
IBM Software Information Management Healthcare Successful healthcare analytics begin with the right data blueprint 2 Successful healthcare analytics begin with the right data blueprint Executive summary
More informationFostering Continuous Improvement of Your Upstream Operations siemens.com/xhq
XHQ Upstream Fostering Continuous Improvement of Your Upstream Operations siemens.com/xhq Upstream Fostering continuous improvement of your upstream operations XHQ Operations solutions provide valuable
More informationOur Emerging Offerings Differentiators In-focus
Our Emerging Offerings Differentiators In-focus Agenda 1 Dotbits 2 Dotbits@US ; Dotbits@India 3 Differentiators and Key Trends 4 Solutions and Service Offerings 5 Representative Experiences Page 2 Dotbits
More informationBusiness Intelligence and Process Modelling
Business Intelligence and Process Modelling F.W. Takes Universiteit Leiden Lecture 3: Business Intelligence & Visual Analytics BIPM Lecture 3: Business Intelligence & Visual Analytics 1 / 72 Business Intelligence
More informationWhat you need to know about Reporting & BI for AX2012 & D365
What you need to know about Reporting & BI for AX2012 & D365 Gina Pabalan Edgewater Fullscope, Data & Analytics Practice Managing Director, Business Intelligence https://www.linkedin.com/in/ginapabalan/
More informationRealize the potential of a connected factory
Realize the potential of a connected factory Azure IoT Central Empower your digital business through connected products How to capitalize on the promise Azure IoT Central is a fully managed IoT SaaS (software-as-a-service)
More informationCapgemini Digital Control Room Analytics. Introducing an end-to-end solution for real-time operations insight
Capgemini Digital Control Room Analytics Introducing an end-to-end solution for real-time operations insight Embrace your digital core In this digital age, data is the new currency Data from your operations
More informationTHE RIGHT TECHNOGRAPHICS FOR B2B TECHNOLOGY MARKETERS
THE RIGHT TECHNOGRAPHICS FOR B2B TECHNOLOGY MARKETERS WHY YOU NEED TECHNOGRAPHICS If your sales and marketing teams are reaching out to prospective buyers, being relevant is an essential component of gaining
More informationMachine Learning 101
Machine Learning 101 Mike Alperin September, 2016 Copyright 2000-2016 TIBCO Software Inc. Agenda What is Machine Learning? Decision Tree Models Customer Analytics Examples Manufacturing Examples Fraud
More informationCASE STUDY Delivering Real Time Financial Transaction Monitoring
CASE STUDY Delivering Real Time Financial Transaction Monitoring Steve Wilkes Striim Co-Founder and CTO Background Customer is a US based Payment Systems Provider Large Network of ATM and Cashier Operated
More informationData Analysis in the Internet of Things: IoT capabilities with MATLAB/Simulink
Data Analysis in the Internet of Things: IoT capabilities with MATLAB/Simulink Dr.-Ing. Rainer Mümmler Application Engineering Team The MathWorks GmbH 2015 The MathWorks, Inc. 1 Overview Publish data to
More informationAlways on Marketing Vodafone. June 2018
Always on Marketing Vodafone June 2018 Vodafone is one of the world s largest telecoms operators 25 19 46 64m 47bn 63bn countries in which we have mobile operations countries in which we have fixed operations
More informationYour Roadmap to Intelligent Assisted Support
COVEO FUNDAMENTALS Your Roadmap to Intelligent Assisted Support 1 Introduction Among support leaders top priorities are the seemingly contradicting needs to reduce support costs, while improving customer
More informationQlik Sense Enterprise
Qlik Sense Enterprise See the whole story that lives within your data Qlik Sense is a next-generation visual analytics platform that empowers everyone to see the whole story that lives within their data.
More informationMove Fast Follow Everything & Focus on What Matters Most Visualize and Optimize Your Customer Journey with AppDynamics
Move Fast Follow Everything & Focus on What Matters Most Visualize and Optimize Your Customer Journey with AppDynamics Nick Hui, AppDynamics BDM Asia 8 Nov, 2018 2018 Cisco and/or its affiliates. All rights
More informationIs Your Company Ready for Digital Transformation? Introducing the Digital Readiness Index
Is Your Company Ready for Digital Transformation? Introducing the Digital Readiness Index Sight Machine regularly meets with large manufacturers who are embarking on journeys of digital transformation.
More informationWhy Reporting in Dynamics AX2012 is Difficult and what you can do about it
Why Reporting in Dynamics AX2012 is Difficult and what you can do about it Written By: Gina Pabalan Director, Data & Analytics https://www.linkedin.com/in/ginapabalan Reporting in the Context of an ERP
More informationData. Does it Matter?
Data. Does it Matter? Jarut N. Cisco Systems Data & Analytics are Top of Mind in Every Industry Automotive Auto sensors reporting location, problems Communications Location-based advertising Consumer
More informationData Governance and Data Quality. Stewardship
Data Governance and Data Quality Stewardship 1 Agenda Discuss Data Quality and Data Governance Considerations for future technical decisions 2 Intelligence Portal Embedded InfoApps Hot Social Bad Feedback
More informationWHITEPAPER. Unlocking Your ATM Big Data : Understanding the power of real-time transaction monitoring and analytics.
Unlocking Your ATM Big Data : Understanding the power of real-time transaction monitoring and analytics www.inetco.com Summary Financial organizations are heavily investing in self-service and omnichannel
More informationMaturing IoT solutions with Microsoft Azure. Glenn Colpaert Azure/IoT Domain
Maturing IoT solutions with Microsoft Azure Glenn Colpaert Azure/IoT Domain Lead @GlennColpaert Who we are 2000 Belgium 2004 France 2013 Portugal 2016 Switzerland 2016 UK 2016 The Netherlands 2017 Malta
More informationInformation Management and Business Intelligence. Service brochure
Information Management and Business Intelligence Service brochure Organizations increasingly recognize information as a business asset. However, there has been very little attempt to exploit information
More informationSmarter Marketing Assessment
Smarter Marketing Assessment Assess how your organisation is performing in the five areas of smarter marketing and determine how you can improve. SMS How smart is your marketing strategy? The relationship
More informationManufacturing operations management. Software portfolio that helps you realize innovation in the Digital Enterprise. Siemens PLM Software
Siemens PLM Software Manufacturing operations management Software portfolio that helps you realize innovation in the Digital Enterprise www.siemens.com/mom A holistic approach to optimize the entire value
More informationData Lake or Data Swamp?
Data Lake or Data Swamp? Keeping the Data Lake from Becoming a Data Swamp. 1 INTRODUCTION Increasingly, businesses of all kinds are beginning to see their data as an important asset that can help make
More informationIs It Time to Evolve from Spreadsheets to Business Intelligence?
Position Paper Is It Time to Evolve from Spreadsheets to Business Intelligence? Gregg Gordon Sr. Director, Big Data Practice No matter how your organization delivers value to your market, customers are
More informationImproving enterprise performance through operations intelligence solutions siemens.com/xhq
XHQ Operations Intelligence Software Improving enterprise performance through operations intelligence solutions siemens.com/xhq What is XHQ? That s simple. XHQ is a better, more intelligent way to make
More information