Providing Answers for Business through Big Data

Transcription

1 Providing Answers for Business through Big Data Jon C. Arnold, Managing Director, Manufacturing & Resources, Microsoft Kevin Schofield, President, ADRM Software, Inc Mike Delgado, VP Sales, Service & Alliances, Panorama Software

2 Today s Discussion Microsoft in Manufacturing & Resources Megatrends and Big Data Industry Data Models Business Insight Questions & Answers 2

3 Microsoft Manufacturing & Resources Industries Automotive High Tech Industrial Equipment Aerospace Oil & Gas Mining Power & Utilities Pharmaceuticals Metals & Minerals Paper & Pulp Chemicals

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6 IDC Industry Big Data Trends and Opportunities TOP OPPORTUNITIES The factory of the future-productivity Accelerate service and operations excellence Design, production, and supply intelligence Smart Grid & Buildings TOP TECHNOLOGIES M2M and Telematics Advanced Analytics DW/Analytics Appliances TOP USE CASES Condition Based Monitoring Supply Chain Resilience Shop Floor Automation Advanced Metering Infrastructure Building Management and energy efficiency Source: IDC 2013

7 Big Data Driver: The Emergence of Intelligent Systems Automated, sensor enabled plant Value-chain collaboration Cross-functional & situational business insight Social Business Mobility & Devices Public or Private Cloud Data Big Data & Business Intelligence Business Value On-Premises Condition-based monitoring Supply chain resilience Smart Grid Shop floor automation

8 Business Intelligence End-to-End Architecture Data Sources CEP, Staging, ETL Data Data Marts Presentation Smart Meters, Sensors,Devices CEP, StreamInsight PI Time Series Store (OSIsoft) Enterprise Data Warehouse SQL Server 2012 PDW Subject Area Cubes Dashboards, Scorecards Azure Marketplace, Third Party Data Staging Meter Management System Subject Area Cubes Adhoc, Canned Reports HOA, Big Data ASCII, Excel, Flat files, Other Data Sources Enterprise Information Management ODS ArcGIS Subject Area Cubes Microsoft SQL Server Analytics, Mining Self Service BI, Search, Workflow

9 Elements of a Big Data Solution Presentation Visualization Predictive Analytics/BI Unlocking the Value Data Acquisition & Integration Data Aggregation Industry Data Model ADRM Software

10 MG Bryan Video Case Study

11 Industry Data Models and Big Data Kevin Schofield President ADRM Software, Inc October 2013

12 Who is ADRM Software? A leader in developing comprehensive, pre-built information blueprints or road-maps for specific vertical markets for the past 24 years We deliver industry-specific data models for overall Enterprise Data Architecture initiatives and as part of larger solutions for big projects such as DW/BI, ODS, MDM, SOA/ESB and Business Glossaries We provide a flexible integrated logical data and information foundation to support large companies in today s rapidly changing and highly competitive environment 12

13 Vertical Markets Supported Communications & Media Financial Services Government & Infrastructure Services High Tech Products Computers High Tech Components Medical Systems Mobile Wireless Devices Networking Equipment Semiconductors Software Insurance & Healthcare Manufactured Products Agricultural Chemicals Automobile Manufacturing Aviation Products Consumer Packaged Goods Durable Medical Equipment Heavy Vehicle Manufacturing Industrial Chemicals Luxury Goods Mining & Metals (avail. late 2013) Oil & Gas (avail. late 2013) Pharmaceutical Products Tires Retail Transportation, Travel & Leisure Utilities Gas & Electric Utilities Water Utilities 13

14 What does an industry model look like? Sample ADRM Software Gas & Electric Utility Data Model

15 Business Area Models Business Area Models vary by industry but typically include the following: Accounting & Financial Reporting Booking / Billing / Backlog Budgets Business Metrics Channels Contracts Credit & Collections Individual Credit & Collections Legal Entity Customers - Individual Customers - Legal Entity Customer Serv. & Contact Mgmt. Geography Human Resources & Payroll Inventory / Supply Chain Manufacturing Marketing & Advertising Orders Party Product Property & Equipment (ie Assets) Purchasing Training & Education

16 Strategic Challenges Enterprise Architecture Data Warehousing / Business Intelligence Operational Data Store / Zero Latency Enterprise Master Data Management SOA / ESB Appl. Rationalization / Modernization Creating or Expanding a Business Glossary 16

17 A View of the Models in Use Customers typically start with Enterprise or DW model (each contains small pieces of most Business Area Models) and selectively add additional detail from Business Area Models during successive phases Enterprise or DW Model Business Area Model Phase 1 Implemented portion 17

18 Value Proposition A pre-built solution that is 80-90% complete off-the shelf and does not require a large, lengthy and risky consulting engagement before realizing a return Quickly (and with less risk) provides a rich integrated foundation which complements today s metadata-aware data integration and BI software, while benefiting from best practices and lessons learned at other similar organizations Comprehensive and integrated nature of data model suites allow companies to focus on delivering value-adding projects secure in the knowledge they will be much more likely to avoid creating information silos 18

19 Smart Grid Big Data - Idaho Power Data Warehouse EDW is critical to Utilities going forward and integrating AMI, Financial, and CIS data is fundamental to Idaho Power strategy moving forward. - Dennis Gribble CIO Idaho Power Customer Challenge No Enterprise Information Architecture Needed Data Model beyond CIM Needed to Integrate CIS & FinServ data for reporting and analytics Complexity of 95 SQL Databases servicing CIS information Solution ADRM Software s comprehensive Utility Industry Data Model Start with Enterprise Data Warehouse Model and expand with detail from Business Area Models Reduced risk, immediate value, predictable, lower TCO Idaho Power is an electric generation, transmission and distribution services for 496K customers headquartered in Boise, Idaho. Results/Benefits Unlocked the value of smart grid data for business insight & accelerated decision making Well defined strategy for aggregating reliability, CIS, outage, costing & financial data Clear app consolidation approach & accelerated timeline for deploying EDW

20 Enterprise Data Warehouse Raiffeisen Bank Customer Challenge Integrate multiple existing SQL Server data marts for reporting and analytics Incorporate data from SAS-based risk data warehouse to reduce duplication Support integrated multi-tenancy (i.e. data from multiple banks in the group with ability to support single company and cross-company analyses) Solution ADRM Software s comprehensive Banking Industry Data Models ADRM quickly scoped and delivered integrated logical model for multi-tenant SQL Server-based EDW Microsoft Consulting Services delivered ETL and reporting / analytics to meet specified requirements on schedule Raiffeisen Bank provides retail, commercial & corporate investment banking services to customers in Austria and other EEC Results/Benefits Integrated various disconnected information silos into true EDW for better business insight, accelerated decision making, and improved risk management Established solid comprehensive analytic foundation for future growth of business Did so while achieving accelerated timeline and reduced risk, while capitalizing on best practices

21 Microsoft Data Warehousing Lineup Parallel Data Warehouse

22 SQL Server Parallel Data Warehouse Where Big Data meets EDW From Entry-Level to Massive Scale Massively Parallel Processing version of SQL Server Multiple SQL Servers working together to answer queries Start with a few TBs; seamlessly scale to multiple PBs Pre-Built Hardware + Software Appliance Best practices built in already Co-engineered with HP and Dell Pre-build HW and Pre-installed SW Big Data and EDW meet Seamlessly query data from both EDW and Hadoop Familiar SQL syntax Transfer data to/from EDW and Hadoop blazingly fast

23 Scalability Delivered by Parallel Data Warehouse Scalability Challenge Aggregate very large and growing volumes of machine generated and other data (from smart meters, weather, etc.) Need to support quick response times for a wide variety of sometimes complex queries with very high data volumes as well as large volumes of simple reporting by growing number of users Solution Microsoft Parallel Data Warehouse Appliance and BI software ADRM Software s comprehensive Utility Industry Data Model Low TCO, very high performance, scalable solution Results/Benefits Stored 2 years of 15 minute smart meter read information for 1 million customers (approx. 70 Billion rows in meter read table) Integrated with selected customer, premise, tariff and weather data Achieved blazing speed on extremely high volume queries

24 Big Data / Hadoop Integration External Tables and full SQL query access to data stored in HDFS HDFS bridge for direct & fully parallelized access of data in HDFS Joining on-the-fly PDW data with data from HDFS Parallel import of data from HDFS in PDW tables for persistent storage Parallel export of PDW data into HDFS including round-tripping of data Regular T-SQL Enhanced PDW Query Engine Structured data External Table HDFS bridge Results HDFS Data Nodes Unstructured data PDW 2012

25 Big Data & BI Mike Delgado VP Sales, Service & Alliances Panorama Software October 2013 Taking advantage of Big Data & BI to make faster and more effective Business decisions.

26 The last mile is Business Intelligence Big Data HDFS Map Reduce Hbase Hive SQL Server BI Hadoop framework BI Suite

27 While data is growing, the Decision Window is narrowing.. Lots of data need to be digested quickly, or risk of being irrelevant Decisions need to be taken faster than competitors do The holy grail is to shorten the time from Data to Action Time Decision Window

28 Business Intelligence 3.0 Revolution

29 Business Intelligence 3.0 ADVANCED ANALYTICS Easy-to-use analytics for business users and advanced analytical capabilities for power users SOCIAL BI Engaging platform for collaborative decision making Self Service CONTEXTUAL DISCOVERY Intelligent BI engine that automatically pushes relevant insights by understanding user s behavior

30 Contextual Data Discovery When you have a world of Data, you need a better Compass

31 Contextual Discovery Helping you focus on what s relevant INTERESTS GRAPH SOCIAL GRAPH - This is who I know - Discussions, Annotations - Example: Facebook, LinkedIn - This is what I like - Tags, Likes, Visits - Example: Amazon, Netflix DATA - Insights, Exceptions, Models

32 Social BI / Collaborative Decision Making Improve business results by Crowdsourcing your decisions

33 We live in a Social World

34 Enterprise Apps Are Influenced by the consumer's world ERP

35 The Social Enterprise Has Emerged! Web 2.0 Data Phones CRM.Net/ Java The Old enterprise Office& s Reports

36 About Panorama Software The company that invented OLAP and sold it to Microsoft 18 years of innovations in the BI industry Won multiple awards (SQL Server Pro Magazine, Stevie Award, Microsoft IMPACT award) Covered in BI Magic Quadrant by Gartner, BI Wave by Forrester, Wisdom of Crowds by Dresner Advisory reports Necto does seem genuinely different from most self-service BI applications aimed at business users out there. Panorama takes a cue from social media in Necto BI offering

37 What analysts are saying Top technologies selected by CIOs

38 #1

39 Over 2,000 customers worldwide Financial Services Manufacturing Healthcare Retail & Distribution Telco, Media & Entertainment

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41 Demo Business Intelligence 3.0 in Action

42 Drag the relevant colleague on a cell to have a discussion on a cell level Connect web content to BI data in seconds

43 Necto uses Fast search to Visually connect Documents in SharePoint to the BI Data Necto in SharePoint: add Excel Services, PowerView and PPS components into Necto to enable Social BI

44 Lync SharePoint webparts SharePoint My Site & Discussions PowerView Reporting Services Lync Excel Services Performance Point Services

45 Leveraging BI 3.0 in Manufacturing & Distribution Analyze sales and key performance trends Optimize the performace across all distribution channels Localized planning and management Improve all critical logistical processes Immediate feedback on impact of key manufacturing programs One UI and application for all business and technical functions with easy of use and deployment

46 Call to Action Come by demo-booth for Demo during breaks Schedule a session with the team while here at event Register for your Free Software Licenses and localized Knowledge Transfer (training) session!! Visit us at Panorama.com

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48 Jon C. Arnold, Managing Director, Manufacturing & Resources, Microsoft Kevin Schofield, President, ADRM Software, Inc. Mike Delgado, VP Sales, Service & Alliances, Panorama Software