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Financial Analytics pafp 11-21-13


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Trends and financial management of the analytics processes

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Financial Analytics pafp 11-21-13

  1. 1. Using Financial Analytics for Performance Management Presentation to the Pittsburgh Association for Financial Professionals Richard Gristak Sr. Practice Dir. Business Analytics Ciber Inc. November 21, 2013
  2. 2. 2 Roadmap Ciber Intro Trends Today The Data Dilemma What are the Business Goals? A Framework for a BI Support Center Analytics and Predictive Analytics Metadata and Contextual Analysis Governance Issues Some vendor product stacks
  3. 3. 3 About Ciber A $1.1 billion Global IT services company that builds, integrates and supports applications and infrastructures for business and government in 72 offices and 19 countries. Founded in 1974 More than 7,000 employees NYSE: CBR - Headquartered in Denver 72 Offices in 19 countries Local Accountability with Global Delivery: Domestic & Off-Shore Growth & Profitability for 35 years Focus on Quality: ISO, CPMM, SAS 70 Best-in-Class Client Satisfaction As a result of an independent customer satisfaction survey, asking more than 150 international CIBER customers, 96% respond that they will choose CIBER again.
  4. 4. 4 Core Capabilities & Services Strategy • • • • • • Development of IT Strategic Plans Design and Implementation of IT Governance Frameworks Design, Evaluation and Implementation of IT operating Models Design and implementation of Portfolio Management (Project & Application) Development of IT Business Cases Cloud Adoption Strategies Project Management & PMO  Project Management  Process Definition  Complete PMO solution offering Enterprise Architecture Enterprise Architecture Program Design and Implementation Business Architecture Development SOA Strategy, Roadmap, Governance Data /Information Governance System & Technology Roadmap Long Term Technology Planning • • • • • • Application Development  Design, build, test & implement custom applications  Application modernization & enhancement  Collaboration  Mobility & wireless  Portal development  Document Management and Enhanced ECM  Web 2.0 and Social Media  Records/Contract Management  Platform Integration and Consolidation BI/DW          Data Warehousing Data Architecture Business Intelligence Systems SAP Business Objects Master Data Management (MDM) Data Governance Common Data Definitions Single View of the Customer Data Metadata Architecture` Managed Services     Application Management ERP Help Desk Hosting Quality Assurance & Testing Enterprise Mobility  Mobile Roadmap & Strategy  Mobile App Development (HTML5,iOS,Android, Windows  Mobile Testing & Security Test Maturity Improvements Managed Testing Services IV&V Test Centers of Excellence Automation and Automated Regression  Technical Support of Testing  Independent Test Organization     
  5. 5. 3 Trends Today
  6. 6. 6 Trends Today  CFO is increasingly involved in IT  Close to 50% of IT Leaders report to the CFO; 25% report to the CMO  63% of CFOs plan upgrades to BI in the coming year  92% of CFOs DO NOT believe IT provides transformational or differentiation capabilities  Analytics today continues to overwhelmingly rely on lagging KPI indicators rather than Predictive Analytics even as more and better tools are available for moving to predictive models
  7. 7. 7 Trends Today CFOs indicate new applications for Financial Governance are needed  Cloud and Mobile are cited as opportunities BUT 91.8% of all devices connecting to the web were PCs in 2012 – only 5.2% were smartphones, and 2.5% were tablets; Predictions for 2014 are over 80% of devices connecting will still be PCs  Big Data is a much talked about subject BUT 90% of Big Data projects are failures
  8. 8. 8 BI Investment Needs
  9. 9. 9 CFO 2013 Initiatives
  10. 10. 10 2014 Enterprise Initiatives
  11. 11. 11 Initiatives in Finance Today
  12. 12. 12 The Data Dilemma
  14. 14. 14 Making sense of it all Clarity of purpose ! Definition of scope Allocation of resources Concrete result expectations ! Comparative Analytical Measures (e.g. KPIs) ! Rationalization of measures into actionable items and hierarchical groups Defining predictive analytics workspaces
  15. 15. 15 Metadata and Contextual Analysis
  16. 16. 16 Social Network Diagram • Contextual analytics is one of the hottest areas of interest pertaining to big data today • Smart companies know there is tremendous value in contextual analytics. But aggregating, categorizing, summarizing, exploring and contextualizing unstructured data is a big undertaking.
  17. 17. 17 Contextualization  Context is the interrelated conditions in which something exists or occurs . Helping define context is Environment, Setting, Timeline, Genre  Why is context important?  Consistency needed in returned result sets  The context describes the internal or external “framework”  Internal contextual information is crucial  External contextual information is knowledge that which cannot be gotten from the text of the item itself  Time and resources are wasted in searching irrelevant and non-material information
  18. 18. 18 Business Goals
  19. 19. 19 Vision to Execution Road Map Alignment 3-Year IT Vision – Business Plans Business Context FY 1 Plan FY 2 Plan FY 3 Plan IT Strategies; Business Outcomes with Target Results; Goals and Objectives; Risks; Business Capabilities; Key Stakeholders; Committed Investments; Financial Models Business Outcome Milestones IT Context Planning & Analysis IT Business Model, IT Services Portfolio, Major Initiatives/Programs, IT Operating Model, Performance Target Results Enterprise Architecture IT Services Applications Common Services Processes Data / Information Infrastructure Shared Services (SIS) Updates to FY 1 plus New Capabilities, IT Services, Portfolios Refresh, and Performance Targets Updates to FY 1 plus New Capabilities, IT Services, Portfolios Refresh, and Performance Targets ISS FY Planning SIS Catalog App Services Compute Storage Content Delivery Support Services Networking Deployment Management & Monitoring EIM & Enterprise Endpoints Security Management Project portfolios sync'd to ISS releases Cross-stack road maps Reference architectures & solution patterns Standards & principles Currency schedules & road maps Budgeting/investment/resource profiles AppDev, security and integration guidelines
  20. 20. 20 Align Change/Development/Investment to Business Outcome Milestones Business Solutions Approach Business Unit Approach Architecture Approach People BU 1 Application Process BU 2 Infrastructure Technology BU n Cloud Services Information Region 1 Information Governance Region 2 IT Services Money Corporate Shared Services Business Processes Manageme nt Technical Shared Services Supply Chain Partners Business Outcome 20 Milestone (final or interim) Functional Milestone within a level of analysis (depends on approach)
  21. 21. 21 In Order to Realize New Opportunities, You Need to Think Beyond Traditional Sources of Data Transactional and Application Data Machine Data Social Data Enterprise Content  Volume  Velocity  Variety  Variety  Structured  Semi-structured  Highly unstructured  Highly unstructured  Throughput  Ingestion  Veracity  Volume
  22. 22. 22 Framework for A BI Support Center
  23. 23. 23 An Analytics Framework Integration User Interfaces Databases Visualization Dev Tools Application Accelerators • Spreadsheet-style visualization for data discovery & exploration Content Management • Built-in IDE & admin consoles • Enterprise-class security Engine Components Map Reduce + Indexing Workload Mgmt Security Apache Hadoop • Unique text analytic engines Admin Console Accelerators Text Analytics • Performance & workload optimizations Information Governance • High-speed connectors to integration with other systems/sources • High availability • Machine data and social data analytics accelerators
  24. 24. 24 Data Exploration  Discover the Data  Provide discovery and navigation  Connect securely to applications that manage data—regardless of location  Leave big data in place File Systems Application/ Users  Identify the value of the data  Recognize users of the data  Establish context of data usage Commenting Tagging Data Explorer Platform  Analyze Structured and Unstructured Data  Visualize relationships and reveal themes Relational Data Content Management Hadoop System Stream Computing Data Warehouse ERP Cloud  Create personalized views of the data Systems Management Supply Chain Shared Folders  Augment the data with user knowledge Application Development Accelerators CRM RSS Feeds Social Tools Visualization & Discovery Email Rating  Collaborate on the Data Big Data Platform Custom Sources External Sources  Identify ongoing user and system integration points 24 Information Integration & Governance
  25. 25. 25 Analytics and Predictive Analytics
  26. 26. 26 Historical Analytics  Presentation of historical data  Dashboards, Drill-downs, interactive reports, static reports  New methods and devices  Identifying the metrics that affect key objectives  Synchronizing those metrics through an organization  Creating user tools to show effects of good (and bad) choices  Tying the financial, operational, and sales worlds together  Analyzing to predict the future  Refining models for accuracy
  27. 27. 27 Predictive Analytics  Manipulation of data  Dashboards, Drill-downs, interactive reports  New methods and devices  Varying the metrics that affect key objectives  Synchronizing the impact of metrics through an organization  Creating user tools to show effects of good (and bad) choices  Tying the financial, operational, and sales worlds together  Creating models that show potential future scenarios  Refining models for accuracy using advanced tools and statistics
  28. 28. 28 Governance Issues
  29. 29. 29 Issues        Data Quality Repeatability Best Fit Models Naming Consistency Formula Consistency Calculation Consistency Plugging Numbers (see Pentagon*)
  30. 30. 30 Sample Product stacks
  31. 31. 31
  32. 32. 32 Analytics Toolscape
  33. 33. 33 A Vendor Sample Toolbox  Data Exploration: Visual data exploration to quickly understand and analyze data within the database  OLAP Optimization: Built-in multidimensional analytics optimization  Geospatial: Native in-database geospatial data types and analytics  Temporal: Native in-database temporal support to manage and update time data and analytics  Advanced Analytics: Optimized in-database data mining technology from leading vendors, open source, and Teradata  Agile Analytics: In-database data labs to accelerate exploration of new data and ideas  Big Data Integration: Partner tools to analyze unstructured and structured data  Application Development: Tools and techniques to accelerate development of in-database and Hadoop analytics
  34. 34. 34 Sample Big Data Appliances IBM - Pure Analytics (Netezza) Oracle – Exodata, Exalytics SAP – Hana Teradata Other vendors entering the marketplace Sample Toolkits IBM – InfoSphere, Open source, Hadoop Oracle – Hyperion, OBIEE, Hadoop SAP – BO, BPC Teradata – Vision, Hadoop SAS – BI Software, BI Analytics, Visual Analytics Tibco – Spotfire
  35. 35. Thank You 35 720 326 9422