Financial Analytics   pafp 11-21-13
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Financial Analytics pafp 11-21-13



Trends and financial management of the analytics processes

Trends and financial management of the analytics processes



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Financial Analytics   pafp 11-21-13 Financial Analytics pafp 11-21-13 Presentation Transcript

  • 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 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 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 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     
  • 3 Trends Today
  • 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 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 BI Investment Needs
  • 9 CFO 2013 Initiatives
  • 10 2014 Enterprise Initiatives
  • 11 Initiatives in Finance Today
  • 12 The Data Dilemma
  • 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 Metadata and Contextual Analysis
  • 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 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 Business Goals
  • 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 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 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 Framework for A BI Support Center
  • 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 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 Analytics and Predictive Analytics
  • 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 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 Governance Issues
  • 29 Issues        Data Quality Repeatability Best Fit Models Naming Consistency Formula Consistency Calculation Consistency Plugging Numbers (see Pentagon*)
  • 30 Sample Product stacks
  • 31
  • 32 Analytics Toolscape
  • 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 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
  • Thank You 35 720 326 9422