Driving Business Performance with effective Enterprise Information Management

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Using data quality to drive effective business performance. The Data Quality Associates way, shared on http://www.dataqualityservice.com


  • 1. A talkbook on Data Governance and Data Quality Management Driving Business Performance with effective Enterprise Information Management

2. Internal Notes Delete before showing to the clients The purpose of this presentation is to introduce clients or prospects to our capabilities in delivering Enterprise Information Management solutions. It is intended for use by non-subject matter experts or SMEs to showcase our capabilities and solutions and to lead prospects to the next conversation, which should be with a SME. Additional slides on our Enterprise Information Management Framework and service offerings can be included as an appendix to this presentation. If your client is ready for a more detailed discussion, you can customize the presentation through the use of these additional slides. Lastly, where ever applicable, the messaging notes can be found in the speaker notes pages. Please delete these notes before sharing electronic copies of the presentation with external parties. 2 3. Contents Market View on Enterprise Information Management Common Enterprise Information Management Challenges and Drivers Our Information Governance Framework and Service Offerings Introduction to our Information Governance Framework Information Lifecycle Management (ILM) Data Governance Framework and Organisation Structure Data Ownership and Stewardship Master Data Management Data Classification Data Flow Analysis Data Quality Management Our Engagement Methodology Appendix- Our Point of view on Business Intelligence v/s Enterprise Information Management Appendix - Other Supporting Slides / Contents 3 4. Market View on Business Intelligence and Enterprise Information Management 5. Business Intelligence and Enterprise Information Management - Defined 5 Our point of view is a little less complicated. Business Intelligence (BI) empowers the right people to receive the right information, at the right time, allowing them to make the right business decisions. Enterprise Information Management (EIM) provides the foundation for the business to operate as truly intelligent enterprise. Business intelligence (BI) is an umbrella term that includes the applications, infrastructure, tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. Gartner Enterprise Information Management (EIM) is an integrative discipline for structuring, describing and governing information assets across organizational and technological boundaries to improve efficiency, promote transparency and enable business insight. Gartner Enterprise information Management (including Data Quality Management and Data Governance) is the single most important prerequisite to a Business Intelligence implementation. Without the proper data, or with too little quality data, any BI implementation will fail. Before implementation it is a good idea to do data profiling; this analysis will be able to describe the content, consistency and structure - Kimball et al., 2008 1 2 3 6. Executives are focused on BI 6 1. Gartner EXP 2012 Survey of CIOs 2. MIT Sloan Management Review : Findings from the 2010 New Intelligent Enterprise Global Executive Study and Research Project Analytics: The New Path to Value Business Intelligence continues to be the top priorities for the CIOs across the globe.1 Over the next 24 months executives say they will focus on supplementing standard historical reporting of data with emerging approaches that convert information into scenarios and simulations that make insights easier to understand and act on. 2 By 2013: 33% of BI functionality will be consumed via handheld devices 15% of BI deployments will combine BI, collaboration and social software into decision-making environments *Gartner Research 7. . but BI has not delivered on the promise. 7 1. Coming Up Short on Non Financial Performance Measurement, Ittner and Larcker, HBR 2. Does your business intelligence tell you the whole story? - KPMG International. Your BI solution may have you looking at the wrong information. 1Fewer than 10 percent of organizations have successfully used BI to enhance their organizational and technological infrastructures. 2 3 4 More than 50 percent of business intelligence projects fail to deliver the expected benefit. Two thirds of executives feel that the quality of and timely access to data is poor and inconsistent. Seven out of ten executives do not get the right information to make business decisions. 70% of companies employ metrics that lack statistical validity and reliability.1 While 95% of companies forecast cash flows, only 14% of cash forecasts are accurate.2 According to KPMG International, the execution of business strategy is often hampered by a lack of reliable information2: 8. Accuracy of Data continues to drive most Business Intelligence and Information Management Projects 2% 12% 15% 16% 17% 52% 56% 57% 60% Other Reduced risk of noncompliance Reduced risk to business performance Greater control Greater flexibility More-timely, accurate indication of future performance Improved efficiency More-robust analytical capabilities High-quality, more-reliable management reporting Multiple responses permitted. Source: CFO Research Services/Lawson Software 2010 0% 10 20 30 40 50 60% Areas of prioritized spend 8 Ensuring the accuracy of the information reported form a business intelligence system is another central theme that organisations encounter. Integrating information from across your enterprise while keeping the quality of data intact from record to report has revealed a range of issues not previously apparent. The executives are now focused on making their BI solutions more reliable by through an information management agenda. 9. Organisations are using an information agenda to plan for the future 9 Highest data priorities for Organization: Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute for Business Value study. Copyright Massachusetts Institute of Technology 2010. Information Governance: In order to create truly valuable business intelligence, organizations should clearly define who owns, uses, and produces information and how it is presented. Such tight ownership and control should help give consistent, accurate reports and allow fair, like for like comparisons of performance. Information Integration: The sheer volume and types of data an organization generates has grown enormously. Managing this data effectively and being able to rely on its accuracy is now even more critical to businesses. Simplification is a one of the most significant component of the information agenda. Complexity should not be viewed as a burden to be avoided; it should be seen as a catalyst and an accelerator to create innovation and new ways of delivering value - Juan Ramon Alaix, President, Pfizer Animal Health - IBM 2010 Global CEO Study. 10. Business Intelligence is more than just reporting 10 Valued Business Information Dashboards, monitoring, in sight KPIs, scorecards Real time reporting Supporting Framework Data Governance Data Quality Information Integration Reporting and Data Management Platforms Infrastructure Database, Security, ETL There are many benefits to the business at the surface, however a sustainable and defined infrastructure and governance framework is required to support the consistent delivery of effective Business Intelligence and Performance Management information. 11. Common Enterprise Information Management Challenges and Drivers 12. Common Business Information Challenges Everyone is effected 12 CEO CFO Board & Management CxO CIO Associates Customers & Suppliers I cant get the information I need quickly enough to react to the events and changes in the market conditions. Root Cause: Data Governance Framework & Organisation Structure, Data Ownership and Stewardship and Data Flow Analysis. It is too difficult to obtain all the information I need to make better decisions. Root Cause: Data Classification, Data Flow Analysis, Data Mapping and Data Modeling. I dont have enough confidence in some of our information to make critical decisions. Root Cause: Data Quality Management and Enterprise Information Strategy . We run this business by gut feel rather than facts. Root Cause: Data Quality Management, Information Integration and Distribution. I receive a multitude of reports with conflicting information so we waste time debating which measures are correct instead of making decisions. Root Cause: Data Quality, Data Standardization and Master Data Management. Majority of our analysts time is spent gathering data instead of analyzing and the information to create insights. Root Cause: Data Quality and Data Ownership/Stewardship. Your organization does not understand the full breadth of the relationship your customers and suppliers have with you. Root Cause: Master Data Management , Data Flow Analysis, Information Lifecycle Management and Enterprise Information Strategy. All most all executives within your organisation are effected by the quality of your organizational data. For Industries that processes huge amounts of data on a daily basis, Data Quality can make or break an organization. Some of the key information management challenges faced by organizations are: Information Consumers 13. Common Business Information Challenges The root causes are also common Common and critical data resides in separate systems Data is incomplete and critical information is not captured. Obsolete Data. Reference data is not consistent across systems. Data content differs from actual business rules. Data does not reconcile across all integrated systems. The same information is captured from multiple system Inconsistent definitions and standards No common definition of data, including customer data. The same data is not captured in a consistent format across the organisation. Difficult or impossible to consolidate information for cross department / cross geography use. Limited Analytical Capability As the data is not completely captured, the ability to analyse data is limited. Restricted ability to discover potential opportunities for cross-sell and up sell using the current available data. Lack of knowledge of the affiliates with repeated transactions. Income/spend patterns and trends are not easily available. Unclear Data Ownership No formalised governance policy in place. The data ownership and consumption of data is not clearly defined. Manual Processes and Limited Sharing of Data There are high degree of manual processes in place and limited sharing of data between countries and systems. Lack of knowledge for end-to-end process. ChallengesRootCause 14. The Need for Enterprise Information Management Why is information management so important to me? Many organisations fail to effectively manage their data, resulting in greater risks to the business and missed opportunities for commercial and competitive advantage. Effective and innovative data governance and data quality management can help you to reduce the risks and realise the true potential of your organisation's information: 14 o Improved customer profitability and product coverage through single customer view and product insights. o Reduce risk (financial and reputational) through improved data quality, control and security. o Better informed planning based on accurate operational and forecast data o Enhanced anti-fraud measures through linking and forensic analysis of structured and unstructured data. o Auditable regulatory compliance and repeatable decisions on large data- intensive. o Early risk warning systems to continuously monitor and improve operational efficiencies and profitability. o Technology-enabled-solutions that are tailored to your needs and enables you to focus on the most relevant information. 15. Driver for Information Governance 15 Drivers Pressures Information Governance Value proposition Increasing Business Value Merger and Acquisition Activities Business Unit Consolidation/Diversification Off-Shoring/Outsourcing Business Process Improvement Administrative Process Improvement Consistent Security and Compliance Practices Quicker Rebranding of Services Reduced Lost Productivity Reduce Cost of Compliance Improved Business Process and Workflow for Data Improving Compliance Federal and State Privacy regulations HIPPA Safe Harbour COPPA FCRA/FACTA NERC/FERC Improved Compliance Monitoring Compliance automation Improved Auditing and Logging Flexibility to Adapt to New Regulations Improved Compliance Reporting Reducing Risk Regulatory compliance Breach of client, employee data or Intellectual Property (IP) Third Party Management Information Asset Management Increased Security Risks Compliance industry regulations Protection of Critical Data Elements Understanding of Data Location and Flow Improved Control of Information Assets Brand protection Better Enforcement of Policy and 3rd Party Management Containing Cost Breach Recovery/Management Costs Management of Legacy Documentation Consolidation of IT Data Repositories Consistent Security and Compliance Practices Information Life Cycle Management Reduced Costs, Resources Classification of Data Elements 16. Our Information Governance Framework and Service offerings 17. Our Information Governance Framework 17 Our Information Governance Framework is designed to strike the right balance between technical and functional infrastructure for effective information management. There are seven components to the framework, which, although interdependent, can also be applied individually. Our Information Governance Framework also unpins our service offerings that help us to assist our client with designing personnel, process, technology, and controls that address compliance requirements, while also protecting the most important information assets Our approach encompasses the complete governance lifecycle, helping to enable clients to choose the appropriate services to achieve their specific business needs. Information Lifecycle Management Data Governance Framework & Organisation Structure Data Ownership and Stewardship Master Data Management Data Classification Data Flow Analysis Data Quality Management Technology 18. Key Components of Our Information Governance Framework 18 Information Lifecycle Management (ILM) The core of our approach focuses on ILM which comprises the policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost effective IT infrastructure -- from the time information is conceived through its final disposition. Data Governance Framework and Organisation Structure The process of defining the roles / relationships and information management organization structure. It helps to drive ownership rules, set standards and direction for data management and create data quality standards. Data Ownership and Stewardship The process of understanding the data and establishing standards and measurable goals to help improve the quality of the data by evangelizing the leading practices across the organization. Master Data Management The process of consistently defining and managing the information that is key to the operation of your business covering customers, products, employees, materials, suppliers etc. Data Classification The process of dividing data sources (documents, applications, databases, etc.) into groupings to which defined level of controls, protection and policies can be applied to support business objectives. Data Flow Analysis The process of classifying and managing the flow of information assets within an organization. Data Quality Management The process of accessing, designing, improving, monitoring and measuring the duality of data across the organization. 19. Information Lifecycle Management Establishes the Information Management Vision and Translates it to Measurable Execution. 19 Phase 1 Generation Ownership Classification Governance Phase 5 Storage Access Control Structured versus Unstructured Integrity/Availability/Confidentiality Encryption Phase 2 Use Internal versus External Third Party Appropriateness Discovery/Subpoena Phase 3 Transfer Public versus Private Networks Encryption Requirements Access Control Phase 6 Archival Legal and Compliance Offsite Considerations Media Concerns Retention Phase 7 Destruction Secure Complete Compliance Audit & Regulatory Legal Measurement Business Objectives Phase 4 Transformation Derivation Aggregation Lineage Integrity At the core of our framework lies the Information Lifecycle Management (ILM) which helps us to gain an understanding of the risks associated with your information across its lifecycle. Key considerations are as follows: 20. Information Lifecycle Management Service Offerings and Sample Deliverables 20 It may take some time for the business to realise that the Data Governance program will create an opportunity for them to improve the overall information quality across the organisation. Practical considerations will come to light when you put into practice the Governance principles and bodies defined in this phase. Be prepared for scope and requirements to change overtime. Ownership and accountability is critical; if no one owns it, it will not get done. Create a risk mitigation plan in the event of a critical lack of available documentation on how owns what information and how is it managed. Emphasize training and internal awareness. Many user satisfaction issues may be overcome by end user training and increased awareness about the Program. Limit the number of instantiated policies and preserve the key capability to customize the management of each individual piece of personal data, based on users privacy preferences. SAMPLE DELIVERABLES AND TEMPLATES Information Lifecycle Gap Analysis Information Lifecycle Improvement Roadmap Change Management Program Data Standardization Process/Bus. Alignment KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Information Life Cycle Management Information Risk and Gap Assessment Information Improvement Roadmap Stakeholder and Change Management 21. Data Governance Framework and Organisation Structure Defines the governance model that supports value delivery and serves the needs of the business. 21 Executive Sponsors Data Governance Manager Business Process Owners IT Systems Owners Governance Team Subject Matter Experts Data Administrators IT Administrators Data Stewards and Data Owners Technical Analyst Business Analyst Data Quality Analyst Data Quality Assurance User Community Data Governance is how an enterprise manages its data assets. Key step within Data Governance is to identify the Information stakeholders (e.g. management, investors, supervisors, auditors) A Data Governance organisation structure primarily made up of the Governance Committee, who are responsible for project oversight, Data Stewards and a Data Quality Group. This example below can be tailored to fit the current organisation structure of our clients, leveraging current existing roles wherever possible. Set direction, strategy and goals for the council globally (e.g., across regions, Sales and Marketing ,etc.). Champion the councils mission and purpose to the corporation. Set Business and IT direction for data architecture and underlying infrastructure Data Stewardship, Corporate data standards, data repository, data architecture and database administration. Data and ownership rules validation Data quality processes and tools Data requirements, data ownership (Location A, Location B, Sales, Marketing, etc.) 22. Invest in resources for participation in the information gathering process. Key project team members such as the Project Manager, Architect(s), Business Analyst(s) and Data Analyst(s) should participate in as many of the meetings as possible, even if the planned subject matter may not seem to be directly applicable to every resource. Site visits to organizations that have mature Information Governance models in place to bring theory to life and crystallize good applicable features into the future design may be a useful tool. Share findings and initial impressions across the program to test initial hypothesis. It is critical to obtain the clients agreement and sign off for the governance framework to gain consensus and the appropriate level of stakeholder support. Mapping current organization to the future organization in the early stages of the project can help ensure efficient transition to the future state. Businesses will incur learning and education costs as they familiarise themselves with the new reporting methods. 22 Information Governance Process and Procedures Data Governance Org Structure Governance Framework The Model to Guide decision making Data Governance Framework and Organisation Structure Service Offerings and Sample Deliverables SAMPLE DELIVERABLES AND TEMPLATES Data Governance Strategy Definition Data and Information Governance Process and Procedures Data Governance Organisation Structure Definition Data Governance Framework Definition KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Data Governance Framework & Organisation Structure 23. Data Ownership and Stewardship Articulates Roles and Responsibilities, and helps to keep the Measurement and Analytics Function Synchronized Across Business Units and IT. 23 Effective Definition of Data Ownership can help to: Understand the data and the purpose of the data in a specific system. Provide contextual understanding of the data in a particular system. Comprehend the data and various data elements in the context of the business purpose of the system. For initial data ownership focus, start with Accounts and Contacts across systems. Data stewards play a crucial role in defining the organizational data elements and subsequently the conceptual data model for an organization. Data Stewards act as the liaison between IT and the business and accept accountability for data definition, data management process definition, and information quality levels for specific data subject areas. Later, the technology team translates this conceptual data model into technology solutions. Data Stewardship Responsibilities Monitor and report data quality issues Review error and exception logs Correct data gaps (i.e., missing data) Randomly audit data Maintain and fine tune data transformation and matching rules 24. When differences occur between sources of information it is often very difficult to gain consensus in an organization as to which version of the truth is correct. It is necessary to exercise caution to obtain information at or as close to the source as possible and to avoid hidden filters, views, stored processes or transformations when profiling data. It is important to obtain sign-off on the correct sources for the data. Assessing the Corporate Strategic Plan will provide insight as to goals and objectives to be obtained. They will be at a high level but should be able to be broken down to lower levels in the individual business units. Consider co-coordinating structured interviews and meetings across all workstreams and socialize the design with key stakeholders to get business buy-in across the workstreams. A refresher training of the key participants has to be undertaken closer to the go-live time to help ensure knowledge is retained. Early Communication about the new Governance organization will help ensure that people are better prepared and are involved in the implementation early in the process. 24 Data Ownership and Stewardship Service Offerings and Sample Deliverables SAMPLE DELIVERABLES AND TEMPLATES Data Ownership Matrix Definition Data Stewardship Structure Definition Change Management Program KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Data Ownership Matrix Stakeholder and Change Management Data Stewardship Structure Data Ownership and Stewardship 25. Master Data Management Uncover value creation opportunities in master data management and data standardization, and reduce the cost of ownership. 25 Master Data Management Processes Supporting Process LEGEND: Routine and Planned Maintenance Ongoing Review of policies and procedures, business rules, system improvements and training material Process and data quality audits Event Triggered Tasks Business or process changes Impact on value lists Impact on mandatory fields New reporting requirements Data quality issues identified System changes Master Data Maintenance Approve and Create Approval to make change Create new record in Master table Quality check on trigger data Data owners contribute key values Control and validation of attribute values Implementation Tasks Review key forms Create Data Dictionary - Define mandatory fields, define attribute values, establish test for duplicates Policy & communication Purging and archiving guidelines Modify and Update Approval to make change Enter changes Changes controlled in same manner as the create process Delete and Archive Identification of inactive records Setting of record status based upon set rules Archiving of records Master Data Operations Source Systems Analysis & Extraction Master Data Analysis (profiling) Master Data Design Root Cause Analysis Establish MDM Process Flows Remediation Action Plan Identify and Develop Data Standards Data Governance and Stewardship Define Global Data Flow Master Data Strategy MDM Vision and Sponsorship High-level Profiling Implementation Roadmap Our methodology for Master Data Management (MDM) provides a holistic approach to manage master data across the entire organisation it is well supported by other information management processes. 26. Master Data Management Service Offerings and Sample Deliverables 26 There is no single approach to addressing metadata. An iterative approach may be used to increase functionality gradually through a series of planned releases. Any harmonisation of data definitions would require changes. Often business units are only willing to participate in harmonisation exercises to the extent that everyone else agrees to their existing definitions. Context is extremely important. In addition to Integrated Information Management skills, the project team should have knowledge of the domain and/or industry knowledge. Plan for substantial changes to the physical design during the Implement Phase as data sourcing and data access construction activities mandate modifications to the Operational Data Stores, Data Warehouse or Data Mart. Data Management and Governance are more than data tools - to be effective they should encompass people, process, all types of data, technology and tools. Leverage the Information Lifecycle Management to determine what information is critical to link and support the operational data. SAMPLE DELIVERABLES AND TEMPLATES Master and Meta Data Management Strategy MDM Design (Data Dictionaries and Data Mapping) MDM Standards and Ownership Design MDM Tools Selection Assistance MDM Platform Implementation and Integration KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Data Dictionary Master Data Definition and Mapping Meta and Master Data Models Master Data Management 27. Data Classification Clearly identifies the information and how to progress towards improved data quality/control and underlying material factors of influence. 27 Organizational data (including Master Data and Meta Data) should have a common definition across the enterprise to operate as a truly unified, consistent, and efficient organization. Our comprehensive Information classifications and Control model can help you identify and prioritize the execution of various information management initiatives Information Classification and Control Model Attributes of Information Definition Accuracy/ Quality Risk Timing Source Usage Disposition Maintenance Security Redundancy The following attributes should be considered while defining the Information Classification and Control Model: Definition - A brief statement that describes the information Accuracy/Quality - An appraisal of the confidence one can have in the information Risk - A statement to highlight the risk associated with the information Timing - A discussion of frequency, regular of irregular, etc. Source - Where did the information originate? Usage - How is it used and by whom? Disposition - Where is the information sent? Maintenance - A description of the activities required to keep the information current Security - A statement of the privacy / confidentiality issues surrounding the information Redundancy - Are there too many versions of the same information? 28. Data Classification Service Offerings and Sample Deliverables 28 Do not permit data gaps to go unresolved since they will require changes to the logical and physical models and may even require revisiting project success factors. Ensure the level of detail is sufficient to help enable the next phase (Build) to take place. The difficulty is to strike the right balance and not to get to the build level but at the same time to define sufficient detail (all thinking should be finished at this stage) Well-established designs, tools and technologies are suitable and innovations should be analyzed for risks and architecture components. Data Management and Governance processes expand beyond MDM to include all data types (structured/unstructured, internal/external) and flows of data. Process and/or technology led transformations should take into consideration the impact on supporting operating model. Organizational issues need to be addressed in parallel with process and technology considerations. Information flow lead time is a critical consideration when designing the processes as it has a direct impact on decision making SAMPLE DELIVERABLES AND TEMPLATES Information Compliance Assessment Information Classification Matrix Information Risk Assessment Governance Controls Design Data Standardization and Quality (including Data Cleansing and Integration) KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Data Precedence Matrix Information Classification Matrix Data Availability Matrix Data Classification 29. Data Flow Analysis Visualize the operational elements for enterprise information integration and infrastructure enabling successful delivery of information to the business. Mapping the flow of information from record to report is critical for the ensuring the quality, accuracy and completeness of the information presented through reports. Working across business functions and aligning with process initiatives, we deliver and end-to-end mapping of your organization's information 29 Dimensional or third normal form (3NF) Approaches, Business, Con ceptual and Physical Data Models. Data Mapping, Data Flow Conceptual Architecture and High level ETL Design Conceptual Data Repository Architecture (Operational Data Store(ODS), Enterprise Data Warehouse (EDW) and Data Marts Master Data Management Processes, Standards and Information Access Privileges Future State of Data Flow should consider: Efficiently managing the flow of information can help you address the following CHALLENGES: Islands of Information Most organizations suffer from information proliferation and duplication. Each island is assessed for its viability and duplicate systems are rationalized, combined, or eliminated. If data I need is stored in several different places, how can I perform comprehensive analysis? Data Management Who owns the data? Data Quality How will we enforce the format in which data is entered? Accountabilities Who is responsible and accountable for the data? Stakeholder Management Who is impacted by the use of the data and what expectations need to be set for the delivery of the information? 30. Data Flow Analysis Service Offerings and Sample Deliverables 30 Avoid jumping to technological solutions until the business objectives and the requirements are well understood. Analyzing the details and complexities of the numerous data elements is the longest and most labour intensive component of Data Governance projects. Care should be taken to manage the scope and estimated effort for this activity. It is important to use a realistic baseline of the maturity level of the clients business and information technology environments. Establish early the priorities for improvement as well as understand the constraints applicable. Plan in advance for any sample data or data analysis requests related to data profiling including adherence to all policies and procedures. Prototyping can be very valuable and prototyping opportunities should be considered where there is a compelling benefit (including risk mitigation) that outweighs the costs. The quality of the source-to-target mappings has a direct impact on the data integration design and implementation.. SAMPLE DELIVERABLES AND TEMPLATES Information Flow Process Maps Data Mapping and Data Modeling Enterprise Information Strategy and Conceptual Design Prototyping and Visualization: ETL Design and Development (Extract Transform Load) Data Warehouse Assessment and Design ETL and Data Warehousing Tools Selection Assistance KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Information Flow Process Maps Data Mapping Matrix Conceptual Data Flow Architecture Internalsourcesystems Eclipse Elgar BUKS I90 Investment products Extract Extract Extract Extract Extract Maxim iser Extract Great Plains Extract Landing Zone Landing Disk Area Staging Area Surrogate key assignment Data Quality Checks Validation & Reconciliation Change capture Business rules Sanity checks Archival & Retention Backup Archive Backup Staging Data Warehouse Databases Archival & Retention Backup Archive Backup Reporting Structure 1 Reporting Structure n . . . Loading (DW) Loading (RS) Applications (BI, Ad-Hoc Querying, etc.) Reporting Ad-hoc queries Reporting 1 Reporting n Reports . . . Extracting Data Modeling Data Flow Analysis 31. Data Quality Management Enable business to deliver high information to the executives when they need it, while continuously improving its quality and the associated processes. 31 Measure Data Quality Implement continuous monitoring dashboards in the environment Access Data Quality Determine information/quality requirements / KPIs Identify data quality issues Identify controls to improve data quality Design Quality Improvement Processes Determine information/reporting requirements / KPIs Develop continuous monitoring dashboards in your proof-of-concept environment Implement Quality Improvement Processes Clean-up existing data quality issues Implement controls in the environment to prevent future data quality issues (e.g. field validations, segregation of duties, mandatory fields etc.) ASSESS DESIGN MEASURE IMPROVE Data Quality Management Our Data Quality Management services provides a structured approach to achieve data quality across multiple systems and processes. Scoping and definition of data Prioritization framework for identification of key risk and control areas Standardized approach to the development of automated monitoring of data quality and implementation of data quality improvement measures (remediation processes and data cleansing/transforming activities) 32. Data Quality Management Service Offerings and Sample Deliverables 32 Follow the leading enterprises that focus their Information Governance Vision on leveraging data and information as an asset to execute their core strategic objectives The quality of the source-to-target mappings has a direct impact on the data integration design and implementation. Poor Quality of data simply transfers the load to other solutions and may have a wide spread effect throughout the organisation. A central and widely accessible document repository of Governance documentation such as, for example, e-rooms makes this effort more transparent to clients stakeholders. Establishing a clear baseline for current business processes is critical for reducing complexity and identifying root causes, gaps, critical requirements, and alignment issues Specialized skill sets can be very helpful to assess architectural components and interfaces. Resources outside the project team such as vendors, industry groups, partners and other organizations should be leveraged where necessary. Preserve the design of the data quality framework as much as possible and document decisions resulting from constraints or limitations of technology components. SAMPLE DELIVERABLES AND TEMPLATES Data Quality Readiness Assessment Data Quality Gap Analysis Data Quality Improvement Roadmap Data Standardization Process/Bus. Alignment Data Security Assessment and Design KEY COMPONENTS OF OUR SERVICE OFFERING LESSONS LEARNT Data Standardization Process/Bus. Alignment A key element of the measurement framework is the linkage of process and business measures. Mobilise, data gather, Strategy Map Workshop preparation Strategy Map Workshop Mobilise, data gather, Strategy Map Workshop preparation Strategy Map Workshop CoB Business and Process measures Agree final Business KPIs and Performance Measures CoB Business and Process measures Agree final Business KPIs and Performance Measures Document and confirm the output from the Strategy Map Workshop Refine Process Performance Measures Document and confirm the output from the Strategy Map Workshop Refine Process Performance Measures Business Lens Process Lens The aim is to take strategy maps and work with the Streamline process teams to align process outcomes and measurement to those strategy maps Document and confirm the output from the Strategy Map Workshop Refine Business KPIs Document and confirm the output from the Strategy Map Workshop Refine Business KPIs Data Quality Gap Assessment Data Quality Improvement Roadmap Data Quality Management 33. Our Engagement Methodology 34. Data Governance Framework & Organisation Structure Data Ownership and Stewardship Master Data Management Data Classification Data Flow Analysis Data Quality Management Initiation Sustenance Maturity Onsite 100%, Offsite: 0% Duration: 3-4 Weeks Onsite 50%, Offsite 50% Duration: 6-8 Weeks Onsite 30%, Offsite 70% Duration: 12-20 Weeks Our Engagement Methodology 34 Feedback to adjust Information Management Strategy and Roadmap Change Management and Communication Project Management Ongoing Data Quality Improvement and Support Access Current State Understand the business direction and Information Strategy Identify As-Is capabilities for Information Management Perform Maturity Assessment for Information Management Design and Plan Future State Identify To-Be Requirements for Information Management Conduct Gap Analysis and identify Improvement Initiatives Build an Information Management Strategy and Roadmap Implement key initiatives (Quick Wins) Validate Requirements and Design Build improvement Initiatives Test, Train and Deploy Implement other initiatives (Reliable Information Management) Validate Requirements and Design Build improvement Initiatives Test, Train and Deploy Feedback to adjust Information Management Strategy and Roadmap Our Information Governance Framework 35. Our Engagement Methodology 35 Strengthen Processes/Controls Reduce # of Vulnerabilities Reduce Incident Costs Information Ownership Reduce Process Time Information Sharing & Availability Integrate Key Processes Reduce Litigation Costs Lower IT Infrastructure Costs Reduce Error Costs Reduce Errors & Mishandling Increase Process Efficiency Cultural Change/Accountability Better Cheaper Faster More Secure Controls Across Lifecycle Controls by Sensitivity Controls Designed for Risk Reduce Implementation Time Effective Communication and Cooperation b/t business units Streamline Acquisitions Lower Audit Costs Decrease Cost of Control Lower Insurance Premiums Project Management Sustain Compliance Optimize Portfolio Management With our broad ranging Information Management Methodology, we can run a program for you that can lead to many benefits: 36. Appendix Our Point of view on Business Intelligence v/s Enterprise Information Management 37. An Integrated Approach to Business Intelligence When aligned with business goals and well executed, BI enables an organization to harness performance drivers and risk and to make better, more timely decisions. An integrated Business Intelligence approach can help our clients to realize the full business value of their information. A Business Intelligence offering can be divided into three key components 37 Enterprise Information Management Performance and Risk Management BUSINESS INTELLIGENCE Analytics and Decision Support Our perspective expands the focus of BI from being a tactical solution to broader organizational capabilities that allow organizations to: Improved customer profitability and product coverage Reduce risk (financial and reputational) Better informed planning Enhanced anti-fraud measures Auditable regulatory compliance and repeatable decisions on large data-intensive Early risk warning systems Technology-enabled-solutions that are tailored to your needs and enables you to focus on the most relevant information 38. Performance Management Planning and Analysis Integrated Reports & Consolidation Risk Management Key Functionalities Addressed by BI 38 Key Performance Indicators (KPIs)/Metrics/Measures Advanced Analytics Dashboards and Reporting Data Visualization Enterprise Information Management Performance and Risk Management BUSINESS INTELLIGENCE Analytics and Decision Support Enterprise Information Management is the collection, organization, and distribution of all types of information to deliver business value to an organization Data Governance Data Quality Data Integration Data Integration Platforms Information Access and Distribution The convergence of Performance and Risk involves shifting BIs objective beyond reporting to delivery of information that enhances the business performance outcome while minimizing risk Analytics and Decision Support represent the ability to acquire, consolidate and transform relevant information into knowledge 39. Enterprise Information Management 39 Enterprise Information Management Performance and Risk Management BUSINESS INTELLIGENCE Analytics and Decision Support Data Governance the process that articulates Roles and Responsibilities, and helps to keep the Measurement and Analytics Function Synchronized Across Business Units and IT Data Quality managing information as a corporate asset will maintain and enhance its value, using quality-driven organizations, processes, standards and supporting technologies Data Integration includes the collection, organization and distribution of all types of data, to manage the full data life- cycle needs of an enterprise Data Integration Platforms represents the set of servers, databases, software, networks and storage used to deliver and maintain information Many companies struggle to produce consistent reporting with a mass of different data in multiple formats making meaningful comparisons difficult or impossible. Effective Enterprise Information Management drives clear accountabilities regarding BI; clearly defined business performance metrics and the ongoing integration between the business units. 40. Performance and Risk Management 40 Enterprise Information Management Performance and Risk Management BUSINESS INTELLIGENCE Analytics and Decision Support Performance Management defined as the overarching activities performed with the objective of measuring, managing and optimizing enterprise-wide performance Planning and Analysis financial planning and analysis of an enterprise address end-to-end needs in financial management, reporting, planning, forecasting and budgeting processes, profitability management and strategic finance Integrated Reporting and Consolidations financial consolidation activities occur as organizations reconcile, consolidate, summarize and aggregate financial data based on different accounting standards and regulations Risk Management encompasses the processes related to identifying, analyzing and managing a wide range of business risks within an organization Enterprise Performance and Risk Management helps the organizations in the design and implementation of performance management framework including process, measures and reporting cascade, in most cases linking strategic objectives to individual performance appraisal and reward. 41. Analytics and Decision Support 41 Enterprise Information Management Performance and Risk Management BUSINESS INTELLIGENCE Analytics and Decision Support KPIs, Metrics, Measures - measures are standard unit of performance utilized to guide the tactical business decisions, these summarize into metrics which are relied upon for the operational business decisions and finally consolidated into the KPI which is relied upon to manage the strategic performance of an organization. Predictive Analytics make insights more understandable and actionable via scenario analysis, data exploration, regression analysis, discrete choice modeling, etc. Dashboards and Reporting provide a real-time insight into operational and financial performance in order to facilitate timely, well-informed business decision-making Data Visualization provides a mechanism to communicate organizational information in a clear and an effective manner through graphical means While gathering and managing business information sets a foundation of an effective Business Intelligence system; the true aim of a BI system to support business decision making. Ability to deliver the right reports, to the right people, at the right time and in the right format can make or break your BI system. 42. Appendix Other Supporting Slides / Contents 43. Common Business Information Challenges Information has never been more important 43 [ Global Economic Crisis: In the current economic climate it is vital to understand and manage performance by cutting costs to increase margins while managing your organisations risk. Organisations are focusing on effective information management to address some of the following challenges: o Know Your Customer, Anti-Money Laundering and sanctions screening processes o Transformational projects or product integration across channels requiring data to support management decisions o Internal and external reporting for customers / financial requirements o Regulatory reporting requirements o Fraud detection and anti-bribery and corruption programmes Big Data Explosion: Big data is a popular term used to describe the exponential growth, availability and use of information, both structured and unstructured. Effective Big Data analysis is the key component in providing Unique Customer Benefits (UCBs) as it can offer a treasure trove of intelligence that businesses can use to gain insights into things like subscriber behaviour and customer churn, and to improve billing accuracy and service quality. Data Privacy / Information Security: Data privacy / Security continues to remain one of the top priorities for information managers across industries. Some industries like Healthcare, Banking and Insurance are effected significantly by the way they handle customer information. Data Governance and Data Quality management can help to manage the integrity and security of information across all interfaces including: o Information exchange between businesses and customers. o Information exchange between businesses o Internal information exchange within an organisation. 44. Categories on Organizational Data and its Management 44 Term Definition Demo Data Management A thorough process of managing various types of data throughout the organization Change Management A thorough process of managing business change requests from the point when a request is logged to the point when it is implemented. Examples of requests handled by the change management process include a new report, changes to data definitions, change to workflow, etc. Master Data Sets of core business entities used in traditional or analytical applications across the organization, and subjected to enterprise governance policies, along with their associated metadata, attributes, definitions, roles, and connections. Master data covers all the traditional master data sets: customers, products, employees, vendors, parts, policies and activities. Meta Data Data about data meaning. It can contain details about the structure of database tables and objects, but also information on how data is extracted, transformed and loaded from source to target. It can also contain information on the origin of the data. Reference Data Any kind of data used solely to categorize other data. Volumes of reference data are much lower than those of master data, and it changes more slowly than master data. An example of reference data is the use of code tables consisting of codes and/or acronyms, descriptions, etc. The code tables hold information about product line, gender, country or customer type etc. Transaction Data A single piece of information related to a certain occurring activity; Transactional Data can change very often and are not constant. Examples of transaction data include purchase order number, general ledger posting, a journal amount, etc Structured Data Data that resides in fixed fields within a record or file. Relational databases, master data files and spreadsheets are examples of structured data Unstructured Data Unstructured Data that does not reside in fixed locations. Examples of unstructured data include e-mails, copies of scanned invoices, free-form text in a word processing document, etc TRANSACTIONAL DATA STRUCTURED DATA META DATA UNSTRUCTURED DATA MASTER DATA DATA MANAGEMENT


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