Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

Enabling an Analytics-Driven Organization

Most of the attention around Analytics goes to the results of the Analytics activity - a better customer profile, a new target market, more efficient product design. What about the process and infrastructure that is needed to get the data to the point in which it is useful to the Analytics community? This presentation addresses the less glamorous, but critically important side of Analytics: the people, process and technology infrastructure that enable an analytics-driven organization.

The presentation will cover these questions:

• How do you align your information assets to your Analytics goals? What data do you need and where do you find it?
• What are the organizational constructs that need to be considered to integrate Data Governance and Analytics?
• What organizational change can be anticipated and how should it be addressed?
• How do you design your data management and data governance programs to support Analytics? How is this different than an operational use case?

This slide deck is drawn from a tutorial presented jointly with Samra Sulaiman of ConsultData at Enterprise Dataversity 2015.

  • Identifiez-vous pour voir les commentaires

Enabling an Analytics-Driven Organization

  1. 1. The First Step in Information Management www.firstsanfranciscopartners.com Enabling an Analytics-Driven Organization Kelle O’Neal kelle@firstsanfranciscopartners.com 415-425-9661 @1stsanfrancisco Samra Sulaiman samra.sulaiman1@gmail.com 202-320-9764
  2. 2. pg 2 Why We’re Here Purpose: Understand the People, Process and Technology needed to support an Analytics-Driven Organization Outcome:  Understanding how Data Management and Data Governance Support Analytics  Knowing the organizational constructs needed to trust Analytics Data  An ability to manage change  Practical knowledge that can be immediately implemented © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  3. 3. Table of Contents 1. Introduction: Clarification of terms and Level setting 2. Enabling Analytics through EDM: • Master and Reference Data Management • Meta Data Management • Data Quality • Architecture • Security and Privacy 3. Creating “line of sight” from Data to Analytics 4. Building the Organization 5. Addressing Change 6. Findings from Research: The relationship between Descriptive and Predictive Analytics 7. Summary and Wrap up pg 3© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  4. 4. What is Analytics? Data Insight Action © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 4 Our Focus Today
  5. 5. pg 5 The Big Picture: EIM Framework © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Provides a holistic view of information in order to manage information as a corporate asset Enterprise Information Management Information Strategy Architecture and Technology Enablement Content Delivery BI, Performance Management , and Analytics Data Management Information Asset Management GOVERNANCE ORGANIZATIONAL ALIGNMENT Content Management
  6. 6. Why is EIM Important? Internal pressures:  Desire to understand customer at any time from any channel  Data Quality issues are persistent  Balance of old mainframe systems with new technologies  Movement to the cloud and losing control of data  Data Volumes are increasing  Mobile apps enabling data to be created and accessed anywhere  Project oriented approach to addressing issues/opportunities External pressures:  Greater amounts of new regulations  Increasing Customer Demands – my information anywhere at any time  Technology and market changes outpacing ability to respond EIM ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business pg 6© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  7. 7. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 7© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  8. 8. Symbiotic Relationship An EIM initiative is an important component of a Data Governance Strategy Must Have “Tools”… • Documented and enforced governance policies and processes • Clear accountability, ownership and escalation mechanisms • Continuous measurement and monitoring of data quality & adoption • Executive support to create a culture of accountability around the quality of the data…it’s everyone’s concern • Solid alignment between business & IT Technology alone will not solve the problem You can’t “do” EIM without Data Governance © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 8
  9. 9. Data Governance Definition  Data Governance is the organizing framework for establishing the strategy, objectives and policy for effectively managing corporate data.  It consists of the processes, policies, organization and technologies required to manage and ensure the availability, usability, integrity, consistency, auditability and security of your data. Communication and Metrics Data Strategy Data Policies and Processes Data Standards and Modeling A Data Governance Program consists of the inter-workings of strategy, standards, policies and communication pg 9 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  10. 10. pg 10 Data Governance Framework © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Vision & Mission • Objectives & Goals • Alignment with Corporate Objectives • Alignment with Business Strategy • Guiding Principles • Statistics and Analysis • Tracking of progress • Monitoring of issues • Continuous Improvement • Score-carding • Policies & Rules • Processes • Controls • Data Standards & Definitions • Metadata, Taxonomy, Cataloging, and Classification • Operating Model • Arbiters & Escalation points • Data Governance Organization Members • Roles and Responsibilities • Data Ownership & Accountability • Collaboration & Information Life Cycle Tools • Data Mastering & Sharing • Data Architecture & Security • Data Quality & Stewardship Workflow • Metadata Repository • Communication Plan • Mass Communication • Individual Updates • Mechanisms • Training Strategy • Business Impact & Readiness • IT Operations & Readiness • Training & Awareness • Stakeholder Management & Communication • Defining Ownership & Accountability Change Management
  11. 11. How Data Management / Governance facilitates Analytics  Provides a focus on data as a foundational asset of the company so that it can be used in multiple ways effectively  Defines data standards to ensure data consistency  Maps data from source to target and identifies transformations  Creates rules, standards, policies and processes for data cleansing and validation  Articulates most trusted and timely data sources to facilitate data sharing  Identifies potential data irregularities and creates a process to resolve them “Between 25 percent and 30 percent of a BI initiative typically goes toward initial data cleansing.” Competing on Analytics, Davenport and Harris © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 11
  12. 12. www.firstsanfranciscopartners.com Enabling Analytics Through EDM • Master and Reference Data Management • Meta Data Management • Data Quality • Architecture • Security and Privacy
  13. 13. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 13© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  14. 14. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Privacy/Security DATA GOVERNANCE pg 14© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle Reference Data Management
  15. 15. pg 15 Master Data Management (MDM) © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential What is MDM? MD is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.
  16. 16. pg 16 MDM Key Considerations © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Category Decision Entity Types • What type of data will be managed in the MDM Hub • What are the agreed upon definitions of each type • What is the required cardinality between the entity types • What constitutes a unique instance of an entity Key Data Elements • Purpose, definition and usage of each data element Hierarchies and Relationships • Purpose, definition and usage of each hierarchy / relationship structure Audit Trails and History • How long do we have to keep track of changes Data Contributors • What type of data do they supply • Why is this needed • At what frequency should they supply it • What should be taken for Initial load versus ongoing
  17. 17. pg 17 MDM Key Considerations (continued) © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Category Decision Data Quality Targets • How good does the data have to be • Root cause analysis Data Consumers • Who needs the data and for what purpose • What do they need and at what frequency Survivorship • What should happen when… Lookups • Which attributes are lookup attributes • What are the allowable list of values per attribute • How different are the values across the applications and how do we deal with inconsistencies Types of Users and Security • What types of users have to be catered for • Can they create, update, delete, search • Can they merge, unmerge Delete • How should deletes be managed Privacy and Regulatory • Privacy and regulatory issues
  18. 18. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 18© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  19. 19. pg 19 Reference Data Management © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential What is Reference Data? Reference Data enables an enterprise to make sense of its data and to turn it into real business information. Reference Data are those codes that categorize data and enable an organization to compare that data with internal and external sources. What is the Total sales for all Males who are Silver Customers that live in states on the Eastern Seaboard and are 35-44 years old?
  20. 20. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 20© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  21. 21. pg 21 Meta Data Management © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Enterprise Goal: A common glossary governed at the enterprise level LineofBusiness Application A Business Glossary includes: • Common terms, definitions, business rules, etc. Conceptual Data Model or Enterprise Logical Data Model includes: • Key business concepts/subject areas • Key business relationships Application B Data Model/Dictionary Data Model/Dictionary Model to Model
  22. 22. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 22© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  23. 23. pg 23 Data Quality © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential What is Data Quality? The planning, implementation and control activities that apply quality management techniques to measure, assess, improve and ensure the “fitness of data” for use.
  24. 24. pg 24 Data Quality Dimensions © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Dimension Key Questions Impact Completeness  Is all appropriate information readily available?  Are data values missing or in an unusable state?  Incomplete data can cause major gaps in data analysis which results in increased manual manipulation and reconciliation Conformity  Are there expectations that data values need to reside in specified formats?  If so, do all values conform to those formats?  By not maintaining conformance to specific data formats, there is an increased chance for data misrepresentation, conflicting presentation results, discrepancies when creating aggregated reporting, as well as difficulty in establishing key relationships Consistency  Is there conflicting information about the same underlying data object in multiple data environments?  Are values consistent across all data sources?  Data inconsistencies represent the number one root cause in data reconciliation between different systems and applications. A significant amount of time by business groups is being consumed with manual manipulation and reconciliation efforts Accuracy  Do data objects accurately represent the “real-world” business values they are expected to model?  Incorrect or stale data, such as customer address, product information, or policy information, can impact downstream operational and analytical processes Uniqueness  Are there multiple, unnecessary representations of the same data objects within a given data set?  The inability to maintain a single representation for each entity, such as agent name or contact information (across all component business systems), leads to data redundancy and inconsistency, as well as increased complexity in terms of reconciliation Integrity  Which data elements are missing important relationship linkages that would result in a disconnect between two data sources?  The inability to link related records together can increase both the complexity and accuracy of any corresponding business intelligence derived from those sources. It directly correlates to the level of trust the business has in the data Timeliness  Is data available for use as specified and in the time frame in which it was expected?  The timeliness of data is extremely important. Data delayed in data denied. Could lead to reporting delays, providing slate information to customers and making decisions based stale data
  25. 25. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 25© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  26. 26. pg 26 Reference Architecture: Conventional EDW © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Data Quality Business Rules EngineMeta Data ManagementSecurity/Privacy Acquisition Management Aggregation/ Persistence Access/Delivery Staging (structured data) • Cleanse • Enrich • Transform • Create golden record (MDM) Sources ODS Data Mart(s) Analytics Data Services Other Data Retrieval Systems Archival services EDW• Internal: HR, Finance • External: Market data, Credit scores
  27. 27. pg 27 Reference Architecture: How Big Data Fits In © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Data Quality Business Rules EngineMeta Data ManagementSecurity/Privacy DATA GOVERNANCE Acquisition Management Aggregation/ Persistence Access/Delivery Staging (structured data) • Cleanse • Enrich • Transform • Create golden record (MDM) Sources ODS Data Mart(s) Analytics Data Services Other Data Retrieval Systems Archival services EDW Structured: • Internal: HR, Finance • External: Market data, Credit scores Unstructured: • Sentiment • Clickstream • PDF Semi-structured: • XML, JSON Staging (Semi- structured & unstructured data) Hadoop
  28. 28. Develop and execute architectures, policies and procedures to manage the full data lifecycle Enterprise Data Management Enterprise Data Management Ensure data is available, accurate, complete and secure Data Quality Management Data Architecture Data Retention/Archiving Master Data Management Big Data Management Metadata Management Reference Data Management Privacy/Security DATA GOVERNANCE pg 28© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Develop and execute architectures, policies and procedures to manage the full data lifecycle
  29. 29. Security pg 29© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Confidentiality IntegrityAvailability According to NIST, “The security management practices domain is the foundation for security professionals' work and identifies key security concepts, controls, and definitions. NIST defines computer security as the "protection afforded to an automated information system in order to attain the applicable objectives of preserving the integrity, availability, and confidentiality of information system resources (this includes hardware, software, firmware, information/data, and telecommunications).” Your Analytics infrastructure and data should comply with the normal InfoSec and Privacy practices of your organization!
  30. 30. pg 30 Key Security Domains © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential 1. Security and Risk Management: Security, Risk, Compliance, Law, Regulations, and Business Continuity 2. Asset Security: Protecting Security of Assets 3. Security Engineering: Engineering and Management of Security 4. Communication and Network Security: Designing and Protecting Network Security 5. Identity and Access Management: Controlling Access and Managing Identity 6. Security Assessment and Testing: Designing, Performing, and Analyzing Security Testing 7. Security Operations: Foundational Concepts, Investigations, Incident Management, and Disaster 8. Software Development Security: Understanding, Applying, and Enforcing Software Security Reference: (ISC)2
  31. 31. pg 31 Big Data Analytics Privacy © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential According to U.S. President’s Council of Advisors on Science and Technology, “Big data drives big benefits, from innovative businesses to new ways to treat diseases. The challenges to privacy arise because technologies collect so much data (e.g., from sensors in everything from phones to parking lots) and analyze them so efficiently (e.g., through data mining and other kinds of analytics) that it is possible to learn far more than most people had anticipated or can anticipate given continuing progress.”* Some Key Challenges include:  Difficulty in data anonymization and masking due to sheer volume, number of sources and variety of data  Collecting information without explicit consent  Lack of or insufficient data governance practices – according to Rand Secure Archives Data Governance Survey in 2013, “44% of the respondent have no formal data governance policy”*  Infrastructure – both on-premise and cloud-based Reference: MIT Technology Review Custom + Oracle Courtesy of Samra Sulaiman, ConsultData, LLC
  32. 32. pg 32© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  33. 33. Analytics pg 33© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential What happened? Why did it happen? What will happen? How can we make it happen? Diagnostic Prescriptive Descriptive Predictive Reference: Gartner Value Difficulty Courtesy Samra Sulaiman, ConsultData, LLC
  34. 34. Key Components of an Effective Analytics Strategy People Process Technology Data pg 34© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  35. 35. pg 35 People © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Roles Key Responsibilities Business SME/Manager (decision maker) • Defines the business problem, business objectives Analyst (explorer) • Specific domain area expert • Works with raw data • Creates reports • Leverages data visualization tools IT • Sets up infrastructure • Pre-processes data • Tests and deploys models Data Scientist • Develops models • Performs statistical analysis • Explores data trends, anomalies
  36. 36. Process Descriptive Prescriptive Prescriptive pg 36© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Courtesy of Samra Sulaiman, ConsultData, LLC Typical Analytics Life-cycle: • Define the business problem—e.g., forecasting future sales based on past performance • Design data requirements—e.g., aside from internal data sources, can data be enriched using external data sources such as credit scores, social media data feeds? • Pre-process data—rationalize and cleanse data; apply the appropriate level of data quality dimensions • Perform data analytics—data analytics can be performed using various algorithms or machine learning techniques to gain insight • Visualize the results—various tools can be leveraged to visualize the insight, show anomalies, etc. Define Problem Design Pre- process Analyze Visualize
  37. 37. pg 37 Technology © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Define your use cases before selecting your tools!
  38. 38. www.firstsanfranciscopartners.com Creating “Line of Sight” • Select use cases: How EDM Impact Analytics pg 38
  39. 39. From Data to Analytics Data Insight Action © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 39
  40. 40. pg 40 Select Use Cases © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential How EDM Impacts Analytics: 1. How Data Quality impacts Analytics:  Demand forecasting  Sentiment analysis 2. How Meta Data impacts Analytics:  Glossary  Lineage 3. How Master Data Management impacts Analytics:  Hierarchy management
  41. 41. pg 41 How Data Quality Impacts Analytics © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Demand forecasting: a retail company wants to forecast future product sales based on historical data to better manage inventory Data Quality Considerations:  Accuracy  Completeness  Consistency  Timeliness  Uniqueness  Integrity  Conformity Focus: highest quality data Courtesy of Samra Sulaiman, ConsultData, LLC
  42. 42. pg 42 How Data Quality Impacts Analytics (continued) © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Sentiment analysis: a software product company wants to analyze consumer feedback quickly after product launch Focus: relevant data - eliminate ‘noise’ quickly Courtesy of Samra Sulaiman, ConsultData, LLC Data Quality Considerations • Accuracy • Completeness  Consistency  Timeliness • Uniqueness • Integrity • Conformity  Relevance (new)
  43. 43. pg 43 How Meta Data Impacts Analytics © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • How data flows across the infrastructure/company? • How the data is derived? • How is data transformed? • Data type changes • Calculations • Business/DQ rules What? Why? How? • Ability to track upstream/data producers and downstream/data consumers • Which system transformed the data? • How data was transformed (which rules and calculations were applied)? • Ability to perform root-cause-analysis – tracing data errors from a report back to the source • What data exists today? • Who owns the data? • Which system is the ‘System of Record’ or ‘Trusted Source’? • Are there standard business rules for that data? • Common understanding of available data • Ability to locate needed data more quickly • Ability to know who can answer questions about the data • Ability to trust the data due to the governance process • Audit trail of who touched/changed a term • Data quality rules, metrics, etc.
  44. 44. pg 44 How MDM Impacts Analytics © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Global Company US Subsidiary A Branch A Branch B Branch C Subsidiary B Europe Subsidiary C Branch D Branch E Business Challenges:  Regulatory Compliance - e.g., inability to uniquely identify all counterparties to a transaction  Sales & Marketing - e.g., inability to roll-up sales by subsidiaries or by region  Product – e.g., poor inventory management due to lack of product hierarchy and inconsistent product data
  45. 45. pg 45 How MDM Impacts Analytics (continued) © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential  Executive sponsorship is critical!  Business-driven with close collaboration with IT  Holistic strategy to avoid re-work later; leverage existing (funded) projects, if possible  Strong Data Governance is key to success  Iterative process – rapid and continuous delivery of key capabilities that business needs
  46. 46. pg 46 A Balancing Act! © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Rank Number Topic Asa…. Iwantto… SothatIcan… AcceptanceCriteria 1 16 “Project”Field identifier Data Governance Lead identifyfromwhichprojecttheWorkInProgress businesstermshasoriginatedandfromwhich projecttheWorkInProgressbusinesstermgroups hasoriginated.WorkInProgressispartof UngovernedBusinessTerms. seewhichprojectteamsareaccountable forspecificbusinesstermsandbeableto managethemaccordinglyduringthework inprogressbusinesstermsection A“free-form”textfieldtitled“Project”thatcanbecompletedon thesamepageasthebusinesstermandbusinesstermgroupis beingdevelopedintheInfoMaptool. TargetReleaseDate:05/01/15(IT) Note:-Managingthisviabusinesstermgroupcanbe complicated sincesomeofthebusinesstermsaresharedbymultipleprojects. Thisapproachhasbeenrejected. 2 1 DataConcept Managementin InfoMap CGData Governance Lead -beabletoseeallDGdefinedcore,non-core(i.e.BL governed)&ungovernedbusinessconceptwiththeir associateddatasubjectanddataconcept association(pertheCoreConceptsspreadsheet) mangethecoreandnon-corebusiness concepts Flags-CGcore/noncore,BLownership(IM,DIST,SERV,GBSand otherentitiese.g.PCS,ITG,GBSHR,GBSFIN,etc.), StandardreportconfigurationthatcanbesharedbetweentheCG DGL&B/LDGLs. 3 2 DataConcept Managementin InfoMap CGData Governance Lead -Forallcore&non-coreassigntheaccountable, consultedandinformeddatagovernancebusiness line&namedowner managetheongoingdefinition,assignment ofaccountabilityofDataSubjectsand ConceptswithinInfoMap Reportstatusandprogress ACIassignmentattheBLlevel. StandardreportconfigurationthatcanbesharedbetweentheCG DGL&B/LDGLs. 4 3 DataConcept Managementin InfoMap CGData Governance Lead -seecore&noncore(businessgoverned)data conceptsthathavenobusinessdefinitionby businesslineandowner. assignandmanagethedevelopmentof coreconceptdefinitionstotheir appropriateowners. AddInflight(WIP)GroupwithinGovernedBusinessTerms StandardreportconfigurationthatcanbesharedbetweentheCG DGL&B/LDGLs. Data Sources DQ SolutionsYour Data Management solutions should be proportionate to your Data Analytics needs and focused on business value Courtesy Samra Sulaiman, ConsultData, LLC
  47. 47. www.firstsanfranciscopartners.com Data Governance Operating Models pg 47
  48. 48. Data Governance is critical for Analytics pg 48 You can’t “do” Analytics without Data Governance An Analytics initiative is an important use case for a Data Governance Office Must Have “Tools”… • Documented & enforced data quality policies and processes to ensure data consistency and standards • Understood business logic that maps data from source to target • Clear data accountability, ownership and escalation mechanisms • Continuous measurement & monitoring of data quality, adoption & value • Clearly defined data elements, attributes and computation/derivation of shared data • Really know your data quality before diving into an Analytics “Project” Data Governance is the program that ensures that the Analytics content is trusted © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 48
  49. 49. Operating Model  Outlines how Data Governance will operate  Forms basis for the Data Governance organizational structure – but isn’t an org chart  Ensures proper oversight, escalation and decision making  Ensures the right people are involved in determining standards, usage and integration of data across projects, subject areas and lines of business  Creates the infrastructure for accountability and ownership Wikipedia: An Operating Model describes the necessary level of business process integration and data standardization in the business and among trading partners and guides the underlying Business and Technical Architecture to effectively and efficiently realize its Business Model. The process of Operating Model design is also part of business strategy. © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 49
  50. 50. Process to create an Operating Model • How are decisions made? • Who makes them? • How are Committee’s used? Culture • Centralized • Decentralized • Hybrid Operating Model • Data Governance Owner • SME’s • Leadership People © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 50
  51. 51. Pros:  Formal Data Governance executive position  Data Governance Steering Committee reports directly to executive  Data Czar/Lead – one person at the top; easier decision making  One place to stop and shop  Easier to manage by data type Cons:  Large Organizational Impact  New roles will most likely require Human Resources approval  Formal separation of business and technical architectural roles Bus / LOBs Operating Model - Centralized DG Executive Sponsor DG Steering Committee Center of Excellence (COE) Data Governance Lead Technical Support Data Architecture Group Technical Data Analysis Group Business Support Business Analysis Group Data Management Group © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 51
  52. 52. LOB/BU Data Governance Steering Committee LOB/BU Data Governance Working Group pg 52 Operating Model - Decentralized Data Stewards Application Architects Business Analysts Data Analysts Pros:  Relatively flat organization  Informal Data Governance bodies  Relatively quick to establish and implement Cons:  Consensus discussions tend to take longer than centralized edicts  Many participants compromise governance bodies  May be difficult to sustain over time  Provides least value  Difficult coordination  Business as usual  Issues around co-owners of data and accountability © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  53. 53. pg 53 Operating Model - Networked Pros:  Flat structure  Data management is aligned to lines of business and/or IT ensuring there is a clear understanding of data requirements for that organizational unit  Relatively quick to establish and implement  Known documented connections and RACI charting creates accountability without impacting an organization chart Cons:  Collaborative decisions tend to take longer to implement than centralized edicts  Many participants compromise governance bodies (making it potentially unruly)  RACI’s and the Network itself needs to be maintained  Little enforceable consistency around managing data across the enterprise  Difficult coordination Autonomy at the LOB level can be challenging to coordinate Data Governance Office © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  54. 54. pg 54 Operating Model - Hybrid Pros:  Centralized structure for establishing appropriate direction and tone at the top  Formal Data Governance Lead role serving as a single point of contact and accountability  Data Governance Lead position is a full time, dedicated role – DG gets the attention it deserves  Working groups with broad membership for facilitating collaboration and consensus building  Potentially an easier model to implement initially and sustain over time  Pushes down decision making  Ability to focus on specific data entities  Issues resolution without pulling in the whole team Cons:  Data Governance Lead position is a full time, dedicated role  Working groups dynamics may require prioritization of conflicting business requirements  Too many layers Data Governance Steering Committee Data Governance Office Data Governance Working Group Business Stakeholders IT Enablement Data Governance Organization © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  55. 55. pg 55 Operating Model - Federated Pros:  Centralized Enterprise strategy with decentralized execution and implementation  Enterprise Data Governance Lead role serving as a single point of contact and accountability  “Federated” Data Governance practices per Line of Business (LOB) to empower divisions with differing requirements  Potentially an easier model to implement initially and sustain over time  Pushes down decision making  Ability to focus on specific data entities, divisional challenges or regional priorities  Issues resolution without pulling in the whole team Cons:  Too many layers  Autonomy at the LOB level can be challenging to coordinate  Difficult to find balance between LOB priorities and Enterprise priorities Enterprise Data Governance Steering Committee Enterprise Data Governance Office Data Governance Groups Data Governance Organization Business Stakeholders IT Enablement Divisional DG Office Business Stakeholders IT Enablement Divisional DG Office Business Stakeholders IT Enablement Business Stakeholders IT Enablement Divisional DG Office © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  56. 56. Operating Model Roles and Responsibilities  Data Governance Steering Committee − Provides overall strategic vision − Approves funding, budget and resource allocation for strategic data projects − Establishes annual discretionary spend allocation for data projects − Adjudicates intractable issues that are escalated − Ensures strategic alignment with corporate objectives and other business unit initiatives  Data Governance Office − Chairs the Data Governance Steering Committee and Data Governance Working Group − Acts as the glue between the Data Governance Steering Group and the Working Committee − Defines the standards, metrics and processes for data quality checks, investigations, and resolution − Advises business and technical resources on data standards and ensures technical designs adhere to data architectural best practices to ensure data quality − Adjudicates where necessary, creates training plans, communication plans etc.  Data Governance Working Group − Governing body comprised of data owners across Business and IT functions that own data definitions and provide guidance & enforcement to drive change in use and maintenance of data by the business − Validates data quality rules and prioritize data quality issue resolution across the functional areas − Trains, educates, and creates awareness for members in their respective functional areas − Implements data business processes and are accountable to decisions that are made pg 56 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  57. 57. www.firstsanfranciscopartners.com Aligning Governance and Analytics pg 57
  58. 58. Example: Data & BI Governance Structure Accountable for Governance and Change Leadership for Data & BI across Company • Executive Data & BI Owner • Forum Chair • Membership – Executive Process Owners • Meeting Cadence - Monthly Data & BI Governance Leadership Forum Accountable for Master Data Quality across (Customer, Product, etc) • MDM Practice Lead • Membership – Chief Data Stewards • Meeting Cadence – Bi-Weekly Data Stewardship Forum Accountable for BI Standardization & Adoption • BI Practice Lead • Membership – Functional Reporting Leads • Meeting Cadence – Bi-Weekly Business Intelligence Forum Strategy & Guidance Agreed Decisions Strategic Initiative Alignment Initiative Requests Project / Initiative Progress Intractable Issues © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 58
  59. 59. Example: Data & BI Governance Forum Roles  Forum to strategize, plan and review Master Data initiatives led by team members  Forum to drive Company’s Performance Measurement Architecture development  Forum to discuss & define strategic direction impacting policy, process & technology  Data & BI decision forum for Project X as well as other Corporate initiatives Market to Sell Idea to Offer Finance World Wide Operation Hire to Retire Issue to Resolution to Prevention FP&A IT Data & BI Governance Leadership Forum Forum Chair – Data Governance Sr. Director Process Owner VP SALES SVP Product Strategy CFO SVP ServicesVP HR SVP Finance VP Info Tech Data Ownership Customer Product Chart of Acct Vendor Employee Executive Data & BI Owner – EVP XXX Executive Process Owner’s represent the Functions within their Process Domains • Active participation is critical to our success • When necessary, delegation to peer Functional Owners is acceptable VP WWOPS © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 59
  60. 60. Example: Analytics Operating Model – Immediate IT Advisor Enterprise Infrastructure Committee Executive Office (CEO) Strategy & Risk (CRSO) IT (CIO) Accounting (CAO) Global Services (COO) PMO Head of Business Analytics CEO Credit Analytics Client Analytics Market Analytics LOB … LOB … LOB … Data Stewards Executive Sponsor Analytics CFO © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 60
  61. 61. Example: Analytics Operating Model – Long Term IT Advisor Enterprise Infrastructure Committee Executive Office (CEO) Strategy & Risk (CRSO) IT (CIO) Accounting (CAO) Global Services (COO) PMO Executive Sponsor Analytics CFO Head of Business Analytics Analytics Working Group LOB Reporting LOB … LOB … LOB … LOB … LOB … CEO Credit Analytics Client Analytics Market Analytics LOB … LOB … LOB … Analytic Directors Data Stewards © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 61
  62. 62. Example: Data Governance and Analytics Sponsor - Data Governance Business Steward Leads Data Governance Office DGO Chair IT Lead DG Coordinator Data Management IT Support Group Data Governance Working Group Data Stewards Marketing Fin. Accting Fin. Treasury Risk ECM Ops. HR Fin. FP&A CreditAdmin Fin. Ext. Reporting Legal/ Compliance SVB Analytics Privacy/CS O IT Advisor Enterprise Infrastructure Committee Executive Office (CEO) Strategy & Risk (CRSO) IT (CIO) Finance (CAO/CFO) Global Services (COO) PMO Executive Sponsor Analytics CFO Head of Business Analytics Analytics Working Group Analytic Directors Credit Analytics Client Analytics Market Analytics LOB … LOB … LOB … CEO This is a role / relationship chart and NOT an organization chart Data Stewards © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 62
  63. 63. Example: Data Governance and Analytics Sponsor - Data Governance Business Steward Leads Data Governance Office DGO Chair IT Lead DG Coordinator Data Management IT Support Group Data Governance Working Group Data Stewards Marketing Fin. Accting Fin. Treasury Risk ECM Ops. HR Fin. FP&A CreditAdmin Fin. Ext. Reporting Legal/ Compliance SVB Analytics Privacy/CS O IT Advisor Enterprise Infrastructure Committee Executive Office (CEO) Strategy & Risk (CRSO) IT (CIO) Finance (CAO/CFO) Global Services (COO) PMO Executive Sponsor Analytics CFO Head of Business Analytics Analytics Working Group Analytic Directors Credit Analytics Client Analytics Market Analytics LOB … LOB … LOB … CEO This is a role / relationship chart and NOT an organization chart Data Stewards © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 63
  64. 64. www.firstsanfranciscopartners.com OCM Basics pg 64
  65. 65. EIM Means Change  Successful EIM means a change to the information management culture, processes and policies  Changing that culture means that you are asking people to think and behave differently about how data is accessed and used  You need an organized and systematic way to manage and sustain those changes – or there is marginal likelihood of success © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 65
  66. 66. Two Sides to Change Management WHO? WHAT? WHEN? WHERE? WHY?  Something old stops, and something new starts  Relatively easy to plan for and anticipate SITUATIONAL REORIENTATION PEOPLE GO THROUGH AS THEY COME TO TERMS WITH THEIR NEW SITUATION  It’s important to help affected individuals let go of the old situation and get comfortable with the new way  Everyone processes at a different rate and are rarely aligned with the milestones of the implementation plan PSYCHOLOGICAL For change to be successful, BOTH sides need to be addressed © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 66
  67. 67. Why Do People Resist Change?  Loss of identity and their familiar world − Loss Analysis  Disorienting experience of the transition between the old and the new  Weak/no sponsorship by executive leaders and managers − Lack of alignment − No involvement  Overloaded with current responsibilities  No answer to WIIFM  No involvement in the crafting the solution  Each individual’s capacity to handle change  Other work and personal issues  How well an organization has handled changes in the past 67 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 67
  68. 68. 68 What Might Resistance Look Like?  Trying to outlast the changes: bargaining for exemption from new policies or processes  Reduction in productivity and missed deadlines  Going back to the old way of doing things  Lack of attendance in project status meetings and events, or training  Higher absenteeism  Open expression of negative emotion  From executives, resistance could be: − No visible sponsorship of data governance; no open endorsements − Refusal or reluctance to provide needed resources and/or information − Repeatedly canceling or refusing to attend critical meetings © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 68
  69. 69. 69 Getting People through Change Successfully Requires….  Clear definition of what is changing − Make sure the new behaviors, skills and attitudes are clearly defined and communicated − Provide examples, training and allow time for practice  Attention to feedback: − What are people saying and how are you addressing it? − You must respond to feedback; be sure and attach the actions you take to the feedback you received so that associates know you are listening  Some reward or recognition structure to encourage new behaviors  Measurement and performance management © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 69
  70. 70. Communication Framework to Drive Change © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential  State the aspiration, the BHAG (Big, Hairy, Audacious Goal)  What is the desired future?  What is the value of the future state to the company?  How does that future state move forward the overall business? High-level, strategic statement of a Goal Vision Picture Plan Participation  Paint the picture of how the future will look and feel once Data Management is implemented.  How are people going to get their work done and interact with each other?  How will a day be organized? Future State Principles  Lay out the plan for achieving the future state; the steps and timeline in which people will receive information, training, and the support they need to transition to the future  Orient managers to tell employees how and when their worlds are going to change  Start with where people are and work forward  What does this mean to me? Overall Roadmap Group specific roadmaps  Establish each person’s part in both the future state and the plan to get there  Show associates their roles and relationships to each other in the future  Show associates what part they play in achieving the future and the transition process to get there  All this helps them let go of the past and focus on the future  What is my role? Who does What Across Enterprise Group Specific Adapted from William Bridges, Managing Transitions  Explain why we’re doing what we’re doing - the purpose behind the outcome  What is/was the problem?  Who said so and on what evidence?  What could occur if no one acts to solve it?  What could happen if that occurs? Why you are executing your Vision Purpose pg 70
  71. 71. Change Management Phases  Organizational alignment implemented  Structure  Jobs/people  Policies/procedures  Incentives  Performance management  Change integration/adoption assessment  Communication plan execution  Training development and delivery  Feedback and analysis of results  Leadership alignment checkpoint  Measurement approach & metrics  Organizational impact analysis  Resistance management  Implementation checklist development  Information gathering and analysis  Stakeholder Analysis  Sponsorship development  Change plan development  Leadership alignment checkpoint  Communications planning  Training needs assessment and planning © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 71
  72. 72. Meet with Program/Project Manager, and lay out CM Approach for the Program/Project Monitor & RefineExtend Change Management Alignment to EIM Phases ****Communication Launch Information Gathering and Analysis Stakeholder Analysis/Loss Analysis Change Readiness Assessment Leadership Alignment Sponsorship Development: Assessment and Road Map Detailed Change Planning Communication Plan OperationalizeImplementStrategize & PlanAssess & Align Project Initiation Planning for Change ****Collect, Analyze and Report on Feedback Implementation Checklist More Leadership Alignment Metrics and measurement Org Impact Analysis: structure, jobs, training, policies Managing Change ****Lesson Learned Assessment Organizational Alignment Action Plan Change Integration Checklist Transitioning to the Business Implementing/ Sustaining Change © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential pg 72
  73. 73. The Potential Value of OCM to Your Business  Can result in real monetary value to the business − Acceleration of planned changes − Faster realization of planned benefits − Minimizes business disruption: loss of staff, lower productivity, etc.  Greater likelihood that the IM/DG changes implemented will be sustained pg 73© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  74. 74. The Correlation is There There is data that shows a strong correlation between effectively managing change and meeting objectives − “Show me the numbers” Analysis from: − Prosci’s Best Practices studies − McKinsey studies − Your own organizational experience? pg 74© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  75. 75. 2002 McKinsey Study  Examined 40 projects  Evaluated − ROI expected − ROI delivered − Level of change management effectiveness Results  Direct correlation between change management effectiveness and gap between ROI expected and ROI delivered  Those that were above average on those factors realized 143% of expected value  Those that were below on all three factors realized 35% of expected valueFrom the article “Helping Employees Embrace Change”, McKinsey Quarterly 2002 Number 4, Jennifer A. LaClair and Ravi P. Rao pg 75© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  76. 76. pg 76© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Using reorganization as an opportunity to change mind-sets and behaviors of the workforce Focusing as much on how the new organizational model would work as on what it looks like Accelerating pace of implementation to make the new model deliver value as soon as possible Addressing all risks and bottlenecks as early as possible, before and during implementation Developing a clear communication plan for all internal and external stakeholders Ensuring that IT, financial, human resources, and other systems were updated to support new organizational model Defining detailed metrics for reorganization’s effect on short and long-term performance and assessing progress against them KEY STRATEGIES KEY PROCEDURES 2010 McKinsey Reorganization Study Taking Organizational Redesign from Plan to Practice, McKinsey Global Survey Results, 2010 Courtesy of McKinsey & Company
  77. 77. Contributes to: Achieving Project Objectives… pg 77© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  78. 78. Consider:  There is a direct and profound effect that a strong change management program bears on an organization’s ability to meet or exceed its project objectives  95% of those who rated their change management program as excellent met or exceeded their project objectives as opposed to only 17% of those who rated their change management program as poor or non-existent pg 78© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  79. 79. pg 79 Contributes to: Staying on Schedule… © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  80. 80. pg 80 Consider: © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential  There is also significant correlation between the quality of the change management program and the project’s ability to stay on or ahead of schedule.  75% of those respondents with excellent change management programs had projects that were on or ahead of schedule
  81. 81. Poorly Managed Change Results in:  Lower productivity  Resistance  Turnover of valued employees  Apathy for the future state  Arguing about the need for change  More people taking sick days or not showing up  Changes not fully implemented; benefits not achieved  People finding work-arounds or reverting to the old way of doing things  The change being totally scrapped  Divides are created between ‘us’ and ‘them’ pg 81© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  82. 82. pg 82 Bottom Line… © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential There is significant correlation between the effectiveness of a change management program and achieving Data Governance results
  83. 83. Five Key Factors for DG Success  Executive Sponsorship  Aligned leadership  Clear communication (early and often)  Stakeholder Engagement  Measurement 83© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  84. 84. Final Thoughts Be absolutely clear and specific about what is changing and what that will mean in terms of required behavior changes: people can’t change behavior if they don’t know what they’re supposed do differently. Appreciate that there is a psychology to change: understanding how people react is essential to structuring your initiative to deal with it. 84© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  85. 85. www.firstsanfranciscopartners.com Survey Results pg 85
  86. 86. Descriptive Analytics Program 86© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Descriptive Analytics/BI is still going strong
  87. 87. Predictive Analytics Program 87© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Predictive Analytics is still emerging
  88. 88. Relationship of the Programs 88© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Majority are under the same umbrella • Very little outsourcing of overall Program
  89. 89. Predictive Analytics Investment 89© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Big Market Opportunity
  90. 90. Descriptive Analytics Investment 90© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • It’s not “either/or”, both are receiving investment
  91. 91. Organizational Construct 91© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • All Analytics will be managed together
  92. 92. Measuring Effectiveness of Descriptive Analytics 92© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Improved data is a measurement of effectiveness • Usual suspects of better decision making, better understanding of results, improved processes
  93. 93. Measuring Effectiveness of Predictive Analytics 93© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • Very similar to Descriptive Analytics
  94. 94. Relative Measures of Effectiveness 94© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential • No comparison between the programs, although they appear to be seeking a similar outcome
  95. 95. Take-Aways • Organizations still struggle for skilled resourcesResources • Descriptive BI isn’t going awayInvestment • Optimization needs to occur across Descriptive and Predictive BIOutcome pg 95© 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  96. 96. www.firstsanfranciscopartners.com Wrap-Up pg 96
  97. 97. Approach to practical, affordable analytics Identify efficiency and operational metrics for BI Analytics environments Confirm scope and seed Analytics & Metrics model Define cost of ownership and operating standards Synthesize and map to benchmarks Develop and present efficiency sustaining plan Rationalize metrics and predictive models Align metrics to business Develop transition plan to unified metrics Data Efficiency Corporate Metrics Analytics / BI Architecture Sustaining Plan pg 97 © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential
  98. 98. 98 Kelle O’Neal kelle@firstsanfranciscopartners.com 415-425-9661 @1stsanfrancisco © 2015 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential Thank you! Samra Sulaiman samra.sulaiman1@gmail.com 202-320-9764
  99. 99. www.firstsanfranciscopartners.com Who Took the Survey?
  100. 100. 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% Executive Management Finance Management and / or Reporting Content and / or Digital Asset Management Application Development Data and / or Information Architecture Business Intelligence and / or Analytics Information / Data Governance Corporate Research and / or Library Marketing and / or Market Research IT Management Software or System Vendor Other Job Function Demographics
  101. 101. Demographics
  102. 102. Demographics
  103. 103. Demographics
  104. 104. Business Intelligence v Data Science
  105. 105. Business Intelligence v Data Science
  106. 106. Business Intelligence v Data Science

×