DATA GOVERNANCE AND INSTITUTIONAL BUSINESS INTELLIGENCE WORKSHOP

Transcription

1 NERCOM, Wesleyan University DATA GOVERNANCE AND INSTITUTIONAL BUSINESS INTELLIGENCE WORKSHOP ORA FISH, EXECUTIVE DIRECTOR PROGRAM SERVICES OFFICE NEW YORK UNIVERSITY

2 Data Governance Personal Journey Two Universities: o Rensselaer Polytechnic Institute o New York University Two Very Different Cultures Similar Approach Please remember to fill out evaluations: bit.ly/nercomp_governance 2

3 Defining Data Governance Work in Progress Refers to the overall management of how do we ensure that data employed is: o Available, Usable, Has integrity (ie can be trusted), and Secured Data governance program includes: o Policy o Governing body or council o Defined set of procedures o The Execution of those procedures o Technology 3

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5 From 2006 Spellings Report Higher Ed is under Pressure Increase Effectiveness Reduce Cost Endangered Endowments Cuts in Federal & State Support Cost of regulatory compliance Demand for Greater Affordability Demand for Accountability Increase Financial /Operational Efficiency Expand Local & Global Impact Establish New Funding Models Changing Technology Changing Markets College Degree is Necessity Changing Student body Globalization Increased Competition Social Cloud Interactive Access to Information from Everywhere, Any Time, on Spectrum of Devices Fast 5

6 Lord Kelvin "If you can not measure it, you can not improve it." 6

7 Organization Maturity Model Defining measurable outcomes (KPI) Develop Targets Develop Models Forecasts Budgets Mission & Goals Planning Act / Adjust Monitor / Analyze Actions, Decision Adjust plans Dashboards Alerts, Scorecards Interactive Reports 7

8 Integrated Information: The Wishing Well Common dictionary of terms Flexibility for future growth Intuitive and easy way to Interact with Data Have one system, one tool, one consolidated view of information High level of confidence in data accuracy Single integrated repository for all reporting Self-service with no dependency on IT 8

9 WHY IS DATA GOVERNANCE SO HARD? Lack of a Quality Foundation Organizations don t know where to start with data governance efforts and lack the tools for ongoing tracking against quality goals Lack of Business Buy-In Data Governance programs struggle for acceptance with the business or fail outright due to lack of attention to data quality issues Lack of Business and IT Alignment IT and business look to each other to resolve data issues, with neither willing to step up and take ownership 9

10 Data Warehouse (DW)/Business Intelligence (BI) & Data Governance 10

11 Data Governance and University Data Warehouse (UDW) Data Warehouse 11

12 Data Warehouse Implementations Highlight Data Issues 12

13 Framing Data Governance discussions Data quality is vital to the adoption of DW/BI If data warehouse content is inaccurate, incomplete, or otherwise unavailable, business users will seek other sources to meet their informational needs Data quality is not merely something that an organization can address as a one-time project. It requires an on-going monitoring & measuring data quality and demonstrated continues improvements Data Governance 13

14 Business Intelligence & Data Warehouse (DW) Program Program that evolves over period of time that has to be monitored and assessed Our Focus Today Implementation Methodology Data Governance Change Management Training DW Architecture Communication Governance Business Intelligence Continue To Evolve & Grow Support 14

15 Data Governance Function Cuts Across Major Components of UDW Data Governance Policies UDW Architecture Processes Accountability (People) Meta Data Error detection and notifications Transformations Change Management Establishing culture that: defines data quality metrics and assigns responsibility Communication, Training & Support 15

16 Data Governance Tasks throughout DW Implementation Initiation Gain Sponsorship Define Governance Structure Define Roles & Responsibilities Define Processes Define SLA Define Security and Access Create Policy Plan & Procure Technologies Implementation Assign Roles & Responsibilities Define Business Rules, Definitions, Transformations Utilize Technology to enforce rules, validate data Utilize technology to create meta data Put in place processes to identify & fix erroneous data Build security Certify On-Going Monitor and Assess Data Quality Identify & Fix Erroneous Data through ongoing automated data validations and business processes Incorporate data training into overall BI training program 16

17 Policies Defines Ownership Defines roles & responsibilities Defines the SLA for business processes to identify and fix erroneous and the escalation process Enforces the need for common definitions and metadata Enforces mandatory training in technology and DATA Outlines the guiding principle around data sharing, security and access Enforces one version of the truth Mandates fixing data at the source systems 17

18 People & Accountability: It Takes a Village Data Trustee Information Consumer Data Custodian Data Governance and People Data Steward Chief Data Management Officer Decisions Support Group 18

19 Accountability: Data Trustee Data Trustee: The individuals, in an operational area, who have wide responsibility for maintaining transactional systems. Admissions, Financial Aid, Registrar, Bursar, Pre-Award, Post Award, HR, Finance, and more Data Trustees will: Establish Data Quality Metrics Oversee the establishment of data management policies across University Oversee the establishment of data management procedures across University. Assign Data Stewards that serve the data management function across University. Serve as an escalation point. 19

20 Accountability: Data Stewards Data Steward: Managers in operational areas responsible for maintaining transactional systems to serve the needs of the University community Admissions, Financial Aid, Registrar, Bursar, Pre-Award, Post Award, HR, Finance, and more The Data Stewards will be responsible for maintaining data accuracy and reliability. They will: Certify data reposited in the warehouse. Certify standard reports & dashboards. Oversee resolution of data errors. Oversee implementation of user-access policies. Participate in establishing common definitions. Collect & record or approve metadata. Establish and manage policy for record retention and archiving. 20

21 Accountability: Chief Data Management Officer (IR) Chief Data Management Officer (IR): responsible for coordinating all activities related to Common Definitions: Work with Data Stewards to Develop Common Definitions. Define Official University Metrics Calculations. Work with the Committee on Institutional Data Policy on approving data policies and procedures. 21

22 Accountability: Data Custodians (IT) Data Custodians: Information Technology specialists assigned to each transactional system that maintains data and to the University Data Warehouse University Data Warehouse, DRM, Finance, Student, HR, Alumni Relations, Hyperion Planning, etc In the UDW environment, the Data Custodians will: Oversee the safe transport and storage of data. Establish and maintain the underlying infrastructure. Perform activities required to keep the data intact and available to users. Collaborate with UDW Data Custodian to implement data transformations, resolve data issues, and collaborate on system changes. 22

23 Data Governance and Support The Decision Support Group (DSG) serves as a Front Line for all reporting and analytical needs Front line for data related issues faced by the user community Offering on-going Training in Tools & Data Work in partnership with Data Stewards to maintain meta data, address data issues 23

24 The Support Framework is based on 3 Tiers The Decision Support Group serves as a Front Line for all reporting and analytical needs Data Trustees Tier 3 DSG can engage Tier 3 when data issues can not be resolved in 4 business days Data Stewards and Data Custodians Tier 2 Decision Support Group (DSG) Tier 1 Goal: To resolve 80% of all inquiries Tier 2 assists the DSG in producing solutions for user community All UDW issues are reported to the DSG If DSG cannot resolve the issue they will involve the appropriate tiered support for resolution 24

25 Data Quality Security & Access Processes Meta Data Management Common Definitions & Data Dictionary 25

26 Processes: Data Quality Business and IT Coming Together Define Data Quality Metrics Error Handling rules Establish Processes to Identify and Fix errors Business: Define IT: Build Error capturing processes Reconciliations processes Notifications & Alerts Fix errors in source systems Certify: data and logic used in dashboards and reports Business: Fix & Manage 26

27 Processes: Common Definitions, Data Dictionary, and Meta-Data Business and IT Coming Together Define Business Rules Define Common Definitions Define Transformations Describe data Business: Define IT: Build Build Transformations in ETL Build Meta Data Repository Make it Accessible in Ad-Hoc, Dashboards & Reports Searchable Monitor access to Meta Data Monitor value: support issues & training Business: Keep it Current & Useful 27

28 Processes: Data Security and Access Business and IT Coming Together Define security and access specifications based on the Policy Establish security approval process Establish process to handle exceptions Business: Define IT: Build Build system to request /remove/update/ approve and store requests Build data security at the data base level and front-end tool Establish monitoring process Business: Fix & Manage 28

29 More on Technology in Data Governance Master Data Management: Ex: defining a Person Data Relationship Management: Ex: Maintaining crosswalk for Organization structures Build DW Architecture for reporting and analytics o Data Extraction and Loading: Transformations o Data reconciliation processes to ensure data reconciles back to the source systems o Data error handling: Rejects, Alerts, Notifications o Dashboards and reports to monitor data quality o Systemized defaulting mechanisms to manage inconsistencies o Implement Data Security policies o Meta Data solutions for storage, maintenance, and interactivity 29

30 Data Governance and Training Training Method Computer Based elearning Facilitated Learning Labs Classroom Instructor Led Training Subject Matter Workshops Virtual Instructor Led Training Community Collaboration User Types Standard Report and Dashboard User Ad Hoc Reporting User Standard Report and Dashboard User Ad Hoc Reporting User Advanced/ Power User Ad Hoc Reporting User All Reporting User All Reporting User Incorporate Training in Business Definitions & Policies Mandatory: No Access is given without training Quiz: Tests Proficiency in basic knowledge of business terms and definitions 30

31 Data Governance and Business Meta Data Dashboards and Interactive Reports Every Page has a Hyperlink that provides contextual information Describes the sources, transformations, and business rules Time Stamps Contact Information Ad-Hoc Every Data Element includes Business Definition Data Dictionary Searchable Includes contextual information on all available data elements, dashboards, and interactive reports Ownership Defined Continuously improved Tied to Training and Support 31

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33 Have I mentioned the Evaluations? bit.ly/nercomp_governance 33