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Data Governance & Data Quality Overview

Data Governance & Data Quality Overview. June 2019. History of NYPA. Founded by Franklin D. Roosevelt in 1931 – Power Authority Act 2000+ Employees Governance 7 Member Board Revenue Resources Power Contracts Generation

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Data Governance & Data Quality Overview

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  1. Data Governance & Data Quality Overview June 2019

  2. History of NYPA Founded by Franklin D. Roosevelt in 1931 – Power Authority Act 2000+ Employees Governance 7 Member Board Revenue Resources Power Contracts Generation Energy Efficiency Projects ($200 – 300M annually) NYS Canals transferred from the NYS Thruway Authority in 2017 524 miles across NYS

  3. NYPA’s vision for Asset Information is: “We have the quality information necessary to support Asset Management decisions within the Asset Management System, making NYPA and our staff empowered to understand its value for informed decision-making” “To get the right information to the right people, at the right time, via the right technology for short-term operations and long term decision making. We need to Manage data as an asset

  4. What is Data Governance? • Data Governance • Encompasses the actions (including people, processes, and technology) used to ensure that key information delivered throughout the organization is appropriately defined, used and maintained • Includes the rules, policies, procedures, roles and responsibilities that guide overall management of an organization’s data • Provides the guidance to ensure that data is accurate and consistent, complete, available, and secure • Enables data to be turned into business insight more efficiently and effectively Data Governance enables an organization to optimize, protect, and leverage all data as an enterprise asset. It is a formal system of accountability designed to enforce proper management of data assets and the performance of data functions.

  5. Business Value of Data Governance Advanced Analytics Advanced Analytics Analysis Enterprises typically spend 60-80% of their time in collecting data and business process reporting. Data Governance will help streamline the data preparation step and focus more on actual analytics.

  6. Data Governance - Where to start? People, processes and technologies are the building blocks for data & Business Intelligence governance and will provide the direction for implementing, managing, and maintaining data will be provided. People • Data Governance operational model and roles • Data Governance governing bodies • Data Integration, Data Profiling, Data Quality tools • Data Quality Scorecards • Data Catalog & Data Dictionary tools • Data Governance support activities (e.g. workflow tools) • Master Data Management (MDM) tools • Data Governance operating processes • Data Governance metrics Follow Leverage Process Tech-nology Enable Data Governance is not just a set of policies or standards. It has to be measurable and demonstrate tangible business value.

  7. What Roles do we need, to manage “Data As An Asset”? What does it take to build a House? How do I best build to the design? How do I best design the house? What do I want in my house? Home Owner Architect Contractor An Analogy Data Owner Data Steward Data Custodian Data Asset

  8. What is Data Quality? Data Quality is not a one-time activity. It is a continuous improvement initiative. Data Quality is an essential part of every Project. Data Quality is quantifiable and measurable.

  9. Measurements of Data Quality What are the characteristics of good data? A Accuracy: Data correctly describe the “real world” object or event C Completeness: All required occurrences of the data are populated C Consistency: A unique piece of data holds the same value & format across all data sets U Uniqueness: All distinct values of a data element appear only once V Validity: All Data conform to their pre-defined values I Integrity: All Data conform to defined data relationship rules (e.g., primary/foreign keys) T Timeliness: Data is delivered on time Remember “ACCUVIT”

  10. Our Data Quality Solution We have a Data Quality Solution to measure, visualize and enhance the quality of data at NYPA. Measure Visualize Enhance Measure the quality of a particular piece of data Analyze data asset and data quality performance Remediate issues and leverage predictive analytics Accuracy Completeness Issue Remediation Business Rules Consistency Uniqueness Validity Integrity Timeliness Predictive Analytics Data Quality Index

  11. How we calculate Asset Data Quality Index (DQI) Every Asset Data in Maximo… Across 6 different Data Quality dimensions… To compute the Asset DQI Goes through 35+ business rules… With different Weights… Business Rule 1 Business Rule 2 Accuracy 20% Business Rule 3 Business Rule 4 20% Completeness Business Rule 5 Business Rule 6 20% Business Rule 7 Consistency Asset Data Quality Index (DQI) Business Rule 8 Business Rule 9 20% Asset Data Uniqueness Business Rule 10 Business Rule 11 DQI is a number between 0 and 1. A higher number indicates better quality with 1 representing perfect Data Quality 10% Business Rule 12 Validity Business Rule 13 Business Rule 14 10% Integrity Business Rule 15 Business Rule 16 Business Rule 17 A-C-C-U-V-I-T

  12. A Data Quality Index (DQI) Score for every single NYPA Asset 67,675 Assets 29 data points per asset 37 business rules

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