KEDA

Kentucky Enterprise Data Architecture

CTC Presentation

April 2007

Glenn J. Thomas, PMP, CPM

Director, Data Architecture

‘Stovepipe’ Systems

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IT’s Tower of Babel

Can You Help Me?

Can You Help Me?

Clarke blames data-sharing woes for 9/11: former Bush adviser cites FBI's failure to build IT Infrastructure.

(News)(Richard A. Clarke)

COPYRIGHT 2004 Computerworld, Inc.

Thousands of Americans died at the hands of terrorists on Sept. 11, 2001, primarily because of a fundamental inability among government agencies to share information, two top former counterterrorism officials said last week.

Manualytics Decision Process

How can we…?

How much will it cost to …?

How will it affect …?

1 to N

Manualytics Decision Process

1 to N

1 to N

1 to N

Manualytics Decision Process

Manualytics (1 to N days)

Manualytics (1 to N days)

Manualytics (1 to N days)

Manualytics (1 to N days)

Manualytics Decision Process

What does it mean?

How sure are we?

How accurate is it?Where did this come from?

The Problem with Manualytics

•Inefficient Processes•Unnecessary Manual Work•Inconsistent Results•Compounded Errors•Data Untraceable from Target to Source•Uncertainty and Confusion•Risky Decisions

“The Data Warehousing Institute (TDWI) estimates that poor quality customer data costs U.S. businesses a staggering $611 Billion a year in postage, printing and staff overhead”

-Wayne EckersonDirector of Research

TDWI

The Cost of Manualytics

• Up to 75% of information workers have made decisions that turned out to be wrong due to flawed data.

• As much as 30% of their week is spent verifying the accuracy and quality of their data.

• Only 10% feel they always have all the information they need to confidently make business decisions.

• 89% said that they have had the information they use in their job questioned or challenged by their peers or bosses.

-Survey conducted by Harris Interactive, June 2006

The Stats on Manualytics

The Kentucky Enterprise Data Architecture will apply best practices for people, process, and policy to improve data quality, integration, security and predictive decision making at all levels of state government.

Level 1:

Initial

Level 2:

Repeatable

Level 3:

Defined

Level 4:

Managed

Level 5:

Optimized

•Entrepreneurial•Individual•Fragmented•Chaotic•Idiosyncratic•Few Users•Rules Unknown•Variable Quality•Costly

•Departmental•Consolidation•Reconciliation•Internally Defined•Reactive•Local Standards•Internal DQ•Specialist Users•Local Process•Costly

•Integration•Enterprise View•Data Accountability•Strategic Alignment•Standards•Sharing & Reuse•Centralized DQ•Planned & Tracked•Wide Data Usage•Metadata Mgt•Common Technology•Efficient

•Quantitative Control•Closed Loop•Low Latency•Interactive•Unstructured Data•Collaborative•Process Efficiency & Effectiveness•Built-in Quality•Extended Value Chain•High Availability

•Improvement & Innovation•Real-time•Extensive Data Mining•Knowledgeable•Competitive Intelligence•Data Assets Valued•Self-managing

Knowledge Logistics TM (labels based on CMM)

Information Maturity Scale

Data-Driven Decision Process

Information Information IntegrationIntegration

Agency B

Agency A

Agency D

Agency C

How do we…?

How much will it cost to …?

How will it affect …?

Data Defined, Integrated and Accurate

Enterprise Data

Warehouse

BusinessBusinessIntelligenceIntelligence LayerLayer

BusinessBusinessIntelligenceIntelligence LayerLayer

Agency A Agency B

Agency C Agency D

Kentucky Enterprise Data Architecture

Benefits:

•Increased Data Interoperability

•Increased Data Reliability & Relevance

•Increased Reuse (Collect Once Use Numerous Times)

•Decreased Data Redundancy

•Decreased Infrastructure Costs & Complexity of Solutions

•Increased Efficiency & Effectiveness of Service Delivery

•Facilitate New Enterprise or Multi-agency Solutions

•Expanded Public Access

Kentucky Enterprise Data Architecture

Data Quality Management

Meta data Management

Data Warehousing & Business Intelligence

Management

Database Management

Data Security Management

Reference & Master Data Management

Document, Record & Content

Management

Data Architecture, Analysis &

Design

Data Governance

©2007 DAMA International

•Inter-Agency Taskforce

•Finalize/Endorse/Communicate KEDA

•Propose Data Policy to the EASC

Data Governance

•Enterprise Architecture Standards Committee •Governance of KEDA and Other Enterprise Policies/Standards

•Agency Data Stewards – Exist/Establish Within Data Subject Areas•Definition, Accuracy, Consistency, Security, Privacy•Not a New Staff Requirement - Formalize Existing Function

•Architectural Management of Other Eight Components

•Enterprise Common Data Framework and Models

•Party/Contact Model

•Database Design Standards

•Data Modeling & Model Management

Data Architecture, Analysis &

Design

•Technology Management (Approved Enterprise Standards)

•Physical Database Design and Naming Standards

•Policy for Database Implementation, Change Control, Backup/Recovery

•Production Monitoring of System Usage, H/W Utilization, Data Dormancy

•Limitation of ‘Shadow IT’ with Local, Redundant, Questionable Data

Database Management

•Not All Data will be Needed or Wanted Across the Enterprise

•Working with CISO and Identity Management Task Force to Review/Update Enterprise Data Security Standards

•MOUs & MOAs for Inter-agency Shared Data

•Data Security to the Field Level when Shared

•Compiled Data where Individual Records Limited by Privacy and/or Legal Restrictions

Data Security Management

Data Quality Management

•Standards

•Enterprise Common Data Framework

•Best Practices

•Proactive Development and Testing Techniques

•Tools

•Data Profiling Can Pinpoint Data Issues & Training Needs

•Information Integration Can Eliminate the Need to Move Data

•On-Line Analytical Processing vs On-Line Transactional Processing

•Enterprise Views of Data for Reporting Across/Within Agencies

•Information Integration

•Enterprise Data Warehouse

•Ad hoc Reporting/Data Mining Capabilities for Appropriate Staff

•Creation of Business Intelligence Competency Centers

•Dependent on Governance, Data Quality and Meta data

Data Warehousing &

Business Intelligence

Management

•Standard Tables of Common Reference Data

•Enterprise Common Data Framework

•Party Contact Model

•Security Model

•Other Models as Appropriate

•Master Data Repository

•‘Best’ Source of Data in the Enterprise

Reference & Master Data Management

•85% of Business Information in Unstructured/ Semi-Structured Format (Merrill Lynch)

•Document Management (i.e., DocuShare)

•Image Management

•Current Research - Convert to XML

Document, Record & Content

Management

•Enterprise Common Data Framework

•Standardized Definitions & Key Attributes

•Enterprise System Inventory

•80/20 Rule

•Include Business and Technical Information

•Source System, Downstream Processing, Conversions, etc.

Meta data Management

Assembling the Puzzle

Enterprise Architecture

Enterprise Meta data Repository

Systems Inventory

Information IntegratorInformation Integrator

Multi-System/Multi Agency Reports

Poor Quality Data = Poor Quality Reports

Status•Interagency Task Force Meeting held April 23, 2007

•Demonstration of Prototype Enterprise Meta data Repository

•Enterprise System Inventory Data Submission Format Distributed

•Enterprise Standards

•Approval of Three Updated Standards to submit to EASC

•Enterprise Common Data Framework

•Party/Contact Model presented for discussion at EASC 3/15 Meeting

•Enterprise Data Integration Capital Project submitted

•Commercial Toolset

•Information Analyzer – Installed, Begun Profiling CTS Phase I data

•Information Integrators – Project Initiated to Define Security, Process

•Enterprise Meta data Repository

•Build Version 2.0 w/ KEDA Task Force Recommendations

•Load System Data as Provided and Map to ECDF

•Continue to Review Existing Data Policies

•Enterprise Common Data Framework

•Party/Contact Model to EASC for approval by FY ’08

•Begin Work on Security Model

•‘Data Integration’ Bundled Service to Agencies for FY’08

•Populate KEDA webpage within COT/Policies & Standards

Next Steps…

Questions???

http://gotsource.ky.gov/dsweb/View/Collection-42559

Glenn.Thomas@ky.gov

For more Info on the KEDA Initiative