The New Jersey Enterprise Data Warehouse. State of New Jersey

Similar documents
2014 NASCIO Recognition Award Submission

JOURNAL OF OBJECT TECHNOLOGY

Revenue and Sales Reporting (RASR) Business Intelligence Platform

MDM and Data Warehousing Complement Each Other

HYPERION MASTER DATA MANAGEMENT SOLUTIONS FOR IT

Data Virtualization for Agile Business Intelligence Systems and Virtual MDM. To View This Presentation as a Video Click Here

IST722 Data Warehousing

What You Don t Know Does Hurt You: Five Critical Risk Factors in Data Warehouse Quality. An Infogix White Paper

LITERATURE SURVEY ON DATA WAREHOUSE AND ITS TECHNIQUES

TDWI strives to provide course books that are content-rich and that serve as useful reference documents after a class has ended.

Human Services Decision Support System Data and Knowledge Management

Strategies to Meet Reporting and Data Sharing Requirements in Corrections

Leveraging MITA to Implement Service Oriented Architecture and Enterprise Data Management. Category: Cross Boundary Collaboration

Data Warehousing. Jens Teubner, TU Dortmund Winter 2015/16. Jens Teubner Data Warehousing Winter 2015/16 1

IT Unification Phase One State CIO Office (or equivalent) Special Recognition

Data Warehouse Overview. Srini Rengarajan

MDM for the Enterprise: Complementing and extending your Active Data Warehousing strategy. Satish Krishnaswamy VP MDM Solutions - Teradata

CDCR EA Data Warehouse / Strategy Overview. February 12, 2010

Master Data Management and Data Warehousing. Zahra Mansoori

Data Virtualization. Paul Moxon Denodo Technologies. Alberta Data Architecture Community January 22 nd, Denodo Technologies

2011: Section 408 Application Kansas Progress Report

Self-Service in the world of Data Integration

EII - ETL - EAI What, Why, and How!

Pennsylvania Department of Public Welfare: Enterprise Incident Management (EIM)

META DATA QUALITY CONTROL ARCHITECTURE IN DATA WAREHOUSING

Three Fundamental Techniques To Maximize the Value of Your Enterprise Data

Rapid Analytics. A visual, live approach to requirements gathering and business analytic development Mark Marinelli, VP of Product Management

Lection 3-4 WAREHOUSING

Knowledgent White Paper Series. Developing an MDM Strategy WHITE PAPER. Key Components for Success

Whitepaper Data Governance Roadmap for IT Executives Valeh Nazemoff

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA

Data Quality Assessment. Approach

See below for data warehouse examples in New York State:

Data Quality and Data Integration: The Keys for Successful Data Warehousing

Data Warehousing Systems: Foundations and Architectures

Data Integration Checklist

Emerging Technologies Shaping the Future of Data Warehouses & Business Intelligence

The Importance of a Single Platform for Data Integration and Quality Management

Data Ownership and Enterprise Data Management: Implementing a Data Management Strategy (Part 3)

The Criminal Justice Dashboard (The Dashboard) Category: Information Communications Technology (ICT) Innovations. State of Maryland.

BUSINESSOBJECTS DATA INTEGRATOR

Integrating Netezza into your existing IT landscape

The Influence of Master Data Management on the Enterprise Data Model

US Department of Education Federal Student Aid Integration Leadership Support Contractor January 25, 2007

Master Data Management. Zahra Mansoori

The Role of the BI Competency Center in Maximizing Organizational Performance

POLAR IT SERVICES. Business Intelligence Project Methodology

The IBM Cognos Platform

CHAPTER SIX DATA. Business Intelligence The McGraw-Hill Companies, All Rights Reserved

Jagir Singh, Greeshma, P Singh University of Northern Virginia. Abstract

Transforming FP&A: Combining Process Redesign & Technology to Deliver Insights

The Case Study of CRM

TDWI research. TDWI Checklist report. Data Federation. By Wayne Eckerson. Sponsored by.

Foundations of Business Intelligence: Databases and Information Management

Hadoop Data Hubs and BI. Supporting the migration from siloed reporting and BI to centralized services with Hadoop

Spreadsheet Governance Pushes MDM to the Desktop

Framework for Data warehouse architectural components

White Paper The Benefits of Business Intelligence Standardization

Technical Management Strategic Capabilities Statement. Business Solutions for the Future

NOS for Data Analysis (802) September 2014 V1.3

FROM DATA STORE TO DATA SERVICES - DEVELOPING SCALABLE DATA ARCHITECTURE AT SURS. Summary

Government Business Intelligence (BI): Solving Your Top 5 Reporting Challenges

Cisco IT Hadoop Journey

SAS BI Course Content; Introduction to DWH / BI Concepts

Paper DM10 SAS & Clinical Data Repository Karthikeyan Chidambaram

The Growing Practice of Operational Data Integration. Philip Russom Senior Manager, TDWI Research April 14, 2010

Department of Rehabilitation Electronic Records System

Building a Data Warehouse

Service Oriented Data Management

BUSINESS INTELLIGENCE. Keywords: business intelligence, architecture, concepts, dashboards, ETL, data mining

2015 Michigan NASCIO Award Nomination IT Investment Fund: Fiscal Responsibility and Accountability

An Introduction to Data Warehousing. An organization manages information in two dominant forms: operational systems of

Establish and maintain Center of Excellence (CoE) around Data Architecture

Chapter 6 Basics of Data Integration. Fundamentals of Business Analytics RN Prasad and Seema Acharya

Business Intelligence at the University of Minnesota

Information Resource Management Strategy and Direction

資 料 倉 儲 (Data Warehousing)

Cloud Ready Data: Speeding Your Journey to the Cloud

Foundations of Business Intelligence: Databases and Information Management

Cloud First Does Not Have to Mean Cloud Exclusively. Digital Government Institute s Cloud Computing & Data Center Conference, September 2014

BUSINESSOBJECTS DATA INTEGRATOR

Analance Data Integration Technical Whitepaper

Your Data, Any Place, Any Time.

Before getting started, we need to make sure we. Business Intelligence Project Management 101: Managing BI Projects Within the PMI Process Group

2005 NASCIO Award Submission Category: Digital Government: Government to Government

SAS Business Intelligence Online Training

Improving your Data Warehouse s IQ

2009 NASCIO Recognition Awards Page 2 of 7

Innovate and Grow: SAP and Teradata

So Many Tools, So Much Data, and So Much Meta Data

Data Warehousing and OLAP Technology for Knowledge Discovery

Strategic Business Intelligence and Analytics in Healthcare

EAI vs. ETL: Drawing Boundaries for Data Integration

What s New with Informatica Data Services & PowerCenter Data Virtualization Edition

How Effectively Are Companies Using Business Analytics? DecisionPath Consulting Research October 2010

Enterprise Data Warehouse (EDW) UC Berkeley Peter Cava Manager Data Warehouse Services October 5, 2006

Corporate Governance and Compliance: Could Data Quality Be Your Downfall?

DataFlux Data Management Studio

Sterling Business Intelligence

Next-Generation Data Virtualization Fast and Direct Data Access, More Reuse, and Better Agility and Data Governance for BI, MDM, and SOA

Transcription:

ENTERPRISE DATA WAREHOUSE 2011 NASCIO Recognition Award Submission New Jersey Office of Information Technology Office of Management Services The New Jersey Warehouse Category:, Information, and Knowledge Management State of New Jersey Page 1 of 7

B. Executive Summary Throughout New Jersey over the last twenty years, agencies have struggled with providing integrated data reporting capabilities to meet management needs. Various approaches have been used, and all shared a few common weaknesses. definitions were not standardized, resulting in inconsistent data quality. Redundant efforts were spent on extracting and publishing data in multiple places and for multiple purposes without leveraging data, effort, or understanding. The business rules for the transformation of the data into useful information were locked in the heads of multiple, at times competing, analysts. The reporting infrastructure was expensive and did not provide all of the desired functionality. The data was often only available in the format required by one report community, and not in the format required by others. In March, 2008, the Office of Management Services published version 4 of the New Jersey Common Information Architecture (NJCIA). This described a framework for developing an enterprise data warehousing (EDW) capability for the state to be used by multiple agencies across multiple subject areas. The framework prescribed a hub-andspokes enterprise data warehousing environment to support all state agencies. The objectives of the NJCIA were to create an enterprise data warehousing environment using robust tools that would create a store of standardized data that could be leveraged by multiple initiatives for operational reporting, analytical reporting, and interfaces to other systems. The environment would be supported by a data warehousing competency center that would be able to meet the diverse data warehousing needs of multiple state agencies. Through December 2010, the original EDW has expanded to include many additional administrative data sources that are now made available to seventeen different agencies. From the original two subject areas, there are now fifteen. In addition to this administrative data, which we call the New Jersey Administrative Warehouse System (NJAWS), the EDW is also home to subject areas such as Criminal Justice Recidivism, Economic Growth, Pensions, Crash Records, EMS Responses, MV Revenue, Risk Management, Transportation Operations, Behavioral Health, and Property Management. It will soon be the home to multiple subject areas for Motor Vehicle Inspections, Children & Families, and Medicaid. But even this does not tell the entire story, as the EDW environment has been a significant part of many additional initiatives, providing data more quickly, more effectively, less expensively, and/or with more reusability. Because the EDW was already in place, Governor Chris Christie was able to roll out his comprehensive, data-driven government transparency site, YOURMONEY.NJ.GOV in only ten weeks. Page 2 of 7

C. Description of the Business Problem and Solution At its heart, the problems that New Jersey faced were data quality and data governance. Every time a manager, a policy maker, or a politician needed information, it was addressed by a one-off scramble to locate data, bring it together, and put it into a useful format. When looked at in isolation, the impact of any one of these efforts was not noticeable. Collectively, however, they resulted in hundreds of data files being moved throughout the organization in an unmanaged way, often with the same data be created and moved multiple times. As a result, the state incurred substantial costs to support the data file creating. Worse, because there were no standards in place to govern the definition, transformation, or use of the data, the resulting information generated had inconsistent data quality. The legacy reporting tools available did not provide functionality that report consumers were demanding, and the tools required substantial technology support and custom programming each time a new report was required. A number of obstacles stood in the way of addressing these data quality and support problems in a comprehensive way. After many years of keeping data internal to their organizations, a culture of data sharing was emerging, in which agencies reluctantly agreed to share data files between each other. While a step towards better information, this actually exacerbated the data quality problems as data was shipped haphazardly through organizations. Agencies resisted the idea of integrated data for reusability. A second obstacle was that the reporting technologies at the time were often in the hands of analysts that were reluctant to give up their very prominent role in delivering information and solutions to management. A third obstacle was that the technology units supporting transactional systems were not comfortable with the idea of their data being available to others. These obstacles were compounded by the lack of authority for a centralized approach. In 2007, the governor signed legislation designating the New Jersey Office of Technology as responsible for information technology in the executive branch. Shortly thereafter, the NJ Office of Legislative Services released an audit report that indicated that New Jersey needed to develop a strategic plan (architecture) for enterprise data warehousing. As a result of these two events, the NJ Office of Management Services (ODMS) in the NJ Office of Information Technology (OIT) published version 4 of the New Jersey Common Information Architecture (NJCIA) in March, 2008. The NJCIA describes a framework for developing an enterprise data warehousing capability for the state to be used by multiple agencies across multiple subject areas. The framework prescribed a hub-and-spokes enterprise data warehousing environment to support all state agencies. Version 4 enhanced previous versions of the framework and also incorporated the governance directed by the new law as well as suggestions included in the audit report. Page 3 of 7

On Integration Line & Batch Transactional Systems Integration Analytic Dashboards Publication Desktop Business Intelligence Tools Metadata Reports Publication The NJCIA is based upon industry best practices as defined by NASCIO, by the Management Association (DAMA), and by the Warehousing Institute (TDWI). On-Line Transactional Systems Web- Enabled NJ Common Information Architecture Conceptual Model Web Services Integration Systems Operational Dashboards Mining Web Services Business Intelligence Delivery Framework On-Line Analytical Systems OLAP Analytics On-Line & Batch Transactional Systems Apps Tier 1 Affinity Apps Tier 2 Agency Apps Tier 3 Federated OLTP base Management Version 4.01 Profiling Tools ESB ETL Replication EAI EII Opera Mart Agency ODS Tiers 2 & 3 UDS Tiers 0 & 1 Exploration Warehouse Warehouse Warehouse Metadata Management Registry and Repositories Administration Quality Tools Metadata Tools ETL Monitoring ESB ETL Replication EAI EII OLAP Cube Subject Area Mart Logical Marts Physical Marts Metadata Mart Conforming OLAP base Tuning base Auditing Revised March 2008 The objectives of the NJCIA are to create an enterprise data warehousing environment using robust tools that would be leveraged by multiple efforts for operational reporting, analytical reporting, and interfaces to other systems. The environment would be supported by a data warehousing competency center that would be able to meet the diverse data warehousing needs of multiple state agencies. The ODMS was given the task of creating the enterprise data warehousing environment, standing up a data warehousing competency center to support all executive branch agencies, to migrate the existing reporting solutions into delivery systems based on the enterprise data warehouse, to develop policies and standards, and to evangelize this approach to agency business and technical staffs. The first three projects brought into this environment were the New Jersey Administrative Warehouse System (NJAWS), the Crash Warehouse (CRASH- DW), and the Department of Transportation data warehouse (TransINFO). Page 4 of 7

The NJAWS re-engineering initiative began in early 2008 and was completed in October 2009. It integrated multiple independent data marts for financial, payroll, human resources, and other back-office systems into an enterprise environment that increased access to data, reduced storage requirements, improved performance, and streamlined administration. Initially an operational reporting area, this environment will eventually be a normalized, multi-subject area data resource that will insulate the state s data as legacy systems are retired and replaced with new solutions. The CRASH-DW brings together data from State Police, Department of Transportation, Motor Vehicle Commission, and Department of Health and Senior Services for longitudinal crash analysis. This had never been done before. Furthermore, the initial release of CRASH-DW to the user community in September 2009 allowed, for the first time, full reporting capabilities across all the relevant crash data supplied by the four above-mentioned NJ agencies. Subsequent functionality was added in September 2010. Additionally, because of the enterprise approach, non-crash emergency medical service data is now available to the Department of Health and Senior Services for the analysis, and DHSS is able for the first time to meet its federal requirements to provide regular data to the National Emergency Management Information System, with no additional costs. The NJ Department of Transportation had attempted to integrate its operations systems twice before over a period of five years, without moving beyond the planning stage either time. Obstacles included the lack of data warehousing technologies, the lack of a data warehousing methodology, and the inability to reach consensus on data definitions. The TransINFO project began in July 2008 and was implemented in June 2010. It overcame obstacles by taking advantage of the NJCIA data warehousing methodology and technologies, as well as using ODMS staff to create a sustainable data model and resolve the data definition conflicts. It leveraged roadway data that was initially part of the CRASH-DW, which resulted in significant savings. Through December 2010, the original EDW has expanded to include many additional administrative data sources that are now made available to seventeen different agencies. From the original two subject areas, there are now fifteen. In addition to this administrative data, which we call the New Jersey Administrative Warehouse System (NJAWS), the EDW is also home to subject areas such as Criminal Justice Recidivism, Economic Growth, Pensions, Crash Records, EMS Responses, MV Revenue, Risk Management, Transportation Operations, Behavioral Health, and Property Management. It will soon be the home to multiple subject areas for Motor Vehicle Inspections, Children & Families, and Medicaid. It played a critical role in the success of Governor Christie s comprehensive government transparency site, YOURMONEY.NJ.GOV. D. Significance to the Improvement of the Operation of Government The enterprise data warehouse represents a fundamental transformation in the way that New Jersey state government views its data resource. is no longer viewed as the Page 5 of 7

property of a business unit, or worse, a business analyst. Instead, it is treated as a reusable resource. New Jersey recognizes the value of the six overarching drivers of the NJCDA that data must be more accurate, more complete, more accessible, more reusable, as well as timelier and less expensive to administer. E. Benefits of the Project This project addresses the NASCIO State Priorities for 2011, including in Category A the Nos. 1, 3, 5, 6, and 10. While not originally the objectives of the NJ enterprise data warehousing program, the following initiatives have leveraged the environment to improve data quality, reduce development costs, and/or reduce development time. Governor Christie s Transparency Site New Jersey s first comprehensive, government transparency center. YourMoney.NJ.Gov is designed to provide a clear picture of the state s revenues and spending for all citizens. The previous Open Public Records Act process was burdensome and expensive compared to Web-based search engines and data storage. This new site consolidates values of public information on one site that is easy to navigate. Its first release went live with two subject areas after only ten weeks by leveraging the EDW. Subsequently, four additional subject areas were added, and the scope was extended to include data from independent state authorities. Payroll Integration - A bi-directional data transfer process was quickly developed between the new statewide electronic Cost Accounting and Timekeeping System (ecats) and the payroll mainframe payroll system to meet requirements, keep the project on schedule, and reduce development costs. Employee Profile - For the first time, the State has an integrated view of NJ employee information using data from six different legacy sources. This profile data is provided to numerous systems through the Warehouse. eobcis Global Justice XML Effort In partnership with the Department of Corrections, OIT has built a Global Justice XML-based data integration process that eliminates duplicate data entry of criminal justice information into two disparate systems, and leverages this integration for reporting and analysis. DOC has re-purposed several data entry staff while improving accuracy and timeliness. This is being expanded to include other criminal justice components. This effort is now being leveraged for an analytical reporting environment (data mart) to study recidivism rates. Cancer Registry Quality Process Using the EDW data cleansing tools, data warehousing staff cleanses, standardizes, and geo-codes cancer registry information for the Department of Health and Senior Services to improve its data Page 6 of 7

accuracy. This service is now being provided to other DHSS units as well as other agencies, such as to cleanse heating assistance data for the Departments of Human Services and Community Affairs. Universal Store What does County Code 02 represent? You would think this is a simple question with a simple answer, but it is not. The State uses a half-dozen different approaches to identifying counties in its systems. In some, 02 is Bergen County (the second county alphabetically). In others, 02 represents Atlantic County (the first county). In others, various alphanumeric codes are used (e.g. A, AT, ATL, etc.). This is just one example of the significant problem of integrating data from multiple sources. It is the reference data that enables integration, but it is the reference data that has the most problems with standardization and quality. The UDS is a section of the enterprise data warehouse that maintains this common reference data. Feeds of dozens of data sources to dozens of systems On almost a weekly basis, the data warehouse teams at ODMS are contacted to obtain a data feed of data in the EDW. In the past, these requests would have gone back to the individual mainframe system teams so that a custom interface could be built. There are notable benefits to not building custom interfaces. Individual requests for data add workload to stretched mainframe support teams and mainframe environments. They strain available processing windows. They add fragility to the overall system. They increase the replacement cost estimates when systems are considered for replacement. The development costs for mainframe extracts are significant. In the EDW, the data has been populated by a single interface. The costs for deploying new data feeds out of this environment are substantially less often by an order of magnitude and the development time is quicker. In some cases, the data can be provided in a self-service format. If the mainframe system is ultimately replaced, the new system need only provide a single data feed into the EDW, and all of the data consumers continue to receive their data. All of these efforts benefited from the use of components of New Jersey s Warehousing (EDW) environment, as guided by our New Jersey Common data Architecture (NJCDA). All of these efforts were completed leveraging existing resources. These efforts were completed more cost effectively, especially within the context of continuing support and maintenance costs. Many of these efforts were completed more quickly due to the existence of the EDW environment. In fact, some of these efforts could not have been completed at all without it. Page 7 of 7