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Onsite Seminar

Information Quality Fundamentals

Many organizations have recognized the value of information quality improvement, and are instituting a information quality management program, either as a function within a line of business, or even at the enterprise level.

This training seminar presents many aspects of an Information Quality program with a selection of best practices guidelines that can be effectively communicated to (potentially non-cooperative) data/system managers. We will discuss a strategic plan for integrating information quality into the system, as well as describing tactical approaches necessary for advising/influencing application program managers to adopting information quality methods and techniques.

This is a two-day seminar, broken into 8 sessions, which are described in greater detail in the sections below:

  1. Information Quality Management Coordination
  2. Introduction to the Components of the Information Quality Program
  3. Data Quality Tools – Overview
  4. Information Quality Assessment
  5. Rule-Based Information Quality
  6. Data Quality Tools – Requirements, Acquisition, Integration
  7. Data Stewardship, Data Standards, and Metadata
  8. Building the Information Quality Program

Each session can be customized into a one-day seminar

Information Quality Management Coordination

Having embarked on a plan for integrating information quality into your processes, whether they are related to ongoing transactional systems or analytical platforms such as data warehouses, the first step is to understand how information quality fits into the overall organizational structure. There are issues that may impede the integration of information quality into the managerial, operational, and technical aspects of the enterprise, including data ownership issues, vertical system hierarchies, questionable administrative authority, and limited business case analysis for data quality improvement. Often information quality personnel are engaged in an advisory role, making it difficult to properly build the IQ program. In this session we introduce information quality concepts, and then briefly look at some of the roadblocks that need to be understood in order to be successful.

  • Introduction to Information Quality
    • Definitions
    • Dimensions of Information Quality
    • Improving Information Processes
  • Challenges in Information Quality Management
    • Information Quality in the Advisory Role
    • Long-term Strategy vs. Short-term Tactics

Introduction to the Components of the Information Quality Program

In this session we review some of the critical components of an information quality program, starting from building the business case justification, looking at various modern aspects of information quality improvement, and provide a high-level overview of the concepts covered during the rest of the course.

  • Information Quality ROI Analysis
    • Identifying impacts of poor information quality
    • Building a business case for information quality improvement
  • Rule-based Validation
    • Formalizing information quality expectations
    • Assessment
    • Establishing Objective Criteria
  • Data Quality Tools
    • Data Profiling
    • Parsing and Standardization
    • Record Matching, Duplicate Analysis, Merge/Purge
    • Data Enhancement
    • Rules Engines
  • Metrics and Continuous Monitoring
    • Establishing Proactive Monitoring
    • The Information Quality Scorecard
  • Data Standards and Enterprise Metadata
    • Standards for Information Exchange
    • Managing Data Standards
    • Using Metadata for Information Quality
  • Data Stewardship
    • Issues in Data Ownership
    • Data Stewardship Policies and Procedures
  • Supplier Management
    • Information Quality and Administrative Authority
    • Influencing Upstream Partners to Improve Quality

Data Quality Tools - Overview

While data correction is no longer viewed as an acceptable means for improving data quality, the tools that have been used in the past, as well as newer technology, can support a proactive, business rule-based approach to continuous information quality improvement. In this session we review different kinds of tools that are available, how those tools can be used, and we will look at different options for each type of tool.

  • Data Profiling
    • Column Analysis
    • Cross-Column Dependency Analysis
    • Cross-Table Redundancy Analysis
    • Data Profiling Options
  • Parsing and Standardization
  • Data Patterns and Pattern Analysis
  • Standardized Formats
  • Data Value Transformation
  • Parsing and Standardization Options
  • Record Matching, Duplicate Analysis, Merge/Purge
    • The Many Uses of Record Matching
    • Exact Duplicates vs. Approximate Duplicates
    • Approximate Matching
    • Record Linkage and Duplicate Analysis Options
  • Data Enhancement
    • Different Kinds of Data Enhancement
    • Data Enhancement Options

Information Quality Assessment

Objective data quality measurement relies on metrics relating directly to how information is being used and how missed expectations impact the business. Once expectations are isolated and understood, we can define assertions that capture those expectations that are used for measuring how information complies with those data quality rules. These rules, which seed our objective data quality metrics, are knowledge-based metadata related to the data sets, suitable for incorporation into the metadata repository. This session discusses the process of exploring information using data profiling tools, identifying data quality rules, and isolating noncompliance as a sequence of stages.

  • Identifying Critical Information Quality Impacts
    • Capturing Historical Impacts Information
    • Classifying Information Quality Impacts
  • Assessment Using Data Profiling
    • Data Set Selection
    • Profiling and Review
  • Identifying Information Quality Rules
    • Identifying Key Information Quality Rules
    • Associating Flaws with Impacts
    • Developing Measurement Criteria

Rule-Based Information Quality

Information quality revolves around "fitness for use," and as more information is used for multiple purposes, the perception of fitness depends on the information consumer and the corresponding application context. In essence, fitness for use depends on compliance with the expectations of the knowledge worker, and being able to measure compliance with those expectations can provide an objective assessment of the quality of the data. Most data quality expectations can be expressed as formal business rules. In this session we present a framework for defining business rules for information compliance, as well as techniques for using these rules as a component of an information quality and knowledge management program.

  • Classes of Information Quality Rules
    • Attribute-Level Rules
    • Record-Level Rules: Consistency, Completeness, Exemption
    • Table-Level Rules: Uniqueness, Dependence, Consistency
    • Cross-Table Rules: Relational, Dependence, Consistency
  • Formalizing Information Quality Rules
    • Formal Representation of Rules
    • Rule Management
  • Rule-Based Validation
    • Rule Engines
    • Automation for Validation and Continuous Monitoring

Data Quality Tools – Requirements, Acquisition, Integration

When a need can be determined that requires a specific kind of tool, the next step is to understand what is necessary to evaluate and acquire the necessary technology. In this session we look at developing a business needs assessment and the steps taken to review and acquire data quality technology.

  • Business Needs Assessment
    • Determining Data Quality Tool Needs
    • Review of Data Quality Tools
  • Acquisition Plan
    • Developing an RFP
    • Evaluation Criteria
    • Critical Application Characteristics: Functionality, Integration, Performance
    • Pilot Projects

Data Stewardship, Data Standards, and Metadata

Organizational changes associated with how data ownership responsibilities are viewed when looking at overall information improvement. To this end, data stewardship policies are necessary. In addition, in any environment where data is exchanged, some degree of standardization must take place to ensure proper understanding and processing of information. Most of this knowledge is embodied by metadata, and in this session we discuss data standards, data stewardship, and managing metadata associated with information quality.

  • The Need for Data Standards
    • Information Exchange
    • Standards for Improved Information Quality and Increased Automation
  • Data Stewardship
    • Stewardship Policies and Procedures
    • Stewardship and Information Management
  • Data Standards and Metadata Management
    • Metadata Concepts
    • The Data Standards Repository

Building the Information Quality Program

In this last session we will review the high-level concepts discussed during the seminar, and then look at an approach to developing the Information Quality Program Plan.

  • Information Quality Planning
    • Setting the Scope
    • Assessment and Business Case
    • Goals and Metrics
    • Gap Analysis
    • Prioritizing Actions
  • The Success Strategy
    • Senior Management Approval
    • Plan for Incremental Success
    • Maintaining Consistency with Long-Term Goals

2 days

Course Format
Lecture, group discussion and exercises

David Loshin

To request a quote for this in-house seminar
Please call (973) 632-0138 or email info@debtechint.com

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