DAMA + Wilshire Meta-Data Conference - Data Strategy Track
Once you have recognized that poor data quality impacts your business, how do you address the elimination of the sources of data flaws? Data quality tools and technologies are employed in different ways to help discover data issues, isolate the source of their introduction, correct the data (if necessary), and adjust the processes to prevent unexpected data from entering the environment in the first place. This seminar explores different data error paradigms and approaches for addressing those errors using data quality tools. We’ll also look at the different tool types, how they are intended to work, and developing best practices for standardizing the way the tools are used across the enterprise. Lastly, we’ll discuss how to assess business needs to determine which tools are most appropriate to be integrated into your organization.

Attendees will learn about how the following approaches work and how they integrate with an enterprise data quality program:
  •  Data parsing and standardization
  •  Record Linkage/Matching
  •  Data scrubbing/cleansing
  •  Data enhancement
  •  Data profiling
  •  Data auditing/monitoring
Do these situations sound familiar? Your company is involved in a data integration project such building a data warehouse or migrating several source systems into an ERP (Enterprise Resource Planning) application. Data quality issues are impacting the project timeline or users are distrustful of the information that is provided. Whether you are just starting the project or are already in production, it is not unusual to find that information quality issues prevent the company from realizing the full benefit of their investment in the new systems. Join us to learn practical approaches to improving the quality of information critical to running your business, satisfying customers, and achieving company goals.

Key topics include:
  • Ten steps to quality data and trusted information
  • The Information Quality Framework
  • Dimensions of data quality
  • Business impact of information quality
  • Real-life application of the methodology
Come with your particular needs in mind, learn how these topics apply to your situation and leave with realistic methods for improving information quality.
How can you measure the quality of something if you don’t know what it is in the first place? Good, robust, precise definitions are central to the measurement of data quality. The definition of any data element should include an objective standard for measurement of that data element. This presentation will cover the following points:
  •  Why definitions are the key for data quality
  •  What are the components of a good, precise definition
  •  How the definition of fundamental business terms affect both the business and business metrics
  •  Why data quality metrics are business metrics
  •  How data quality affects the bottom line
  •  How do you measure 100%? And why this is important to the business
Is your system migration doomed from the start? Embedded in your legacy applications are business processes and data model assumptions that could sink your project or literally add years to a full cutover. Your implementation path is likely strewn with minefields of code patches and undetected data quality gaps. This presentation will cover real-world examples, warning signs, and practical techniques for keeping your career and company above water. It will also touch on ways to avoid, and if necessary, navigate costly litigation.

Rigorous data profiling precedes every successful data integration initiative, and routine profiling of integrated data helps sustain data quality over time. Explore data profiling tips and techniques using metadata frequency distribution, patterns, data structure, data standardization, calculations, business rules, and referential integrity independent of specific tools. In this session, you\'ll learn specific techniques for dealing with challenging fields including dollar amounts, dates, social security numbers, and multi-use fields.
Poor data quality in the enterprise results in lost business, reduced productivity, unnecessary expenses, failed IT initiatives, inability to comply with government regulations and even outright operational failure. Conventional data quality tools rely on the manual creation and maintenance of expansive sets of rigid "rules" and "dictionaries," which render them expensive, time-consuming, difficult and disruptive to implement. This expense and difficulty leaves many organizations without a practical solution to this critical business issue.

Learn how a new class of data quality solutions, based on self-learning technology, is enabling every corporation to quickly, easily and non-intrusively achieve high-quality master data across the organization. In addition to shorter time frames and more accurate results, these solutions naturally enable tight business process integration, thus bringing the benefits of corporate information quality efforts to all business processes. In practical terms, numerous routine operational and analytical functions will benefit substantially through on-demand data management facilities over SOA.
  •  Enterprise data quality: an increasingly dangerous problem
  •  The challenges in achieving enterprise data quality
  •  The limitations of conventional data quality solutions
  •  How self-learning technology is applied to corporate data quality
  •  Improving operational and analytical functions with real-time data quality facilities using SOA
The Zachman Framework precisely defines the structure of the set of “data models” that bridge between the Enterprise Strategy for Inventory Management, the context for defining the Enterprise’s data models, and the instantiation of the data in the Enterprise databases. This structural alignment is fundamental for managing Data Quality and Administration of the Data.
Data Quality via Data Profiling at Allstate Insurance Company.

This presentation discusses an initial approach to one aspect of data quality via the use of a data profiling tool. How the tool was deemed necessary, what capabilities it provides, and what approaches are being taken to effectively use data profiling will be covered. Also covered will be the issue of information protection and it’s affects on quality assessments.
  •  demonstrates how data profiling assists data quality
  •  presents some real examples
  •  stresses the importance of metrics both for tracking and for marketing funding opportunities
  •  outlines Sarbanes Oxley concerns
With the goal of improving student data collected from across the state, the New Hampshire Department of Education has developed an automated, inline validation system which allows data owners in 180 school districts to submit data, view validation results online, and modify data submissions to correct errors.

This presentation describes New Hampshire’s data validation system in terms of its success in promoting:
  • Improved communication and feedback between regulators and data owners
  • Better understanding of data issues and business rules
  • Shorter cycle times for problem identification and correction
  • Measurably higher levels of data quality across the state

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