eLearningCurve

One of the most common and important activities in data management, data profiling is the process of analyzing actual data and understanding its true structure and meaning. Data profiling is the first critical step in many IT projects, and is truly the key ingredient to successful data quality management. Tune in to this episode of The Learning Curve to hear data quality management guru Arkady Maydanchik cover the what, why, when, and how of data profiling. Data profiling techniques of varying levels of complexity will be discussed, as will topics related to the gathering, analysis, organization and performance of data profiles and data profile initiatives.

Getting started with any major initiative can be difficult, and data governance is no exception. Last week, eLearningCurve instructor Maria Villar outlined the major elements of a data governance program. In this episode of The Learning Curve, her partner, Theresa Kushner, introduces you to how others have already started this journey. In the case study portion of this training, you are introduced to elements of a program dedicated to governing customer data. Designing and implementing a Data Governance Organization Model will help your organization get on the right track for saving money and improving data-related processes. Ideally, a Central Governance Team will report to senior management, and serve as a liaison to various data experts from across the enterprise. That's just one aspect of successful data governance.

Selecting an MDM vendor can be a daunting task, unless you have an insider showing you the way. That's exactly what Andy Hayler does in this episode of The Learning Curve. The former founder of MDM stalwart Kalido, Andy is now an independent analyst, using his inside knowledge of the industry to help clients navigate the increasingly complex waters of Master Data Management. Tune into this Webcast to hear Andy take the guesswork and uncertainty out of the vendor selection process. He'll outline his structured process for product evaluation, which includes technical, commercial and functional criteria. This Webcast offers a preview of Andy's full-length course at eLearningCurve.com. The in-depth workshop offers a framework that describes the model of an ideal MDM product. The framework and the process, when combined with a survey of the MDM market, positions you to quickly create a short-list of vendors and then to conduct a detailed evaluation of short-listed products.

A data governance program should encompass all of the policies, standards, processes, people, and technology that are essential to managing critical data assets. Successful data governance programs are matched to an organization's culture, information maturity, priorities, and sponsorship. Tune into this episode of The Learning Curve to hear Data Governance expert Maria Villar, who will outline some of the key components of this growing discipline. You'll learn the business case for Data Governance, which types of data should be governed, and implementation fundamentals for successful governance, including standards and policies, processes, people and technology.

One profession that continues to grow in this difficult economy is Data Quality. More and more organizations are recognizing the value of Data Quality initiatives, and as a result, there are jobs to be had. But what kind of jobs? And what are the necessary skills to get those jobs? Tune into this episode of The Learning Curve to find out!

As today’s business environment continues to evolve and grow ever more complex and competitive, data integration is required to coordinate master data and achieve a single, consistent view of organization’s core business entities. MDM solutions enable master data synchronization, combining disparate applications and business units under the umbrella of a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data. William McKnight’s Introduction to Master Data Management provides the tools necessary for real world MDM solutions.

Master Data Management (MDM) presents even well seasoned data management professionals with unique and complex challenges that might not be fully foreseen from the outset of the project. The intricacies of managing identities and hierarchies, and resolving conflicts among disparate data sources, make MDM an ambitious but potentially fruitful undertaking; particularly in light of the far-reaching and multifaceted implications that MDM has on organizations. MDM guru Andy Hayler brings his experience and expertise to the table in this episode of eLearningCurve’s Webcast series. Andy will discuss MDM’s relation to other IM disciplines, the concepts and terminology of MDM, architectural options for MDM implementation, advice on building a business case for MDM, and best (and worst) MDM practices.

Given the scale and complexity of the data quality field, data quality practitioners need a strong understanding of the fundamental concepts, principles, and terminology that are common in quality management. Building upon that foundation, they need to understand how quality management concepts and principles are applied to data and learn the language utilized in the data quality world. In this episode of The Learning Curve, data quality visionary David Wells provides an overview of the concepts, principles, practices, and terminology of the data quality field. Webcast duration is 20 minutes.

With the advancing of time and cumulative growth of corporate databases comes an endless process of upgrading and redesigning the programs responsible for data integration, as well as the databases themselves. The typical result of these dynamics is that information systems get better, while data quality deteriorates. Without a comprehensive data quality-monitoring program, bad data spread like viruses. In this episode of The Learning Curve, data quality guru Arkady Maydanchik discusses various practices that can be put in place to mitigate the problem and maintain high data quality through data integration. Webcast duration is 20 minutes.

Given the far-reaching and long-lasting implications of data models, and the temporal and financial realities of the environments that models are typically constructed in, the importance of effectively creating an objective measure of data model quality comes to light. In this episode of The Learning Curve, data quality specialist Steve Hoberman will provide insight into the tools and techniques needed to strengthen data models. In this 20-minute Webcast, he will outline the creation and application of data model scorecards and the use of automated tools to enforce modeling best practices and standards.