Knowledge Integrity: Master Data Standards and Data Exchange

Knowledge Integrity: Master Data Standards and Data Exchange

This topic surfaces frequently, and it relates to the use of agreed-to data set formats for data interchange whose use somehow diverges from their original intent. The changes occur subtly over time, and in potentially multiple places; therefore, until an analyst actually sits down to review the different uses, the variants would not even be noticed.

Data Integration Advisor: ODS Redux, Part 2

Data Integration Advisor: ODS Redux, Part 2

Conventional wisdom doesn’t always age like a fine wine. When it starts smelling like dirty socks, it’s time to challenge it. Sometimes the conventional wisdom published in books, written in articles and presented in seminars is accepted as gospel, even when it starts to develop a distinctive odor. Examine conventional wisdom periodically to determine if it is current and relevant. This is particularly true when it comes to an ODS. The purposes and methods used for building the ODS may have changed dramatically over the years.

Meta Data Classification

Meta Data Management in the Enterprise

At a very high level, meta data can be classified into two categories. Shared meta data Unique meta data
Shared meta data elements need to have consistent definition and semantics across the enterprise.� For example definition of a Customer entity should be homogenous throughout the enterprise.

Knowledge: The Essence of Meta Data: The Meta Data Support Model, Part 2

Knowledge: The Essence of Meta Data: The Meta Data Support Model, Part 2

The customer doesn’t want to feel alone and without support. Many times consultants and IT projects come into the environment and leave without a trace of letting the customer know how they can get further help. The customer support model provides three specific areas where the support team can provide help and assurance. The areas include an online environment, subject matter expert (SME) network and external resources.

Enterprise Information Integration: A Pragmatic Approach

Enterprise Information Integration: A Pragmatic Approach

Unfortunately, many of the systems we’re relying on today, to provide us critical business information, were not designed with this task in mind. Many of these systems were designed for the sole purpose of increasing productivity. Thus, only the data necessary to produce the intended increase was incorporated into the system. Fortunately, the need for greater and greater productivity has forced us to change systems or build new ones to meet the demand. This process has resulted in data accumulating in rough layers that have been loosely knitted together, to provide us with many of the answers we need today to remain competitive. However, these layers have become so fragmented and isolated, that it requires Herculean efforts to make sense of it.
To drive this point home even further, the CEO of a major logistics and transportation company called his efforts to obtain a consolidated view of all business done with a particular trucking company across all business units, a “Chinese fire drill.” This quote indicates that his information systems do not support his needs, and instead, require the chaotic process of humans manually pulling data and consolidating it, in order to obtain the values this CEO needs.

Sounds like most of the organizations that I work with. Mr. Morgenthal’s book may be useful in the re-education process.