Data Warehousing

Data warehousing is a term that is used frequently but many people in information technology don’t grasp the importance of this environment to the success of their decision-making processes. As the storage and access facility of an organization’s historical record of decision-related data, data warehouses contribute to the advancement of business intelligence and improved decision-making. Strategic data warehouses provide a way for organizations to collect, store, relate and disseminate critical historical data for business intelligence purposes. The Data Warehousing Portal can serve as your entrance to the fascinating and challenging world of this powerful set of technologies, practices and data. This portal contains articles, white papers, discussion forums and other resources about data warehousing and business intelligence.

Data Warehousing Trends for 2000

Over the last 10 years decision support has evolved from the "hottest" new industry buzzword to a technology that has demonstrated that it can provide significant value to a corporation. Now as decision support moves into the 21st century we are maturing in ways we could never anticipate those many years ago. It’s critical to understand these trends, as they will impact most every corporation. In this article we will look at these trends and make some predictions that are currently changing data warehousing or will occur over the next 12 months.

“Independent” Data Marts: Being Stranded on Islands of Data - Part 1

There is a severe disease that has spread to epidemic proportions throughout our society. This disease is particularly dangerous as it effects are not readily identifiable at the time of infection. However if this condition goes untreated it can be debilitating and even terminal. This disease is not hepatitis, but rather "independent" data marts.

“Independent” Data Marts: Being Stranded on Islands of Data - Part 2

This column is the second portion of a three part series on migrating from independent data marts. In part one of this series we examined the characteristics of independent data marts, the flaws in their architecture, and the reasons why they exist. This installment shall focus on the approaches for migration, initial planning and how to identify a migration path.

“Independent” Data Marts: Being Stranded on Islands of Data - Part 3

This column is the third and concluding portion of a three part series on migrating from independent data marts. In parts one and two we examined the characteristics of independent data marts, the flaws in their architecture, the reasons why they exist, approaches for migration, initial planning and how to identify a migration path. In this month’s column I will present a case study illustrating how a corporation can migrate from independent data marts to an architected data warehousing solution.

Which Should Come First the Chicken or the Egg (Meta Data Repository or the Data Warehouse)?

Over the years I’ve given more than a hundred keynotes/seminars on data warehousing and meta data. During these talks I’ve been asked the question of which should you build first many times. After giving it careful thought I have come to the conclusion that a corporation’s optimal approach is to FIRST build their meta repository. Let’s examine the reasons why.

XML’s Uses In Data Warehousing: Getting Data In

Over the past 10 years, data warehousing has proven to be a highly valuable technology that the vast majority of corporations have leveraged to provide them with a competitive edge in the marketplace. As we enter the next decade XML (eXtensible Markup Language) is poised to accomplish much the same. The one unanswered question is how will these two essential technologies function together? This month we will present how XML enables our data warehouses to bring in external data (from websites and other corporations).

Data Warehouse Roles and Responsibilities

The classic definition of a Data Warehouse is architecture used to maintain critical historical data that has been extracted from operational data storage and transformed into formats accessible to the organization’s analytical community. The creation, implementation and maintenance of a data warehouse requires the active participation of a large cast of characters, each with his or her own set of skills, but all functioning as a series of teams within a large team.

Data Warehousing: a Short Overview

Many corporations are experiencing significant business benefits using data warehouse technology. Users report gains in market competitiveness (increased revenue) and information management (reduced costs).

Data Warehousing and Enterprise Resource Planning – A Combination of Forces

Since the introduction of the term “data warehousing” in 1990, companies have explored the ways they can capture, store and manipulate data for analysis and decision support. At the same time, many companies have been instituting enterprise resource planning (ERP) software to coordinate the common functions of an enterprise.

Data Warehouse Indicators of Success (Part 1)

There has been much heated discussion over the failure rate of data warehouses. Luminaries disagree on the percentage of those that have succeeded. The problem may be with the definition of success and failure. In fact, very few organizations have identified, up front, what for them will be success or failure. That being the case, any industry-wide numbers on failure are meaningless.

Data Warehouse Indicators of Success (Part 2)

In our previous article, we talked about the measures of success. In this article, we will discuss the factors that are necessary for a project to succeed, and how to measure your data warehouse results to determine if your project was indeed a success.

Data Warehouse Goals and Objectives: Part 1

In this article, we will examine the traditional decision support systems (DSS) and the reasons why they have failed to provide complete, correct, and timely information to the organization. In the two follow-up articles, we will describe how short term and long term data warehouse objectives address the deficiencies of traditional DSS environments.

Data Warehouse Goals and Objectives: Part 2

In our previous article (DM Review, date) we identified all the deficiencies in traditional decision support systems (DSS), and we mentioned the difficulties of data management.
We can now recognize the role a data warehouse plays, or ought to play, in a data management solution. A data warehouse is not just another DSS database. It is an environment of one or more databases designed to deliver consistent and reconciled business intelligence to all business units in the organization.

Data Warehouse Goals and Objectives: Part 3

In our previous article (DM Review, date) we reviewed common short term data warehouse objectives. In this article we will conclude our series with a discussion about long term data warehouse objectives and the importance of synchronizing all data warehouse objectives with the strategic goals of the organization.

Planning for the Evolution of the Data Warehouse

There are several things to consider when planning the evolution of a data warehouse. Some of these are obvious, while others are often overlooked.

The most obvious area given much attention to is scalability of the technical platform. Because a data warehouse cannot be developed in one big bang, growth in terms of database size, number of users, network size and complexity, and hardware capacity are usually anticipated and planned for. Even though the growth factor is often underestimated, some exercise in capacity planning is still performed.