In this part we're going to talk about how important it is to know where you are starting from, before you head off on a new application journey. Understanding and mapping your legacy systems is a key success factor for a data migration project, but can be a very difficult and time consuming battle.
This article identifies the 10 most commonly made mistakes when designing a real-time data warehouse and gives advice to help you avoid these pitfalls. Foreword Real-time data warehousing is clearly emerging as a new breed of decision support. Providing both tactical and strategic decision support from a single, consistent repository of information has compelling advantages.
The result of such an implementation naturally encourages the alignment of strategy development with execution of the strategy. However, a radical re-thinking of existing data warehouse architectures will need to be undertaken in many cases.
Evolution toward more strict service levels in the areas of data freshness, performance, and availability are critical. The pages that follow identify the 10 most commonly made mistakes when designing a real-time data warehouse and give advice to help you avoid these pitfalls.
Creating real-time feeds into a data warehouse is often perceived as a critical aspect of bringing intelligence to the real-time enterprise.
While technology vendors may be enamored with real time, it is critical to focus on business-driven solutions and avoid getting caught up in the technology hype. Real time should not be the goal for a data warehouse implementation in and of itself.
A better goal would be right-time data warehousing—whereby the service levels for data freshness are driven by business goals rather than technology hype. Just because real time can be implemented, does not mean that it should be implemented in all cases.
The idea of right-time data warehousing is that data freshness service levels are aligned with and driven by the needs of specific business processes. Aligning service levels for data acquisition with the business processes within an organization will result in more cost-effective implementations and, ultimately, better return on investment.
For example, consider the requirements for an analytic application designed to support decisions related to exception handling for late flights in an airline data warehouse.
Does data acquisition of late flight events really need to occur in a small number of seconds from the operational bookkeeping systems? The airline will know at least 10 minutes prior to the flight landing time whether it will be late or not. Assuming that the analytic decisions related to gate assignment, holding connecting flights, re-accommodation, and so on can be accomplished within a reasonable amount of time minutesthe need for immediate small number of seconds acquisition of the late flight event would be overkill.
A more cost-effective capacity plan and implementation solution can be realized when the data freshness requirements are not overstated relative to the business requirements.
The immediate capture and analysis of test data from the assembly lines is essential for process control and quality management. Proactively detecting machine drift and taking corrective action before missed tolerances force shutdown of an assembly line for more drastic repairs can mean millions of dollars in savings.
However, it is important not to make short-sighted decisions in the design of the data acquisition architecture for the data warehouse.
A well designed architecture will allow for increasing data freshness SLAs as business requirements evolve.
It is important to implement a scalable solution that can be adjusted upwards in capacity to support more aggressive data freshness according to the needs of maturing business processes.
Re-writing or re-architecting a data acquisition infrastructure due to lack of foresight can be a significant drain on the ROI for a real-time data warehouse. On the other hand, over-engineering the initial implementation can be just as big a drain. The key is a scalable architecture that allows just the right amount of capacity to be deployed at each stage of evolution in deployment of an organization's real-time data warehousing capabilities without code re-writes!
Confusion between Bookkeeping, Decision Making, and Action Taking Life was much simpler in the early days of data warehousing. It was possible to make almost black and white distinctions between the online transaction processing OLTP systems and the data warehouse.
The OLTP systems were online and operational in nature. Data warehouse solutions were batch oriented and definitely non-operational. However, significant changes have taken place over the last 15 years of evolution in data warehousing.Data Management is a broad field of study, but essentially is the process of managing data as a resource that is valuable to an organization or business.
One of the largest organizations that deal wit. Data quality refers to the condition of a set of values of qualitative or quantitative variables.
There are many definitions of data quality but data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers.
In my last blog post I showed the basic concepts of using the T-SQL Merge statement, available in SQL Server onwards..
In this post we’ll take it a step further and show how we can use it for loading data warehouse dimensions, and managing the SCD (slowly changing dimension) process.
Hi Guys, I have the following situation in one of the projects i'm managing. (Sorry for the length of the post, just thought that sometimes the question gets mis-interpreted if enough data is not provided). Data Warehouse Modeling and Quality Issues: Introduction Panos Vassiliadis Ph.D.
thesis Page: Chapter 1 Introduction 1. DESCRIPTION A Data Warehouse (DW) is a collection of technologies aimed at enabling the knowledge worker. This is part three of an ongoing series that's taking a look at data migration projects.
In this part we're going to talk about how important it is to know where you are starting from, before you head off on a new application journey. Understanding and mapping your legacy systems is a key success factor [ ].