What is a data warehouse, how do I use it, and what's in it for me? How should you instruct and inform your data warehouse users on these subjects? Here is an informative paper on the inner workings of a data warehouse that was built to perform category management analysis for a retail grocery chain.
Stolen from Real Life (a data warehouse development project for a waste water treatment management system), this Microsoft Project plan takes you through the construction of a complete data warehouse from start to finish.
Need to plan a data warehouse project? This synopsis outlines the steps, deliverables and team roles for strategizing, architecting, designing and planning the implementation of a data warehouse.
Your clients are probably curious about the elusive "metadata" and knows it only as "data about data". Show them via a simple diagram how metadata, the reference information about the business data in the warehouse, fits into the data warehouse structure.
Here's a reusable project plan for developing a Data Warehouse logical data model, an important architectural component of the overall data warehouse.
Why build a data warehouse? Be sure you have good justification in the form of a solid business case before taking the plunge.
An evaluation of relational database systems determines the most suitable engine for a data warehouse. This report is an example of a comparative analysis of data warehouse database tools.
This super-comprehensive project plan in Microsoft Word is chock full of information on how to plan and manage a data warehouse project. It covers the gamut in DW project planning and control activities and will teach you a lot about data warehouse architectures, tools, risk management, development activities, quality management and the like. Enjoy the read!
Need help selecting a Data Warehouse toolset? You must consider many factors regarding vendor service and reputation, tool functionality and tool quality against your technical and business requirements when selecting Data Warehouse construction and implementation tools. Let this matrix guide your selection.
This Microsoft Project Plan will walk you through the ins and outs of putting together a business question assessment to analyze the data you need for your data warehouse.
Do you need a clear, understandable and consensus-driven logical data model for a client data warehouse implementation? This sample Logical Data Model Project Plan (in Word format) outlines the information you will need to include in your plan.
Map the transformation of the data elements from source system tables to data warehouse target tables.
Your client has asked you to analyze his business and present him with a roadmap for making the business data available via a data warehouse. This Microsoft Project plan presents a two-month business analysis project to achieve that goal.
If you've been tasked to build a data model for a Data Warehouse development project, check out this Microsoft Project plan.
A handy spreadsheet for recording Data Warehouse QA issues, from data transformation to report generation problems.
Oops! isn't what your client wants to hear at the eleventh hour in the delivery cycle. Use this sample QA plan to verify that the data warehouse (or data mart) has been designed and built correctly before it undergoes user acceptance testing.
This excellent Microsoft Project plan will help you implement a complete life cycle project to design and build a data warehouse.
Are you putting together a data warehouse? This Microsoft Project plan will help you keep tabs on all the complex stages and steps involved in building decision support systems and a knowledge-based applications architecture and environment.
Here's a template, with attached examples, for testing your Data Warehouse canned queries (analytical reports).
Here's an outline on how to conduct a data warehouse implementation project for a single subject area, from analysis through deployment.
What data should you keep in a Data Warehouse and why? This form helps you identify the needed data and its associated characteristics in relation to your business requirements.
This example will help you put in place logical as well as physical database design standards for building data warehouses using Business Objects.
How do you select the best data extraction/transformation toolset for your data warehouse? Evaluate features, functionality, vendor reputation... the works. Follow the example set by this robust tool evaluation.
Even though you've built the perfect data warehouse, it's better to be safe than sorry if something goes wrong during its operation and you need to restore the original data files. Take the time to document your backup and recovery procedures, as in this example.
This Microsoft Project plan can serve as a guide to evaluating vendor tools for your data warehouse. It includes a Gantt chart schedule and cost analysis for the complete tool selection process.
There's a lot to consider when selecting a Data Warehouse toolset. Use this comprehensive tool evaluation matrix to guide your final selection.
Get your data warehouse into better shape! Use this matrix to find common errors and fixes for them.
There are lots of packaged applications out there, so which is the right one for your business? This thorough questionnaire and checklist will cover the important aspects of the selection process so that you can make an informed decision on a package and vendor. It is particularly oriented toward data warehouse/business intelligence packages, but can be adapted to other kinds of vendor application packages.
Managing customer expectations in an era of aging analytics can be challenging. IT business interface managers need to become more active in understanding business intelligence users’ gripes and "wow" factors regularly.
As the world still comes to grips with the tragedy in the United States, one expert in business intelligence argues that a national data warehouse combining all of the law enforcement agencies' criminal activity and suspect data would be a step in the right direction.
Pack your lunchbox and sharpen your No. 2 pencils. It's time to bet back to the basics of data warehousing.
Does the perfect data warehousing book exist? Click here and check it out.
ITIL analytics can be a powerful enabler in helping better understand the performance of a set of services--and will help you identify ways to streamline and automate operations.
The Coast Guard Aviation Logistics Center will outsource its earned value management system for overseeing about $400 million worth of projects over the next five years.
Tools are only one piece of the puzzle. Here are five reasons why data warehousing solutions have not become as mature as expected, and what you need to do in order to make your data warehousing implementation successful.
It is absolutely critical that IT management use the project resource budget properly to put the right resources in the right roles. Read on for help in using the budget to best staff your team.
Mission-critical projects need to be well-justified, with clear goals that can be referenced throughout the life of the project. This business case template offers an excellent approach to goal-setting and a way to communicate those goals effectively.
Getting your data model right and designing it to scale is the key to building a successful data warehouse. Here's how to make sure your toilet doesn't end up in the kitchen.
Data warehouse projects are, essentially, the biggie size IT project. Project structure, standards and organizational guidelines: these are all magnified when creating the foundation for a successful DW project environment. Don't fear, though, we'll walk you through the basics of a DW project and help eliminate questions and focus on building a successful project.
Project management alone is not enough to tame the beast of data warehousing. It's time to call in the big guns of program management.
They won't be duking it out on pay-per-view any time soon, but the battle wages over who is the true Father of Data Warehousing.
When people think of methodology, they usually imagine the theorists of IT pontificating on the virtues of an application development strategy. Methodology, in truth, is very important to the success of a data warehousing project. Without methodology, a team can walk blindly into a dark room and come out with the stab wounds of a failed effort.
The Sarbanes-Oxley Act is striking fear in the hearts of corporations across America.
Are you implementing a package in a distributed application development environment? You need a plan for the full implementation cycle, from planning and requirements analysis through deployment. Here's a sample from a Warehousing/Shipping environment.
You can walk the walk. Make sure you can talk the talk with this guide to BI buzzwords.
Managing data to reap the benefits of high-performance business intelligence means having to store more data than most systems can handle. That's where SANs come in, and it's why you need to know about this next trend in storage as part of your BI process.
How do you define a consultant? It takes more than a business card to be a true expert in data warehousing. Before you bring in the consultants, make sure your getting the expertise you will most certainly pay for.
Most organizations don't stop and really assess how well their data warehouse will support the purposes it is being targeted for. It is extremely important that all warehouses perform a yearly assessment to analyze the maturity of your data warehouse. Here's some help.
You've muddled through the selection, structure, standards and organization steps of your DW project--now what? It's time to execute. Get the tools you need to work through the daily DW grind. Use them correctly, and you can avoid or resolve common problems.
How do you put a real dollar figure on the value of a BI project? You probably can't. That doesn't mean that you have to forgo determining business benefits, you just have to attack it from a different angle.
