This article originally appeared in the Volume 22, Number 4 issue of TDWI's Business Intelligence Journal.
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Data Warehouse Appliances: Lessons from the Trenches
By Jerry Locke & Steve Dine
Over the past few years data warehouse (DW) appliances have become a viable option for business intelligence (BI) programs looking to load and process large data volumes as well as reducing query execution times. However, like most architectural decisions, there are many factors to consider when choosing to implement a DW appliance that aren’t usually highlighted during the sales presentation. This article will present some observations and lessons learned from a recent project in which we implemented a DW appliance for both the data warehouse and the data mart layers of the client architecture.
By: Steven Dine, President of Datasource Consulting, LLC
I am always dismayed when reading articles and analyst reports that include a statistic of the percentage of Business Intelligence (BI) initiatives that fail or do not meet customer expectations. Most of those articles and reports go on to describe the now well-known reasons for why these types of initiatives fail, including lack of high-level sponsorship, poor data quality, low level of functional participation, unacceptable query response times, etc. What I never see mentioned, but often see in practice, is the incompatibility between the requirements and the implemented architecture in consultant-led initiatives. How does this happen when nearly every consulting organization has a BI implementation methodology and experienced resources in implementing BI programs? While there are many causes, one often overlooked reason is the inconsistency in how BI consulting companies define the basic BI concepts. Many times the definitions depend on which consultant at the company you ask. I have yet to find these definitions posted on even the largest BI consultancy websites.
Why are Business Requirements Important to BI and Data Warehousing Projects?
By: Datasource Consulting
When considering a Business Intelligence (BI) and Data Warehousing project, it’s extremely important to not overlook the process of gathering, prioritizing, and agreeing on the project’s business requirements. This process of discovery is done after the company’s business objectives are documented, validated and organized by each functional business area.
What is Lean BI and its Purpose?
By: Steve Dine
I have yet to meet a BI team that has too little on its plate. In this article we will look at some of the drivers creating all this work and offer a set of principles that can be used to become more efficient and effective while still focused on delivering value. The main idea for this methodology evolved while leading a BI team for a leading medical manufacturer. We were frustrated by the amount of time spent maintaining our existing infrastructure and the number of features/data elements being added to the data warehouse that weren’t being utilized. During that time, the company was advocating Lean manufacturing principles and practices in our European divisions and there appeared to be some parallels to our situation. Our dabbling into Lean was the original root that grew into this concept.
Business Intelligence: Perception and Reality
By: Steve Dine
How many times have you heard a statistic such as "42% of respondents rate their Business Intelligence (BI) program as moderately successful" or "more than 50% of all Business Intelligence projects fail"? When I read these types of statistics I often wonder what's behind these numbers. I'd love to be able to drill down directly to the respondents and ask them how they defined success or failure when answering the question. I recently met with a company that asked if I could come in and help make their Business Intelligence program more successful. Naturally, my first question was to ask them how they define success. Each person in the room defined success a bit differently and meeting turned into a healthy discussion on what constitutes a successful Business Intelligence program.
Business Intelligence Tools: 10 Steps to a Successful Tool Evaluation
By: Dave Crolene
Datasource recently completed a project where we helped a client evaluate and compare two business intelligence tools, both were data virtualization products. As an objective facilitator, Datasource Consulting was called up on to ensure that both products had a fair and even playing field and to help discover the strengths and weaknesses of both tools to help drive a decision. Whether you are evaluating Business Intelligence tools (BI) , Extract Transform Load tools (ETL), Relational Databases (RDBs), etc, this article provides a step-by-step approach that we use to compare and evaluate products.
Business Intelligence Projects: Why They Fail (Part 1 of 5) - The Crumbling Foundation
For Business Intelligence projects (BI) to be successful, they must meet user requirements, be designed with strong user involvement, include a manageable scope, provide fast query response times, deliver the required reporting and analytic capabilities and provide value. Also, as nearly all experienced BI practitioners will attest, for business intelligence projects/programs to be successful, they must be built iteratively. In the trenches, that means sourcing, integrating, and developing front-end capabilities that target a subset of the enterprise data in each project. Sometimes the scope of an integration project is limited to a specific organizational process, such as invoicing, and other times by system, such as order-to-cash. Either way, subsequent business intelligence projects add to the scope and volume of data that is integrated and available for analysis. For initial business intelligence projects and subsequent iterations to be successful, the data architecture must be scalable, flexible, understandable and maintainable. However, most organizations put more thought and effort into their selection of BI tools than their data architecture. The end result is that they implement a data architecture that crumbles under the growing requirements of their users and data.
Enterprise Data Management - Key Definitions of industry terms
At Datasource Consulting, it is fairly common that our friend and even some colleagues will ask us about some of the Enterprise Data Management terms we use on a regular basis. It also seems that everyone is aware of how fast the Enterprise Data Management industry is moving and how quickly new terms pop up.
Below, you will find definitions to some of the Enterprise Data Management terms that we come across frequently and have a meaningful tie-in to the Business Intelligence and Data Integration space.