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This free resource is made available by the Rainmaker Group to share our domain expertise in all things data warehousing and business intelligence.
Our only request - if you find the content here useful, refer a friend. Our goal is to build the most comprehensive library of whitepapers related
to data warehousing and business intelligence on the web. You can submit a request for a paper, or submit your whitepaper for inclusion to
feedback@rainmakerworks.com. Content is added frequently so stop back often!
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| Digital Dashboard Design Best Practices |
| Digital dashboards are an excellent tool for quickly communicating the status of key performance indicators (KPI). The visual presentation of a digital dashboard allows executives and managers to quickly identify problem areas and take immediate action. Like all tools digital dashboards must be designed with the end user in mind and with a specific purpose. |
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| Business Intelligence & Data Warehouse Tool Selection |
| Selecting the right business intelligence and data warehouse software can be difficult and confusing. The Rainmaker Group has created an extensive list of criteria for selecting the right OLAP, and BI/DW software. Each criterion should be scored based on the need and associated importance for your organization. |
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| Making Data Actionable |
| The increasing acceptance and implementation of business intelligence technology including data warehousing, OLAP , and data mining is putting rich data analysis tools into the hands of business end users but many continue to find they have too much data and not enough information. It?s imperative the technology make data actionable to be of real value. The dichotomy of data warehousing and OLAP is while it provides a rich data analysis experience information must be translated into action to be useful to the business. This article provides practical tips on making data actionable so end users can quickly apply information to improve the business. |
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| Using Your Existing Data To Maximize Profits |
| There is a growing realization among business leaders that they have too much data
and not enough information. After spending the 1990's investing heavily in new core
software systems most businesses' key processes are automated and running fairly
smoothly. But most managers still tell us decisions are generally based on 'gut-feel' not
data and facts. When you look at the most successful companies in various industries
you see one common element - a great ability to harvest minute by minute transactions
and transform that data into wise decisions. |
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| Data Warehouse Design: OLTP vs. OLAP |
| The design of a data warehouse database and online analytical processing (OLAP) cubes is fundamentally different than a transactional processing database (OLTP). The data warehouse is specifically designed to facilitate super fast query times and multi-dimensional analysis. The following table summarizes the major differences between OLTP and OLAP system design. |
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| Analysis Best Practices - Price Elasticity |
| Price Elasticity is a measure of how demand for a product is affected by price changes. This
measure can help determine whether to change the price of products by calculating what effect
price changes have on the quantities customers buy. |
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| Closing the Fact Gap |
| The volume of data available for analysis is increasing exponentially, but the resources available to turn the data into useful information are becoming scarce, giving rise to what has become know as the Fact Gap. However, with the right technology and the right approach to analysis, this gap can be filled, and it is the companies which exploit the available technology that will leap ahead of their competitors. Learn how to close the fact gap in your organization. |
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| Quick Extraction, Translation & Load (ETL) Checklist |
| While there are many difficult questions, there are no simple answers when it comes to ETL Extraction, Translation, and Loading) of a data warehouse. ETL projects are often underestimated, under planned,under staffed, and even under implemented. It is no wonder that a data warehouse ETL project can go over budget. Follow this quick check list to ensure ETL success. |
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| Building the Business Intelligence (BI) Competency Center |
| Building the business intelligence (BI) competency center within an organization requires broad commitment and a team with cross functional skills. To be successful the BI team must consist of resources with business skills,
technical skills, and analytical skills. |
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| Business Intelligence - The New Competitive Advantage |
| There is a new competitive weapon being leverage by successful organizations and it is quietly
recognized by some senior executives as the primary reason for their increased profitability and success. As a consumer you have no doubt experienced the impact. Consider for a moment how you receive coupons for dog food just about the time the bag in the garage is running low, or how Wal-Mart always seems to have one or two items on your shopping list conveniently placed right next to the shopping carts at the entrance to their stores. (download full article) |
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| Shedding Light on Shadow IT: Is Excel Running Your Business by Neil Raden |
| Shadow IT, those people performing IT functions but not part of the mainstream IT organization,
have been found to be as much as 78% of the size of the total official IT staff. The existence
of Shadow IT implies a failure on the part of IT to provide all of the services to meet their
clients needs, and the problem is universal. This is especially acute in Business Intelligence
(BI), which is the largest segment of Shadow IT. The vast majority of knowledge workers perform
their modeling, reporting, number crunching and sharing with spreadsheets and personal
databases, often in spite of the existence of IT-sponsored BI efforts. |
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| Using Business Intelligence (BI) In Manufacturing |
| Business intelligence has long been used by financial services and retail organizations to create competitive advantage but manufacturing decision makers have struggled with how to apply the technology in their world. This whitepaper written by Bill Hays, Rainmaker Group Engineering Manager, discusses how manufacturers leverage Business Intelligence and OLAP technology to consolidate disparate data sources for accurate decision making, improve quality, reduce machine downtime, and better labor cost control. |
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