Sherwin-Williams\u27 Data Mart Strategy: Creating Intelligence Across the Supply Chain

Companies can build a data warehouse using a top-down or a bottom-up approach, and each has its advantages and disadvantages. With the top-down approach, a project team creates an enterprise data warehouse that combines data from across the organization, and end-user applications are developed after the warehouse is in place. This strategy is likely to result in a scaleable data warehouse, but like most large IT projects, it is time consuming, expensive, and may fail to deliver benefits within a reasonable timeframe. With the bottom-up approach, a project team begins by creating a data mart that has a limited set of data sources and that meets very specific user requirements. After the data mart is complete, subsequent marts are developed, and they are conformed to data structures and processes that are already in place. The data marts are incrementally architected into an enterprise data warehouse that meets the needs of users across the organization. The appeal of the data mart strategy is that a mart can be built quickly, at relatively little cost and risk, while providing a proof of concept for data warehousing. The risk is that the initial data mart will not scale into an enterprise data warehouse, and what has been built will have to be scrapped and redone. This article provides a case study of Sherwin-Williams\u27 successful use of the bottom-up, data mart strategy. It provides background information on Sherwin-Williams, the data warehousing project, the benefits being realized from the warehouse, and the lessons learned. The case is a textbook example of how to successfully execute a data mart strategy. Video clips of interviews with key individuals at Sherwin-Williams help bring the case alive

Analyzing and Developing Technique for Mining Very Large Databases to Support Knowledge Exploration

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Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

Data Warehousing Failures: Case Studies and Findings

Eight studies of data warehousing failures are presented. They were written based on interviews with people who were associated with the projects. The extent of the failure varies with the organization, but in all cases, the project was at least a disappointment. Read the cases and prepare a one or two page discussion of the following: 1. What’s the scope of what can be considered a data warehousing failure? Discuss. 2. What generalizations apply across the cases? 3. What do you find most interesting in the failure stories? 4. Do they provide any insights about how a failure might be avoided

Building a Data Warehouse and Data Mining for a Strategic Advantage

Technology is fundamentally changing the way companies do business. Consolidations, globalization, and deregulation have put increased pressure on managers to better understand their businesses and take them to the next level. Given the fast-paced business environment today, decision-making cycles have been shortened and managers need accurate information in a timely manner in order to make quality decisions. A properly designed and populated data warehouse can provide the relevant data necessary to make good decisions. Significant advances in computer hardware and end user software have made it easy to access, analyze, and display information at the desktop. The data companies continue to collect from their current information system provides a great source of information about its customers and processes. Data mining software programs are powerful tools that can be used to interrogate the massive amounts of data contained in the data warehouse in order to uncover relationships. To help business leaders and decision makers manage their companies effectively, companies need to make as much information as possible available and give decision-makers the tools they need to explore it according to Kapstone (1995). By implementing a data warehouse and using data mining tools companies can uncover relationships that can be used to achieve strategic advantages. First, I will explain data warehouses, why they are built, and how to build them. Second, I will cover data mining tools and the benefits companies are experiencing by using them. Finally, I will focus on the strategic advantages of building a data warehouse and extracting valuable data using sophisticated data mining tools

improving query performance using distributed computing

Data warehouses are used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP) where short response times are essential for on-line decision support. One of the most important requirements of a data warehouse server is the query performance. The principal aspect from the user perspective is how quickly the server processes a given query: “the data warehouse must be fast”. The main focus of our research is finding adequate solutions to improve query response time of typical OLAP queries and improve scalability using a distributed computation environment that takes advantage of characteristics specific to the OLAP context. Our proposal provides very good performance and scalability even on huge data warehouses

Review of modern business intelligence and analytics in 2015: How to tame the big data in practice?: Case study - What kind of modern business intelligence and analytics strategy to choose?

The objective of this study was to find out the state of art architecture of modern business intelligence and analytics. Furthermore the status quo of business intelligence and analytics' architecture in an anonymous case company was examined. Based on these findings a future strategy was designed to guide the case company towards a better business intelligence and analytics environment. This objective was selected due to an increasing interest on big data topic. Thus the understanding on how to move on from traditional business intelligence practices to modern ones and what are the available options were seen as the key questions to be solved in order to gain competitive advantage for any company in near future. The study was conducted as a qualitative single-case study. The case study included two parts: an analytics maturity assessment, and an analysis of business intelligence and analytics' architecture. The survey included over 30 questions and was sent to 25 analysts and other individuals who were using a significant time to deal with or read financial reports like for example managers. The architecture analysis was conducted by gathering relevant information on high level. Furthermore a big picture was drawn to illustrate the architecture. The two parts combined were used to construct the actual current maturity level of business intelligence and analytics in the case company. Three theoretical frameworks were used: first framework regarding the architecture, second framework regarding the maturity level and third framework regarding reporting tools. The first higher level framework consisted of the modern data warehouse architecture and Hadoop solution from D'Antoni and Lopez (2014). The second framework included the analytics maturity assessment from the data warehouse institute (2015). Finally the third framework analyzed the advanced analytics tools from Sallam et al. (2015). The findings of this study suggest that modern business intelligence and analytics solution can include both data warehouse and Hadoop components. These two components are not mutually exclusive. Instead Hadoop is actually augmenting data warehouse to another level. This thesis shows how companies can evaluate their current maturity level and design a future strategy by benchmarking their own actions against the state of art solution. To keep up with the fast pace of development, research must be continuous. Therefore in future for example a study regarding a detailed path of implementing Hadoop would be a great addition to this field

Data Warehouse and Business Intelligence: Comparative Analysis of Olap tools

Data Warehouse applications are designed basically to provide the business communities with accurate and consolidated information. The objective of Data Warehousing applications are not just for collecting data and reporting, but rather for analyzing, it requires technical and business expertise tools. To achieve business intelligence it requires proper tools to be selected. The most commonly used Business intelligence (BI) technologies are Online Analytical Processing (OLAP) and Reporting tools for analyzing the data and to make tactical decision for the better performance of the organization, and more over to provide quick and fast access to end user request. This study will review data warehouse environment and architecture, business intelligence concepts, OLAP and the related theories involved on it. As well as the concept of data warehouse and OLAP, this study will also present comparative analysis of commonly used OLAP tools in Organization