Data Warehouse Strategy to Enable Performance Analysis

2000 ◽  
Vol 1719 (1) ◽  
pp. 175-183
Author(s):  
Daniel K. Papiernik ◽  
Dhruv Nanda ◽  
Robert O. Cassada ◽  
William H. Morris
Keyword(s):  
Decision Support ◽  
Performance Analysis ◽  
Data Warehouse ◽  
Data Mart ◽  
Strategic Investment ◽  
Business Decision ◽  
Data Marts ◽  
Future Direction ◽  
Virginia Department ◽  
Enterprise Data Warehouse

The Virginia Department of Transportation (VDOT) has engaged to implement an enterprise data warehouse as part of a strategic investment in its information technology (IT) infrastructure. Data warehousing provides an information architecture that serves as the enterprisewide source of data for performance analysis and organizational reporting. To assist VDOT in achieving its strategic outcome area objectives, a programming and scheduling (P&S) data mart is being developed to track preconstruction project activities. This data mart and subsequent data marts function as departmental decision support platforms, enabling VDOT’s operating divisions to perform their own enhanced analytical processing, visualization, and data mining for more informed business decision capabilities. Presented is a case study based on the enterprise data warehouse and P&S data mart being developed and implemented for VDOT by TransCore. Explicitly described is how one VDOT division, Programming and Scheduling, will benefit by investing in IT to achieve its strategic goals. The design approach, methodology, and implementation procedure for the P&S decision support data mart are detailed. The methodology for capturing the performance measures that have been defined by the P&S division in the context of its strategic outcome areas is highlighted. Recommended future direction and the technologies that the agency should adopt to continue to maximize their IT investment are outlined.

scholarly journals Importance of Data Mart for Data Warehouse

2021 ◽  
pp. 65-68
Author(s):  
Hoemra N. Halvadi Assistant Professor
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Market Share ◽  
Data Science ◽  
Data Repository ◽  
Data Mart ◽  
Data Marts ◽  
Conceptual Data ◽  
Gathering Data ◽  
Significant Data

In today’s world there are large amount of significant data to counter highly force races, extends market share and improve profitability. for that they required the information in a such way that can be a subject oriented, combined, non-volatile and time-variant. It a conceptual data for data repository of data for gathering data from various sources and merge by the whole enterprise. The data mart is newly progress area of data Science which is to be used to important deployment of decision support ability with the fire answer on investment need by the pace of extant business. Basically, data mart is coming from the data warehouse to emerged the technology and getting data in faster way which is used to merge to create Data warehouse. In the paper we will take review about design and integration of data marts and various techniques used for merged data mart.

Data Warehousing

2008 ◽  
pp. 2749-2761
Author(s):  
Hugh J. Watson ◽  
Barbara H. Wixom ◽  
Dale L. Goodhue
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Top Management ◽  
Data Warehouses ◽  
Web Based ◽  
Good Data ◽  
Customer Services ◽  
Enterprise Data Warehouse

Data warehouses are helping resolve a major problem that has plagued decision support applications over the years — a lack of good data. Top management at 3M realized that the company had to move from being product-centric to being customer savvy. In response, 3M built a terabyte data warehouse (global enterprise data warehouse) that provides thousands of 3M employees with real-time access to accurate, global, detailed information. The data warehouse underlies new Web-based customer services that are dynamically generated based on warehouse information. There are useful lessons that were learned at 3M during their years of developing the data warehouse.

scholarly journals Datawarehouse Sebagai Alat Penunjang Pengambil Keputusan Bisnis dalam Analisis Penjualan: Studi Kasus pada Bagian Penjualan PT XYZ

2011 ◽  
Vol 2 (2) ◽  
pp. 835
Author(s):  
Sepsugiarto Sepsugiarto
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Object Oriented ◽  
Decision Support Tool ◽  
Daily Activities ◽  
Business Decision ◽  
Support Tool ◽  
Sales Data ◽  
Decision Making System ◽  
Object Oriented Approach

Datawarehouse as a business decision support tool for sales data analysis functions to assist the sales department in making daily decisions. PT XYZ often experiences problems in analyzing its sales data, such as can not identify the highest sales areas and the total highest number of sales. The purpose of this study is to analyze and design a decision support tool which is appropriate to the needs of the sales of PT.XYZ. The design of systems uses object-oriented approach while database design uses data warehouse method. Any result obtained from this study is a design a data warehouse that can be used to assist the sales department to create sales decisions everyday. The designed decision-making system based on data warehouse can help the sales department in their daily activities. 

scholarly journals Analysis and Design of Data Mart Decision Support Systems at PT Marlindo Tirta Nusantara

SinkrOn ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 133
Author(s):  
Budi Sudradjat
Keyword(s):  
Data Warehouse ◽  
Accurate Information ◽  
Data Mart ◽  
Analysis And Design ◽  
Data Marts ◽  
Operational Systems ◽  
Challenging Environment ◽  
Asset Data

The role of information technology in each company is very influential in providing complete and accurate information. With a data warehouse or data mart the company can utilize important asset data to present the information needed.. Shipping firms nowadays are in operation with extremely competitive and challenging environment. Vast volume of knowledge is generated from various operational systems and these are used for determination of several business issues that needed imperative handling. The results obtained from this study are data marts that simplify and accelerate the provision of data and information to support decision making, so as to provide a basis for developing DSS and EIS applications. Conclusions obtained by the data warehouse or data mart can provide complete, accurate and integrated information as a basis for consideration for the executive in making decisions so that decisions taken are based on real facts owned by the company.

scholarly journals Pembentukan Data Mart Menggunakan Metode Generalization

2016 ◽  
pp. 138
Author(s):  
I Gede Sugita Aryandana ◽  
I Made Sukarsa ◽  
Putu Wira Buana
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Single Subject ◽  
Data Mart ◽  
History Data ◽  
Analysis Process ◽  
Data Marts ◽  
Analyze Data

Technology today causing the data needs of an agency or company to process the data or analyze data quickly, dense and higher. Companies or institutions want the data analysis process can save time as much as possible. The data warehouse is a data analysis technology that is useful to resolve the issue. The data warehouse is a repository of data that is useful to accommodate all the history data held by agencies or companies. Data marts are small part of the data warehouse. Data mart is focused on a single subject. This study uses a generalization method to perform the process of establishing a data mart. Generalization is a useful method to reduce or narrow the differences in the data based Subclass. Subclass were integrated into a Superclass useful to collect some data from the Subclass. Subclass is the data that is more descriptive. Superclass is more general in nature of data. The result obtained is a collection of some Subclass predetermined or selected later formed a Superclass useful to accommodate the resources of the Subclass.

Statistical Sampling to Instantiate Materialized View Selection Problems in Data Warehouses

2008 ◽  
pp. 2201-2225
Author(s):  
Mesbah U. Ahmed ◽  
Vikas Agrawal ◽  
Udayan Nandkeolyar ◽  
P. S. Sundararaghavan
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Business Decision ◽  
Time Dynamic ◽  
View Selection ◽  
Materialized View ◽  
Novel Approach ◽  
Maintenance Time ◽  
Materialized View Selection ◽  
The Right

In any online decision support system, the backbone is a data warehouse. In order to facilitate rapid response to complex business decision support queries, it is a common practice to materialize an appropriate set of the views at the data warehouse. However, it typically requires the solution of the Materialized View Selection (MVS) problem to select the right set of views to materialize in order to achieve a certain level of service given a limited amount of resource such as materialization time, storage space, or view maintenance time. Dynamic changes in the source data and the end users requirement necessitate rapid and repetitive instantiation and solution of the MVS problem. In an online decision support context, time is of the essence in finding acceptable solutions to this problem. In this chapter, we have used a novel approach to instantiate and solve four versions of the MVS problem using three sampling techniques and two databases. We compared these solutions with the optimal solutions corresponding to the actual problems. In our experimentation, we found that the sampling approach resulted in substantial savings in time while producing good solutions.

Data Warehousing

2011 ◽  
pp. 202-216 ◽  
Author(s):  
Hugh J. Watson ◽  
Barbara H. Wixom ◽  
Dale L. Goodhue
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Data Warehouse ◽  
Data Warehousing ◽  
Top Management ◽  
Data Warehouses ◽  
Web Based ◽  
Good Data ◽  
Customer Services ◽  
Enterprise Data Warehouse

Data warehouses are helping resolve a major problem that has plagued decision support applications over the years — a lack of good data. Top management at 3M realized that the company had to move from being product-centric to being customer savvy. In response, 3M built a terabyte data warehouse (global enterprise data warehouse) that provides thousands of 3M employees with real-time access to accurate, global, detailed information. The data warehouse underlies new Web-based customer services that are dynamically generated based on warehouse information. There are useful lessons that were learned at 3M during their years of developing the data warehouse.

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