The Data Warehouse Modernization Tipping Point

An active data warehousing, or ADW, is a data warehouse implementation that supports near-time or near-real-time decision making. It is featured by event-driven actions that are triggered by a continuous stream of queries that are generated by people or applications regarding an organization or company against a broad, deep granular set of enterprise data. Continental uses active data warehousing to keep track of their company’s daily progress and performance. Continental’s management team holds an operations meeting every morning to discuss how their

One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

What information is accessible? The data warehouse offers possibilities to define what’s offered through metadata, published information, and parameterized analytic applications. Is the data of high value? Data warehouse patrons assume reliability and value. The presentation area’s data must be correctly organized and harmless to consume. In terms of design, the presentation area would be planned for the luxury of its consumers. It must be planned based on the preferences articulated by the data warehouse diners, not the staging supervisors. Service is also serious in the data warehouse. Data must be transported, as ordered, promptly in a technique that is pleasing to the business handler or reporting/delivery application designer. Lastly, cost is a feature for the data

A data warehouse is a large databased organized for reporting. It preserves history, integrates data from multiple sources, and is typically not updated in real time. The key components of data warehousing is the ability to access data of the operational systems, data staging area, data presentation area, and data access tools (HIMSS, 2009). The goal of the data warehouse platform is to improve the decision-making for clinical, financial, and operational purposes.

CHAPTER 2: DATA WAREHOUSING Objectives: After completing this chapter, you should be able to: 1. Understand the basic definitions and concepts of data warehouses 2. Understand data warehousing architectures 3. Describe the processes used in developing and managing data warehouses 4. Explain data warehousing operations 5. Explain the role of data warehouses in decision support 6. Explain data integration and the extraction, transformation, and load (ETL) processes 7. Describe real-time (active) data warehousing 8. Understand data warehouse administration and security issues CHAPTER OVERVIEW Data warehousing is at the foundation of most BI. This is the data warehousing chapter of the book. Later chapters will use it as they discuss DW

In the future, the development is more focused on Big Data where the requirement of availability of information increase directly with the complexities of decision making increase, thus the requirement of data infrastructure need larger and more analytically to align with knowledge and decision-supporting technologies (Hosack et al., 2012). Increasing information available to KMDSS through data warehouse capabilities may be useful to the several industries. DSS has been on the forefront not only of new technologies, but of new ways to address existing business problems and processes. The nature of DSS is to continuously improve the decision-making processes that, in turn, improve the efficiencies of

Data warehouses are targeted for decision supporting. Old, summarized and consolidated data is very much important than detailed as well as individual records. As data warehouses store consolidated data, possibly from several operational databases, for perhaps a very long time, they tend to be in orders of magnitude much greater than operational databases; enterprise data warehouses are projected to be hundreds of gigabytes to terabytes in size.

SCM and CRM systems as part of one overall Enterprise Resource Planning (ERP) system will take BLDR to a technology sophistication level that would more effectively service their national footprint. However today, rather than one ERP system, BLDR utilizes multiple operational systems due to multiple historical mergers and acquisitions. Because of BLDR’s multiple systems, information concerning purchasing, sales, inventory levels, and the like must be brought together for companywide reporting and analysis. A data warehouse is used for this purpose. Haas and Cumming (2013) explain that “a data warehouse is a logical collection of information gathered from many different operational databases used to create business intelligence that

Article, www.coppereye.com/data_warehousing, states the aspects of return on investment of data warehouse is "the architectures have typically placed a premium on storing large volumes of data, and being able to execute queries very rapidly against this data." Real-time, with current information, is what is available with all the new data warehouse technology. Also, the article states, "it is common practice that loading the data is done overnight, and in many cases taken much longer with the growing success of data warehouse projects." Another aspect is, "business owners are no longer willing to accept reporting on last week's or even yesterday's performance, but want immediate access to data and reports about what is happening in the business to make ever more time-critical decisions.":

Demand-driven approach which is also called as requirements-driven, starts from identifying the information requirements of business users. This implies a top-down technique. For this method high level of top management involvement is required and the focus is on their needs to align DW with corporate strategy and business objectives. Requirements are used to build a conceptual

As your business evolves, the data warehouse may not meet the requirements of your organization. Organizations have information needs that are not completely served by a data warehouse. The needs are driven as much by the maturity of the data use in business as they are by new technology.

The data warehouse DBMS market is going through a transformation due to the rise of "big data" and logical data Warehouses. Surprisingly, many establishments entered the data warehouse market in 2012 for the first time, swelling demand for professional services and causing vital changes in vendors' positions.

Setting Priorities: Development of data warehouse should be an evolution. First version of data warehouse should contain the data for few of the areas, and it should support the application and all the users those who are accessing

Data are a vital organizational resource that needs to be managed like other important business assets. Today’s business enterprises cannot survive or succeed without quality data about their internal operations and external environment. This growth drives corporations to analyze every bit of information that is extracted from huge data warehouses for competitive advantage. This has turned the data storage and management function into a key strategic role of information age.

Before discussing the current data warehouse architecture in place at ICICI Bank, the issues and drawbacks associated with it due to immense growth and different modalities of data sources, it would be appropriate to have a quick look at the data warehouse history and architectural framework and how ICICI Bank’s data warehouse has evolved over the years. Back in 2008 ICICI Bank used Teradata and was dependent on Teradata for its data warehouse. Back in those days the size of the data warehouse was 3TB. Because of the dramatic growth in the amount of data, user population and the source stations coupled by cost of scaling and maintenance as well as system availability, posed a problem for the bank in using their legacy data warehouse solution. The bank felt that its legacy data warehouse solution posed scalability issues and the major issue that bank faced was with