Improving The Data Warehouse Technology Essay

Real-time data warehousing creates some special issues that need to be solved by data warehouse management. These can create issues because of the extensive technicality that is involved for not only planning the system, but also managing problems as they arise. Two aspects of the BI system that need to be organized in order to elude any technical problems are: the architecture design and query workload balancing.

Learning Outcome 6.5: Define a data warehouse, and provide a few reasons it can make a manager more effective.

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.

In order to reach his goal, there are many issues that need to be addressed. The first issue is that in order to ensure that the data in the data warehouse is correct, there needs to be strong data governance by all users. The 2nd concern is that users of the current systems will not

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

One crucial thing that organizations need to consider in today’s unstructured data world is to successfully integrate data warehouses. For this, the companies need to re-consider their enterprise data architecture and classify the governance strategy that can be talented through such efforts. There lies a need for data managers

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.

All projects are working towards a Data Model that gets approved by engineering and business in the next few sprints. The goal is to provide self service reporting capabilities for the data relevant to respective business function.

"A data warehouse is a subject oriented, integrated, time variant, non-volatile collection of data in support of management 's decision making process". Source

Data warehousing is defined as the design and operation of processes and tools to manage and deliver complete, timely, accurate, and understandable data for decision making. It includes all the activities that make it possible for an organization to create, manage, and maintain a data warehouse or data mart. Data warehousing majorly deals with managing the development, the implementation, and the operation of a data warehouse or data store. It includes data management, data acquisition, data archiving, data cleansing, storage management, data integration, data distribution, security management operational reporting, analytical reporting, backup and recovery planning,

Moreover, its relation to the data warehouse turns the first pattern of development on its head. Here multiple data marts are parents to the data warehouse, which evolves from them organically. The third pattern of development attempts to synthesize and remove the conflict inherent in the first two. Here data marts are seen as developing in parallel with the data warehouse. Both develop from islands of information, but data marts don’t have to wait for the data warehouse to be implemented. It is enough that each data mart is guided by the enterprise data model developed for the data warehouse, and is developed in a manner consistent with this data model. Then the data marts can be finished quickly, and can be modified later when the enterprise data warehouse is finished. These three patterns of data mart development have in common a viewpoint that does not explicitly consider the role of user feedback in the development process. Each view assumes that the relationship between data warehouses and data marts is relatively static. The data mart is a subset of the data warehouse, or the data warehouse is an outgrowth of the data marts, or there is parallel development, with the data marts guided by the data warehouse data model, and ultimately superseded by the data warehouse, which provides a final answer to the islands of information problem. Whatever view is taken, the role of users in the dynamics of data warehouse/data

According to Haertzen (2012), “Enterprise Data Warehousing (EDW) is a process for collecting, storing, and delivering decision support data for an entire enterprise or business unit”. A data warehouse is not operational data. It contains a copy of operational and other data, rather than being a source of original data. This data is often obtained from multiple data sources and is useful for strategic decision-making. Its purpose is not just to maintain historical data. A data warehouse contains specific data that has been gathered for analytics and reporting. Enterprise Data Warehousing includes people, processes, and technologies to achieve the goal of providing decision support. The Data Warehouse contains intelligent data collections which are modeled to support the reporting and analysis needs of the Decision Support function of the organization. So, the main key goals of data warehouse are: Make fact based decisions, Make timely decisions, Make profitable decisions that reduce costs and increase revenue. These decisions can support a number of stakeholders include: Customers, Employees, Shareholders, Suppliers,

Summary: The text book I have chosen is “The Data Warehouse Toolkit” third edition, written by Ralph Kimball and Margy Ross. This book mainly involves on techniques to develop the business in real-time. As the authors had a lot of experience because of their work from 1980’s, they have seen both the growth and failures of the companies in the market. Chapters in this text book involves goals of data warehousing which include Data staging area, data presentation, data access tools. Kimball modeling techniques involves gathering business requirements and data realities, business processes, different table techniques. Case studies in retail sales are explained in this text book, four step dimensional design process which includes the design process with the help of different dimensions and facts. In order management chapter it deals with the business processes that to be implemented in data warehouses as they supply core business performances metrics and finally provide the real time warehousing requirements. Customer relationship management involves in improving the customer relation with the company or product, understanding the needs of customer and providing high level service is the goal of this chapter. In accounting, we deal with model of general ledger information for the data warehouse, it describe the years and dates at which things to be happened and show different dimensional models which helps to combine the data from

A Data Warehouse is a database-centric system of decision support technologies used to consolidate business data from many disparate sources for use in reporting and analysis (Data Warehouse). Data Warehouses and Data Warehouse systems are primary used to server executives, senior management, and business analysts with accurate, consolidated information from various internal and external sources to aid in the process of making complex business decisions (Data Warehouse Process).

Before discussing the current data warehouse architecture in place at ICICI Bank, issues associated with it, especially due to immense data 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 with 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 one of the major issues that bank faced was with their current