The Use of Data Warehouse in The Decision Making Process
It is obvious that there is no organization running without data. The data can be viewed as tangible assets of an organization just as any physical asset. So, they need to be stored and made available to those who need them when they need them. However, the data by themselves are useless. So, they must be put together to produce useful information. In turn, information becomes the basis for relational decision making. To facilitate the decision-making process, a new development of database systems was developed called "data warehouse".
The data warehouse can be generally described as a decision-support tool that collects its data from operational databases and various external sources, transforms them into information and making that information available to decision-makers (top managers) in a consolidated and consistent manner. (2:64)(4:82)
BACKGROUND
The data warehouse is not more than a database but separated from other databases like the operational database distributed database and text database. When did management start to utilize this powerful tool and why they seek to use it.
The data warehouse has been developed at the beginning of 1980s. However, it was optimize to transform non-organized and lightly summarized data from the operational database into analytical tool that supports intelligent decision-making. (6:19)
The term DSS (Decision support system) database is used interchangeably with the data warehouse. On the other hand, other names for the operational database are transactional database and production database.
WHAT IS A DATA WAREHOUSE?
The data warehouse can be very simply defined as an integrated, subject-oriented, tim...
... middle of paper ...
...s tool so that their top management could carry out the decision-making process with more confidence to achieve the desired goals.
REFERENCES
1. Ballou, Donald P., and Giri Kumar Tayi. "Enhancing Data
Quality in Data Warehouse Environments." Communications of the ACM Jan.1999: 73-78.
2. Gould, Lawrence. "What You Need to Know About Data
Warehousing." Automotive Manufacturing & Production Jun. 1998: 64-67.
3. Scheuerman, Michael. "Planing to Build A Data
Warehouse." Credit Union Magazine Dec. 1998: 32-33.
4. Stephenson, Miles, and Michael McCathren. "Digital
Decisions." Restaurant Hospitality Feb. 1999: 82-84.
5. Taylor, Rick. "Knowledge Is Power." Credit Union
Management Jan. 1999: 39, 43.
6. Teresko, John. "Information Rich, Knowledge Poor."
Industry Week 1 Feb,1999: 19-24.
Smith, W., & Jewett, D. (2009). Tableau software and teradata database the visual approach to the active data warehouse. In Retrieved from http://www.tableausoftware.com/learn/whitepapers
To give alternative courses of action and to recommend the best alternative to improve the company’s operations.
Each plant comprises a number of small; multi-skilled; flexible; collaborative and self-managed teams instead of functional departments with specialised functions (e.g. legal, finance or human resources etc as in a conventional system). These teams have the decision-making power over all plant-specific business functions including capital allocation, expenditures, strategic planning and plant design. This bottom-up decision making process emphasises the trust the company places in its employees and is very effective in decentralizing the power base, consequently, involving every employee in being responsible for the performance of the company not just the CEO.
When I am teaching in the future, I am going to explain “data informed decision making” in three ways: what “data informed decision making” is , ways it can be used and ways it improves students.
The most common purpose for (BI) systems is to aid in the decision making process. BI systems collect data, store the gathered information in data warehouses, analyze the data and then present the data in easy to understand applications for the decision making process. The following studies research the role that BI systems play in the decision making process.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
The last decade can be marked as a period of significant changes in the business world. Being accustomed to utilize computers as a powerful tool with its office applications such as Microsoft Word and Excel. In the 1990s office workers first faced the opportunity to share information using the Internet (McNurlin, 2009). However, the situation became even more different with the transition to the third millennium. With a further development of information technologies, the majority of big enterprises had to reconstitute their business processes and to make the transition to the Internet economy. Enterprise resource planning (ERP), supply-chain management (SCM), customer relationship management (CRM) software and the variety of other information systems became essential components of the new economy. It can be expected, that all these complex solutions were designed to bring great benefits for different sides of the corporate activity, in particular, decisions made by top-managers are expected to become nearer to the ideal, customer service is to be improved and collaboration more prolific. Nevertheless, to ensure the desired results it should be taken into account that the key concept of these reorganizations is an information or a data, dealing with which can be a serious issue, and wide utilizing of the data warehouses in contemporary organizations confirms this fact.
Decision making refers to the process of finding and selecting options according to the priorities and values of the person making the decision. Since there are many choices involved, it is important to identify as many options as possible so as to pick the option that best fits a company’s target, goals, values and vision. Due to the integral role of decision making in company growth and financial progress, many firms such as Amazon.com and EBay are pumping in huge investments in business intelligence systems, which are made up of certain technological tools and technological applications that are created for the purpose of facilitating improved decision making process in business. In this paper, I take a critical look at Decision Support Systems and how they affect organizational Decision making.
The objective of this paper is to have a critical assessment of what are Decision Support Systems, particularly in the Organizational context, historical genesis of these systems and the latest trends in this sub field of MIS. Along with the above mentioned objectives, we have tried to explore, by way of examples, the relevance and importance of DSS in large, complicated decision making settings. We also will attempt to visualize, how DSS’s may evolve 20 years from now in a vastly connected world and which type of problems it could help solve efficiently
Databases are becoming as common in the workplace as the stapler. Businesses use databases to keep track of payroll, vacations, inventory, and a multitude of other taske of which are to vast to mention here. Basically businesses use databases anytime a large amount of data must be stored in such a manor that it can easily be searched, categorized and recalled in different means that can be easily read and understood by the end user. Databases are used extensively where I work. In fact, since Hyperion Solutions is a database and financial intelligence software developing company we produce one. To keep the material within scope I shall narrow the use of databases down to what we use just in the Orlando office of Hyperion Solutions alone.
Managers should be ready to teach the importance of decision-making skills and reinforcing organizational policy. Avoiding hasty, careless decisions, which can have devastating results on the manager's unit or the entire organization. Decisions made with forethought, using the many managerial tools available will lead to better and more profitable operatio...
“Decision making is a process of first diverging to explore the possibilities and then converging on a solution(s). The Latin root of the word decision means "to cut off from all alternatives". This is what you should do when you decide.” (Kotelnikov, 2008). In fact, the decision making process helps reduce doubt and uncertainty about alternative choices to allow individual to choose the best reasonable choice. In addition, the decision making process can make the difference between a successful and an unsuccessful organization. Consequently, management tries to use the best techniques and tools possible to make the best decision. Nowadays, most organizations seem to think that they have the most effective and efficient decision making process. So what are the different styles of decision making processes have organizations implemented? In order to answer this question, the team members will investigate and observe the decision-making processes most prevalent in their organization. As a result, these papers will first compare and contrast the problem identification and formulation styles in the team members’ organizations. Then the most favorable aspects of each style will be discussed to describe a process by which a problem can be identified and described to stakeholders in a manner that is sensitive to their perspective.
Management will continue to encounter new challenges that require problem solving and decision-making strategies. Some problems may be easily resolved while others could take much longer depending on the complexity of the problem. In order for management to make effective decisions and achieve success for their businesses, the decision makers need to have adequate knowledge of the situation, critical thinking and excellent communication skills, and a sophisticated approach for tackling problems. Every business should have a systematic approach for solving problems and making decisions. Without one, decision making would be insufficient and businesses would be unproductive.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
For the past couple of decades the majority of businesses have wanted to construct a data-driven organization or company. Furthermore, companies around the world are considering harnessing data as a basis of competitive advantage over other companies. As a result, business intelligence and data science use are popular in many organizations today. The increase in adoption of these data systems is in response to the heavy rise in communications abilities the world over. Which, in turn ,has increased the need for data products. Indeed, the Data Scientist profession is emerging to be one of the better-paying professions due to the urgent need of their labor. This paper is going to discuss what business intelligence is all about and explain data science that is usually confused to be similar to business intelligence. I will tackle a brief overview of data scientists and their role in organizations.