Quiz 3 busi 610

Data management is vital to any business as this is a key tool to an organisations business improvement, as you can refer back to data, and compare them against benchmarks. Analysing data can provide evidence for possible future structure such as identify trends, as well as indicate where improvements can be made. However there are strict procedures to be followed when collecting and storing data.

The data warehouse contains all the information that both the chain managers as well personnel can access. This information helps them see which products are selling, how much, where more important points of sales are, which are needed in inventory and which items needs to be checked for quality etc. Similarly these databases also contain solid information about consumers such as what is the ratio of repeat customers, what age group needs to be targeted for advertising, which new group is emerging and how to stay in touch with consumers about new products and sales.

Any path that if delayed will delay the completion of the entire project is known as a:

This data is collected and organized in order to process orders and maintain good customer service. The logical view of data would allow a knowledge worker to arrange and access information based on the needs of the business separating it from the physical view of how information is arranged and stored. The ability to do this allows for an employee to create detailed reports in order to determine information such as customer information and their order numbers and dates. This is imperative for a company like Comcast who has over 27 million customers in order to have a system to keep important data to analyze. Using a data warehouse allows them to gather from several databases and then the company can use the information to determine for example how many units of voice products are sold to create the necessary business intelligence to make future decisions and remain

* Describe the role of databases and database management systems in managing organizational data and information.

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.

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.

In this paper the writer will seek to respond to the questions designated for both scenarios. This paper will list typical fields for each type of data. Provide an example of two relationships that you need to track. This paper will also answer the questions of: Do you need a database system? If not, can Excel® handle the data and the output? What are the advantages and disadvantages? Would you use a personal database or an enterprise database? Explain your answer. Would a decision support system (DSS) be helpful? Explain your answer.

The data warehouse comes ready for use, but an organization has to get prepared to use it. The main factor is data warehouse usage. A data warehouse can be used for decision making for management staff.

Service customer interaction can also be between the customer and technology. The customer may be required to interact with technology in a technology based service encounter to experience the service on offer. In such a service encounter the customer is in control and might be willing to participate but not possessing the necessary skills, knowledge and abilities to operate the machinery for example for a customer to purchase goods online or book a hotel reservations using the company’s website requires the customer to be computer literate hence when not and fails to place an order the service on offer would be deemed by the specific customers as of poor quality. Lovelock and Gummesson (2004) suggest that the service offer and encounter are less variable when machine-intensive technologies are utilised in service encounters since variability of the service encounter posses a great threat to the quality of service on offer. In a case where the customer successfully interacts with the technology in the service

Enterprise view of data is required to maximize the efficiency of the organization as a whole (csu, n.d.). The sharing of data and maintaining the transparency is achieved by integrating the data within the system. Integrating the data is a complex task as there is always certain information that is sensitive and it is not meant to be shared with each and every department or individual working within or outside the organization. Making enterprise data strategy demands administrators to understand that the data is critical and is an asset to the organization. Wayne Eckerson describes enterprise view of data as the enterprise data strategy built by the organization to have a successful growth rate plan. He also points out that only one in 10 organizations have such sort of strategy as most of them don’t put together the required soft skills that are needed to manage change incurred by the strategy and the investment for data management techniques and tools to certify the delivery of high quality and consistent data supported with business initiatives and strategies (Eckerson, 2011). Most of the organizations face a lot of difficulties in maintaining the data quality. The cost of data quality incurred is much higher and it sometimes contributes to forty percent of the problems related to IT in a corporation this problem occurs because of the inaccurate data (steria, 2012). One major issue when creating an enterprise view

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

Organizational data is increasingly prevalent due to the ease of collecting data from several sources. Because so much data is now available and gathered, organizations must set a data management system to clean then consolidate the data to give users clear insight into the organization’s behavior. To implement such a system requires the collaboration of both the business manager and the business’s IT organization. The IT organization must have a clear understanding of the data standards and data model that the manager requests to correctly implement it. Furthermore, the IT organization must determine how the system should be designed to match the data needs of the organization. Alongside the IT team, the business manager must address several issues concerning the system, including how the data should be organized, collected, and distributed. The business manager is also responsible for ensuring the quality of the data gathered and how to address any technical issues that arise, as well as oversee the support for any transitions to updated software, so that data is not lost.

In early 80s business world more concerned about the large amount of data that is emerging from their customer world and worried about how to store that amount of data, during that time old business instruments took a large amount of time to execute the business instead of running it and it is also costly, time consuming and risky to deals with that much big amount of data (Inmon, 2005). The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy introduced the term “Business data warehouse”. In 1986, Red Brick Systems founded by Ralph Kimball began to do research on improving data access (Hammergren, 2005). In 1990s executives become less concerned with the day-to-day business operations and overall concerned with overall business functions and worried about large amount of data. Due to the improvements and magnification in the information systems where large amount of data needs to be saved and retrieved, data warehousing was additionally enhanced and advanced to cope up with such immensely large amounts of data (Kelly, 2009).

The successful company's activities (management) are related to collection, storage, and analysis and interpreting data. The purpose of data collection is to get hold of information and to continuously record and to make decisions on key issues. Data collection is the storage of data and prepare for the future process. Moreover, every piece information is a valuable resource in business, such as cost of production, share prices and exchange rates, company reports and market research. The information (data) is mostly collected about their own organisations to analyse themselves and given to other companies to analyse it. Also, the up to date and accurate information (progress of the business) helps to managers and employees involved in making decisions. There are two types of data that usually used in businesses, the quantitative and qualitative. The quantitative data is a business or financial analysis, performance that aims to understand behaviour by using complex mathematical and statistical modelling, measurement, research and display the results, using the charts, histograms, tables and graphs. Quantitative analysis can be done for a variety of reasons, such as the measurement, performance estimation and calculation of the number of financial instruments (earnings per share, popular product, cash flow and costs). Moreover, surveys and the use of government publications are common methods of collecting quantitative data. For example. Tesco has the