ADM 1370 Study Guide - Final Guide: Data Management, Data Quality, Data Redundancy

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10 Jul 2014

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Information: data converted into useful and meaningful context. Analytical data: raw data converted to information (graphs, charts, statistics, etc. ) Data management: structured way to process data into information. Data sources & databases data storage data analysis results solutions. Challenges: out of date data, volume of data increasing. Data principles: principle of diminishing data value, principle of 90/90 data use, principle of data in context. Record: group of related fields: field: group of letters/numbers. Byte: group of bits that represent a character. Data redundancy: duplicate data stored in multiple places. Data inconsistency: same attribute with different values. Poor security: many can access and change company data. Many data sources: web and e-commerce applications, pos terminals, crm, supply chain, and enterprise planning applications. Entity: what the information pertains to (row in table) Relational dbms: data in tables based on a common property represented in 2d chart. Foreign key: primary key of another table connecting the tables.

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CASE STUDY

It used to be that epidemiologists at the CDC only knew that you had the flu when you visited your doctor and the doc- tor then reported that information to other agencies. By the time this information made its way to the CDC or state health departments, it was often several days old, which meant that the medical community was always playing catch-up. And of course, your doctor was only aware of your illness if you showed up at his office, not when you decided to tough it out at home. Nowadays, however, before you ever go to the doc- tor, you are likely to search the Internet for âflu symptoms,â tweet your suddenly declining health status, or Instagram yourself lying in bed. Study the case below, which shows how the Internet and social media have impacted the field of epi- demiology, and then answer the questions that follow.

Google Used to Predict Influenza Outbreaksâ2008

In 2008, Google launched Google Flu Trends, an applica- tion that compiles aggregated data from key word searches

for clinical terms, such as thermometer, chest congestion, muscle aches, or flu symptoms. Google reports the data on a website, which then provides an early-warning system for the locations of new flu outbreaks. Because the data are collected from searches performed each day, trends in flu symptoms become apparent much more quickly than when they are based on data reported during office vis- its or in lab reports from physicians around the country. When the CDC compared actual cases over the course of a year with Googleâs findings, the data from the two sources matched.

Initially, Google was only compiling information about flu trends in the United States and Canada. But after the H1N1 virus appeared in Mexico in 2009, the CDC asked Google to go back and look at Internet searches conducted by people in Mexico during that time. Evaluation of the data indicated that Google detected an uptick several days before the CDC did (Figure 23.1).

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Influenza activity

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Figure 23.1 2009 Inflenza activity (a) Data from the Centers for Disease Control, based on physical examination by health care providers, shows a peak in influenza cases in late April. (b) Google âFlu Trendsâ shows an influenza peak in February.

No. of cases

Case Study Exercise 23 Morbidity and Mortality Weekly Report 199

Epidemic tracking based on Internet searches reflects what is called collective intelligence. It works because indi- viduals using their personal computers tend to search for terms related to their immediate needs and intentions, and they generally do this before presenting in a doctorâs office or emergency room. The methodology used in the Google search was published in the prestigious science journal Nature, and another independent study has been published about a similar search analysis conducted by Yahoo, showing that it was effective in predicting flu trends.

Some people worry that data collected from Internet searches may compromise individualsâ privacy. However,

Google maintains that Flu Trends cannot be used to iden- tify individual users because the data are anonymous and are aggregated before being presented. Another potential drawback is that this data collection method is less likely to be useful for tracking epidemics in societies having a low percentage of computer ownership, namely, developing countries. However, considering the high stakes involved in identifying an epidemic quickly, Internet search term analy- sis holds great promise for public health. And, unlike most health innovations, itâs free!

3. How might data collected in this way not be representative of a particular population?

4. Explain why the two graphs in Figure 23.1 do not show influenza activity peaking at the same time.

6. In 2013, Google Flu Trends vastly overestimated the number of cases of the flu in the United States. One explanation for this was that there was a greater than usual number of media reports about influenza early in the flu season. Based on your knowledge of how Flu Trends works, explain why Google missed the mark in 2013

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Ollie Mace has recently been appointed controller of S. Dilley & Company, a family-owned manufacturing firm founded 28 years ago. The firm manufactures automotive parts. Its four major operating divisions are heat treating, extruding, small parts stamping, and machining. Last year's sales from each division ranged from $150,000 to over $3,000,000. Each division is physically and managerially independent, except for the constant surveillance of S. Dilley, the firm's founder.

The AIS for each division has evolved according to its needs and the abilities of its accounting staff. Mace is the first controller in the firm's history to have responsibility for overall financial management. Dilley expects to retire in a few years and wants Mace to improve the AIS before he retires so that it will be easier to monitor performance in each division. Mace decides to redesign the financial reporting system to include the following features:

1. It should give managers uniform, timely, and accurate reports of business activity. Monthly reports should be uniform across divisions and be completed by the fifth of the following month to provide enough time to take corrective actions to affect the next month's performance. Company-wide financial reports should be available at the same time.

2. Reports should provide a basis for measuring the return on investment for each division. Thus, in addition to revenue and expense accounts, reports should show assets assigned to each division.

3. The system should generate meaningful budget data for planning and decision making purposes. Budgets should reflect managerial responsibility and show costs for major product groups.

Mace believes that a new chart of accounts is required to accomplish these goals. He wants to divide asset accounts into six major categories, such as current assets and plant and equipment. He does not foresee a need for more than 10 control accounts within each of these categories. From his observations to date, 100 subsidiary accounts are more than adequate for each control account.

No division now has more than five major product groups. Mace foresees a maximum of six cost centers within any product group, including both the operating and non-operating groups. He views general divisional costs as a non-revenue- producing product group. Altogether, Mace estimates that approximately 44 expense accounts plus 12 specific variance accounts would be adequate.

Required:

The question aks to Design a chart of accounts for S. Dilley & Company. Explain how you structured the chart of accounts to meet the company's needs and operating characteristics. Keep total account code length to a minimum, while still satisfying all of Mace's desires.

Keeping the Customers Satisfied

Shelly Sanders has just been promoted to supervisor of the home furnishing department of the large department store where she works. The store manager made it clear to her that her first challenge was to turn around the poor performance of the department, particularly in sales, which were below budget, and customer satisfaction. The department receives more complaints than any other in the store (although it rates about average in the chain).

Dissatisfied customers contribute to a poor reputation in the community, which translates into poor sales. Shelly decides she needs to learn about the departmentâs current customers and find out how they feel about purchasing from it. Her first move is to check the departmentâs information on repeat business. There is none.

Next, she hunts out the departmentâs customer suggestions and complaints records to analyse and finds a notebook of scrawled customer comments and queries- all negative. Wondering how valid the information is, but realising she probably wonât find anything better, she draws up a check sheet to break down complaints and suggestions by frequency and type. Then she transfers the data onto Pareto charts and finds that the most common cause of customer dissatisfaction is problems with the delivery of furniture customers have ordered.

On investigation, she finds that, when furniture is ordered, the manufacturer quotes a lead time, which sales staff pass on to their customers. The manufacturer is often late with delivery. In turn, manufacturers often blame their suppliers for late delivery of raw materials,

However, because the store has no tracking system, the first the staff in the furnishings department hear of a problem is when customers ring up to complain that their order is overdue and ask when they can expect delivery. The staff then have to search through back orders, check with suppliers to find out the new delivery date and ring the customer back. This is time consuming and creates ill will between customers and the store. Itâs also difficult for the staff to find the time to track down late orders, since their priority is meant to be serving customers and ensuring the display area is well presented.

(Reference: Cole,K (2010).Management: theory and practice)

Based on this case study, assume you are a customer service staff and answer the following questions:

Outline the concept of internal and external customers; provide examples using this case study.

List the internal and external customerâs needs of the home furnishing department.

Outline how you will identify the customerâs needs?

Identify the causes for the customer service problem in the home furnishing department.

Provide suggestions to improve the customer service at home furnishing department.

âQuality customer service adds valueâ. Using this case study as an example, discuss this statement.

Develop policies and procedures to avoid any delay in delivering furniture to customers.

Explain how you will ensure customerâs requirements are met? Provide a strategy.

What lessons can you learn from this case study?

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ADM 1370 Study Guide - Final Guide: Data Management, Data Quality, Data Redundancy

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