chapter 4 data analysis and diagramming in various ... · data analysis and diagramming in various...

MIS 2000 * Data Analysis and Diagramming in Various Functions * Bob Travica ©

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Chapter 4

Data Analysis and Diagramming in Various Business Functions

This chapter expands on the topic of data analysis and diagramming. This skill will be applied to different functions in organizations, that is, to different organizational processes studied in this course. The ultimate goal of data analysis and diagramming is to design databases for one or more computer-based IS supporting a particular business process. A schema resulting from data analysis becomes a key part of design of an IS. We will look at some standard schemas as well as to some that present newer business trends. The chapter ends by looking at data quality standards.

Purchasing Business starts on the supply side. Companies purchase raw materials and half-products in order to produce a more complete or final product. It helps to think of a company from the general systems perspective, as discussed in the chapter on basic concepts. First, a company must take inputs from the environment. For a furniture factory, the input is physical materials like wood and glass; for a bank, the input is borrowed funds; and for a consulting company, the input is client business problems to solve. (Of course, all of these must take human resources and financial inputs as well.) There is a particular part of company that takes these inputs.

Figure 1. Schema for process of purchasing physical items


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Depending on the industry, this input part may be called differently, such as purchasing (as in the furniture company), acquisitions or contracting (in consulting firms), procurement, supplies, and so on. These inputs then move to the production part of the company to be processed into products for market. In data analysis of purchasing operations, one key entity is Purchased Item (Figure 1). An item is ordered from a supplier company via a Purchasing Order (PO), which is another key entity. PO is a concept and it translates into a business document (electronic, paper). This sort of schema is the basis for designing Transaction processing Systems (TPS) and Management Information Systems (MIS). These types of systems support supervisory and mid-level management, respectively. They are studied more latter in the course.

Production A company's supplies are transformed in production operations, whether in manufacturing or services. The production function is where the specific business is performed, and it may be complex. The production function includes several processes. One of these is scheduling. Scheduling must be done in any type of company, whether it is in the manufacturing or services sector. In data analysis of scheduling, Schedule is the key entity. It is a calendar-like document that shows who is doing what and when. Therefore, a schedule requires at least two other entities – Task and Worker. A schema for the scheduling process appears in Figure 2.

Figure 2. Schema for production scheduling process The schema in Figure 2, with some additions, is the basis for making databases that are the core of production TPS.


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Human Resources HR management supports an organization in its production core as well as throughout its operations. This is a broad area covering the entire cycle of employee-related issues. These include recruiting, training, pay, wellness, grievances, etc. The pay process is very important for employees, so it has to function smoothly. IS for pay management are a must in modern organizations. Sometime these are labeled with HRIS – Human Resource Information System. Note that accountants are also interested in the pay because it creates significant recurring costs for a company. So, pay figures feed into the accounting IS. For the pay process, the key entity refers to Pay. Figure 3 shows it as Salary (fixed hours) and Wage (variable number of hours). An employee gets just one kind of pay at a time. One can calculate either kind of the pay by using the appropriate rate(s) and the hours booked (regular and overtime). Note several interesting things about this diagram. There are multiple FKs both in table Salary and in table Wage. The attribute Amount in tables Salary and Wage is calculated by multiplying rates by hours. Relational databases support calculated attributes. Design of PKs for this schema is different than the usual key design. For example, SalaryID may take some specification of job and rank. PeriodID in table WorkHours may take some specification of time, such as year-month (e.g., 2014-9, 2014-10, etc.). Of course, at the practical level when a database is developed one can always use a default key, integer values that automatically increase when a new row is added in a table (in MS Access, this is called Auto Number). Table WorkHours may further be expanded down to days and hours, which is especially useful in the case of wages. As with old-fashioned punch clocks, daily hours of each employee are tracked for every month, a monthly total is used for calculating a wage.


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Figure 3. Schema for pay process

Accounting Similarly to HR, accounting is not a production process but a support process. Still, it is a chief lever for management control in any organization. Accounting tracks monetary value of all tangible things on the inflow (revenue) and outflow (cost) side. These two objects translate into revenue and cost entities or accounts (there can be many of each). In data analysis of accounting, one key entity is Account. There are many types of account. One division is receivables (revenues) vs. payables (costs). Each of these can be from different sources. As shown in Figure 4a, revenue accounts can be Budget, Sale, and Interest accounts. Costs can result from operations, purchasing, and employees pay, as depicted in Figure 4b. Note that these breakdowns are marked with the word “is” in the figures. An account receivable is a budget account, or sale account, or interest account, or some other.

Figure 4a. Schema (partial) for income accounting

Figure 4b. Schema (partial) for expense accounting


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As organizations operate on the daily basis, accounting data are continually created. Cost accounting is particularly dynamic. Particular events or transactions are what creates these data. Examples of transactions are a purchasing, the use of utilities, IS operations, utilization of other technologies, and a marketing activity. This continuous flow of data must be regularly recorded in accounting IS. Accounting IS (AIS) deploy the entity called General Ledger to record the continuous flow of accounting data. In the past, the general ledger was a large book, so accountants would perform “book keeping.” Today, this is all electronic. Figure 5 depicts a schema with general ledger and two previously mentioned types of accounts that the ledger feeds.

Figure 5. Schema for general ledger process

Marketing Marketing processes create and manage markets. If a supply department is on the back-end of an organization, a marketing department is on the front-end. A marketing department makes the output part of a company (even though it may be involved in production too). Marketing key entities have to do with customers and markets. To understand markets, marketers perform customer grouping or market segmentation. Market segmentation is based on defining ranges of customer expenditures (spend), for example as high, mid, and low. Segments could be defined for a particular product or across products. The goal is to compare the actual spend of every customer to these segments. This apparently helps to understand where a particular customer belongs and how many customers a company has in each market segment. Figure 6 shows these key entities implemented as tables Market Segment and Customer Spend.


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Figure 6. Schema for market segmentation

Table Customer Spend is in the center of schema shown in Figure 6. Its most important attribute is SpendAmount. This figure is calculated based on inputs from tables Purchase and Period. Within table Purchase, individual purchase transactions are calculated as the product of sale price and quantity. Table Period allows for manipulating period of time in order to get aggregate purchase figures per customer. Once the spend of a particular customer in a particular period is calculated, it is compared with the spend ranges that define market segments in table Market Segment. Then, the system fills the attribute (foreign key) MSegmentID in table Customer Spend. Customer ID in this table is the foreign key that references a particular customer in table Customer. If you feel that this schema is more complex than those discussed so far, you are right. It is so because IS for market segmentation belong to a more complex type called Decision Support Systems (DSS). Such systems are used in decision making of upper management or professionals called market analysts. DSS for market segmentation take inputs from less complex systems. Also, they can make some decisions on their own. One example is the mentioned automatic identification of a customer’s market segment.

Sales The output part of a company rests on a sales department, in addition to marketing. A schema for sales in a grocery store appears in Figure 7. Entity Customer is crucial. Another important entity is Sale. Note that this diagram is similar to the one discussed in Chapter 3. The main difference is that Figure 7 includes entity Sale that fits in the context of grocery store. In contrast, the sale


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process previously discussed involved the entity CustomerOrder. Other new entities capture promotions and the reward card. Notice also the tables resulting from association entities, such as SaleDetail.

Figure 7. Data diagram for sale process in grocery store Sales operations are typically supported by TPS. This TPS may also be called Point of Sales Terminal.

Electronic Commerce Electronic commerce (e-commerce) is about buying and selling via the Internet. This is a separate topic in this course. At this point, it is interesting to compare e-commerce with classical retail in “brick and mortar stores” discussed in the previous section (refer to Figure 7). Think about this: How does a merchant learn about customers? In the classical store, the merchant can meet with the customer and perhaps learn about the customer needs through conversation. Still, in stores with a larger customer turnout, the merchant usually learns just from the data collected in the sales TPS – sales details, total spend, reward cards, and promotions pushed to customers. Contrast this with the e-commerce situation depicted in Figure 8. Online customer is invisible to the merchant . This customer is identified by an Internet address, and he/she resides in a “cyber space.” The sales transactions also take place in that space created by computer networks and various IS. Sure enough, all the data in classical retail can still be collected and used for profiling the online customer. But there is more to it. The online merchant has some new sources of customer data at hand. As Figure 8 shows, the merchant can learn from searches performed by visitors to an online store. The search engine providing access to a product catalog saves the search terms; notice table CatalogSearch. These terms indicate customer interest.


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The online merchant can also learn about a customer’s wants from the store’s website pages that a customer visits (table PageVisiti in Figure 8). The time spent on page is also automatically tracked. Even more specifically, every click the customer makes on menu items can be recorded in a customer database. These items may be links leading to more details on products, or advertisements for particular products. These facts are captured in the attribute Screen Items Clicked. Yet another source of learning for an online merchant is overlapping interests between customers. For example, if one customer purchases products XY and the other customer purchases products YZ, these two overlap on product Y. The customer profiling system will automatically offer product X to the second customer, and product Z to the first customer. Figure 8 shows this matching with the table ComparableCustomer. Automatic customer matching is a way of extending customer profiling in the e-commerce world. It is also a way of promoting merchandise. This happens when you access an online store you visited in the past and you are greeted with offers of certain products. The management goal is to push cross-selling (increasing the range of things sold).

Figure 8. Profiling online customers E-commerce keeps expanding with the increasing acceptance of mobile devices. In 2014, one third of the world’s population of 7.2 billion has had access to the Internet, while the number of cell phone (mobile telephone) owners equaled 95% of the world’s population (note that some people have more than one phone). As more people communicate and shop electronically, this creates new customer data. Figure 9 shows some electronic traces created this way. Sources are the messages, “likes,” and conversations about branded products that people make and post in the cyberspace. These transpire at social media Websites (e.g., Facebook), microblogging sites (Twitter, tumblr, etc.), and blogs (Web pages written by individuals who wish to voice their opinions on various issues), to name a few. The development of new data sources created a new trend called Big Data. This trend is easy to understand in terms of three “v.” Big Data are big in volume, as they are created in large


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quantities. They are also big in variety of sources and data types; Figure 9 shows some. And these data come at a big velocity (speed). Put together, Big Data are large quantities of data that come fast and from various sources. Marketing can benefit from Big Data. Social media marketing, relationship marketing and similar new methods have already taken advantage of Big Data. But many other areas have also started to benefit from Big Data, including genetics research, medicine, farming, power grid management, traffic control, and personal analytics (for example, historical fitness data, monitoring current exercises, finance, romance, etc.).

Figure 9. Big Data sources used in marketing

Data Quality While appropriate modeling of business data is necessary for good business, it is essential that information systems provide quality outputs for informing the system users. Assuring this quality starts with input data, and extends to the data processing part of IS. Certainly, all organizational data, whether on the input or output side, must comply with certain quality standards. Here are the most important standards. They are lined up to create the acronyms ReCoN and ACT – Relevance, Consistency, Non-redundancy, Accuracy, Completeness, and Timeliness. The acronym may be memorable as it associates to operations from action movies – get a recon (reconnaissance, observation of a battlefield) in order to act. Relevance The relevance standard requires that data correspond to user needs. This standard is applied firsthand when an IS designed. Also, during system use, users should be served just with system outputs that address their area of work. For example, if a purchasing manager is presented with a marketing report, the relevance standard would be violated.


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Consistency The consistency standard requires that a piece of data appears in the same way throughout a system. For example, if a particular customer name is entered in a customer table with three parts (the last name, first name, and the middle name), then it must appears with all the three parts wherever it is used in the IS storage and outputs. If the middle part is dropped or abbreviated, this customer is likely to be treated as different persons. Non-redundancy That data should be non-redundant means that duplication of stored data should be avoided whenever possible. In relational databases this is accomplished by entering particular data just in a table that represents the related entity. For instance, a customer name, address and contact details are entered just in table Customer. If the same customer needs to be referenced in table Order, this should be done by making the Customer ID a foreign key in table Order, without repeating customer name and other attributes. Accuracy Accurate data reflect factual state of affairs. For example, factual expenditures and revenues entered should be entered in an accounting TPS rather than fixed numbers. Recent cases of corporate fraud demonstrate grave consequences that inaccurate accounting data can cause. Incorrect data may also be accidently entered into a system. A lack of accuracy on the output side is likely to occur. A warning against this violation of data quality is conveyed in the saying “garbage in – garbage out.” Completeness The completeness standard requires that all the data required for a task are indeed supplied. To meet this standard on the output side of a MIS, complete data must be entered previously into a TPS. Note that the data coverage should be right on the mark rather than “enriched” by arbitrary additions. Such additions may violate the relevance standard. Timeliness The timeliness standard requires that certain data are available when the business demands it. This does not necessarily mean “as soon as possible” (ASAP). A quarterly report from an MIS should be delivered at the end of a quarter, but not before that. Even a longer timing applies to business processes containing wait times that must be respected. This is the case with processes involving courts of law and attorneys. Such processes need to allow time for collecting strong evidence on legal cases rather than being executed in the shortest possible time. Yet sometimes the speed is really crucial, as in securities trading. The ASAP data are necessary, but still because business demands so.


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Questions for Review

1. Data analysis is useful for understanding business and ultimately designing IS. However it can be applied almost anywhere. Can you try to analyze yourself in the context of things you do, you have, you like, and so on? For example, I (entity) attend (relationship) school (entity); I like... and so on.

2. Describe a company as a system. For each system part, discuss an information system

you learned about in this chapter.

3. List the key entities in the schemas you learned about in this chapter.

4. Does the schema for production scheduling (Figure 2) have anything in common with the schema for pay process (Figure 3)?

5. How is entity wage or salary captured in an accounting IS?

6. What is special about the schema for market segmentation and for the IS used for this

purpose?

7. Focus on Figure 7. Why does table PromotionList have a combined key?

8. What are the similarities and differences between the customer in the classical brick and mortar store and online customer? Take the merchant perspective.

9. What is big in Big Data?

10. List all data quality standards (ReCoN & ACT).

11. Define three data quality standards and give an example for each.


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Exercise

Course Registration System at the University of Manitoba Each academic term, between 25,000-30,000 students register to take courses at UofM. The process begins with the individual courses that are tracked by their ID (e.g., MIS 2000), title, the faculty offering it, number of credits, and a short description. Courses are offered through course sections; one course runs in one or more sections. Each section is described in terms of the course it belongs to, year of the offering, semester, section number, day, time, maximum enrollment, and campus location. A student registers for a course section by going through the registration task. Each registration record identifies a student and the section the student enrols into. Also recorded are the date of registration and whether a student has paid for it. Student details maintained are the first, middle and last name, study major, and student’s home faculty. Create a complete schema for the UofM Course Registration process. Do not forget to include primary and foreign keys.

chapter 4 data analysis and diagramming in various ... · data analysis and diagramming in various...

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