ch 5, 10

Budgetary control

After an organization has finalized its annual budget, it divides those monies into monthly allocations. Managers at various levels monitor departmental expenditures and compare them against the budget and the operational progress of corporate plans.

Budgeting

An essential component of the accounting/finance function is the annual budget, which allocates the organization's fi nancial resources among participants and activities. The budget allows management to distribute resources in the way that best supports the organization's mission and goals.

• Financial ratio analysis:

Another major accounting/finance function is to monitor the company's financial health by assessing a set of financial ratios. Included here are liquidity ratios (the availability of cash to pay debt), activity ratios (how quickly a fi rm converts noncash assets to cash assets), debt ratios (measure the fi rm's ability to repay long-term debt), and profi tability ratios (measure the fi rm's use of its assets and control of its expenses to generate an acceptable rate of return).

• Data independence:

Applications and data are independent of one another; that is, applications and data are not linked to each other, so all applications are able to access the same data.

• Data isolation:

Applications cannot access data associated with other applications.

Financial Planning and Budgeting.

Appropriate management of financial assets is a major task in fi nancial planning and budgeting. Managers must plan for both acquiring and utilizing resources.

Inventory Management

As the name suggests, inventory management determines how much inventory an organization should maintain. Both excessive inventory and insuffi cient inventory create problems. Overstocking can be expensive, because of storage costs and the costs of spoilage and obsolescence. However, keeping insuffi cient inventory is also expensive, because of last-minute orders and lost sales

• Auditing:

Auditing has two basic purposes: (1) to monitor how the organization's monies are being spent and (2) to assess the organization's financial health. Internal auditing is performed by the organization's accounting/finance personnel. These employees also prepare for periodic external audits by outside CPA firms.

• Data integrity:

Data meet certain constraints; for example, there are no alphabetic characters in a Social Security number fi eld.

• Data inconsistency:

Various copies of the data do not agree.

• Volume:

We have noted the huge volume of Big Data. Consider machine-generated data, which are generated in much larger quantities than nontraditional data. For instance, sensors in a single jet engine can generate 10 terabytes of data in 30 minutes. (See our discussion of the Internet of Things in Chapter 8.) With more than 25,000 airline fl ights per day, the daily volume of data from just this single source is incredible. Smart electrical meters, sensors in heavy industrial equipment, and telemetry from automobiles compound the volume problem.

Managing Big Data

When properly analyzed, Big Data can reveal valuable patterns and information that were previously hidden because of the amount of work required to discover them. Leading corporations, such as Walmart and Google, have been able to process Big Data for years, but only at great expense. Today's hardware, cloud computing (see Technology Guide 3), and opensource software make processing Big Data affordable for most organizations. The first step for many organizations toward managing data was to integrate information silos into a database environment and then to develop data warehouses for decision making. After completing this step, many organizations turned their attention to the business of information management—making sense of their proliferating data. In recent years, Oracle, IBM, Microsoft, and SAP have spent billions of dollars purchasing software firms that specialize in data management and business intelligence

Such out-of-the routine reports are called

ad hoc (on-demand) reports.

procurement process

also known as the order-to-cash process, the company sells goods to a customer. Fulfi llment originates when the company receives a customer order, and it concludes when the company receives a payment from the customer.

interorganizational processes,

and they typically involve supply chain management (SCM) and customer relationship management (CRM) systems. SCM and CRM processes help multiple firms in an industry coordinate activities such as the production-to-sale of goods and services.

A secondary key is

another field that has some identifying information, but typically does not identify the record with complete accuracy. For example, the student's major might be a secondary key if a user wanted to identify all of the students majoring in a particular field of study.

Functional dependencies

are a means of expressing that the value of one particular attribute is associated with a specific single value of another attribute. For example, for a Student Number 05345 at a university, there is exactly one Student Name, John C. Jones, associated with it.

Master data

are a set of core data, such as customer, product, employee, vendor, geographic location, and so on, that span the enterprise information systems. ex: the master data are "product sold," "vendor," "salesperson," "store," "part number," "purchase price," and "date."

Explicit knowledge

deals with more objective, rational, and technical knowledge. In an organization, explicit knowledge consists of the policies, procedural guides, reports, products, strategies, goals, core competencies, and IT infrastructure of the enterprise.

connectivity

describes the relationship classification.

Drill-down reports

display a greater level of detail. For example, a manager might examine sales by region and decide to "drill down" by focusing specifi cally on sales by store and then by salesperson.

production process

does not occur in all companies because not all companies produce physical goods. In fact, many businesses limit their activities to buying (procurement) and selling products (e.g., retailers).

unary relationship

exists when an association is maintained within a single entity.

ternary relationship

exists when three entities are associated. In this Technology Guide, we discuss only binary relationships because they are the most common. Entity relationships may be classified as one-to-one, one-to-many, or many-to-many.

binary relationship

exists when two entities are associated.

Human Resources Planning and Management. Managing

human resources in large organizations requires extensive planning and detailed strategy. IT support is particularly valuable in the following three areas: • Payroll and employees' records: The HR department is responsible for payroll preparation. This process is typically automated, meaning that paychecks are printed or money is transferred electronically into employees' bank accounts. • Benefi ts administration: In return for their work contributions to their organizations, employees receive wages, bonuses, and various benefi ts. These benefi ts include healthcare and dental care, pension contributions (in a decreasing number of organizations), 401K contributions, wellness centers, and child care centers. Managing benefi ts is a complex task because multiple options are available and organizations typically allow employees to choose and trade off their benefi ts. In many organizations, employees can access the company portal to self-register for specifi c benefi ts. • Employee relationship management: In their efforts to better manage employees, companies are developing employee relationship management (ERM) applications. A typical ERM application is a call center for employees' problems. Table 10.1 provides an overview of the activities

Relationships

illustrate an association between entities. The degree of a relationship indicates the number of entities associated with a relationship

Exception reports

include only information that falls outside certain threshold standards. To implement management by exception, management first establishes performance standards. The company then creates systems to monitor performance (via the incoming data about business transactions such as expenditures), to compare actual performance to the standards, and to identify exceptions to the standards.

An enterprise application integration (EAI) system

integrates existing systems by providing software, called middleware, that connects multiple applications. In essence, the EAI system allows existing applications to communicate and share data, thereby enabling organizations to utilize existing applications while eliminating many of the problems caused by isolated information systems.

ERP II systems are

inter-organizational ERP systems that provide Web-enabled links among a company's key business systems—such as inventory and production—and its customers, suppliers, distributors, and other relevant parties

Another problem is that data are generated from multiple sources:

internal sources (for example, corporate databases and company documents); personal sources (for example, personal thoughts, opinions, and experiences); and external sources (for example, commercial databases, government reports, and corporate Web sites).

A data model

is a diagram that represents entities in the database and their relationships.

A foreign key

is a field (or group of fields) in one table that uniquely identifies a row of another table. A foreign key is used to establish and enforce a link between two tables.

A data mart

is a low-cost, scaled-down version of a data warehouse that is designed for the enduser needs in a strategic business unit (SBU) or an individual department. Data marts can be implemented more quickly than data warehouses, often in less than 90 days. Further, they support local rather than central control by conferring power on the user group. Typically, groups that need a single or a few BI applications require only a data mart, rather than a data warehouse.

Normalization

is a method for analyzing and reducing a relational database to its most streamlined form to ensure minimum redundancy, maximum data integrity, and optimal processing performance.

An entity

is a person, place, thing, or event—such as a customer, an employee, or a product— about which information is maintained. Entities can typically be identifi ed in the user's work environment. A record generally describes an entity

Knowledge management

is a process that helps organizations manipulate important knowledge that comprises part of the organization's memory, usually in an unstructured format. For an organization to be successful, knowledge, as a form of capital, must exist in a format that can be exchanged among persons.

Master data management

is a process that spans all organizational business processes and applications. It provides companies with the ability to store, maintain, exchange, and synchronize a consistent, accurate, and timely "single version of the truth" for the company's master data.

A data warehouse

is a repository of historical data that are organized by subject to support decision makers in the organization.

A database management system (DBMS)

is a set of programs that provide users with tools to create and manage a database. Managing a database refers to the processes of adding, deleting, accessing, modifying, and analyzing data stored in a database. An organization can access the data by using query and reporting tools that are part of the DBMS or by using application programs specifi cally written to perform this function.

Computer-integrated manufacturing (CIM) (also called digital manufacturing)

is an approach that integrates various automated factory systems. CIM has three basic goals: (1) to simplify all manufacturing technologies and techniques, (2) to automate as many of the manufacturing processes as possible, and (3) to integrate and coordinate all aspects of design, manufacturing, and related functions via computer systems.

Data governance

is an approach to managing information across an entire organization. It involves a formal set of business processes and policies that are designed to ensure that data are handled in a certain, well-defined fashion. That is, the organization follows unambiguous rules for creating, collecting, handling, and protecting its information. The objective is to make information available, transparent, and useful for the people who are authorized to access it, from the moment it enters an organization until it is outdated and deleted.

ch 10. A transaction

is any business event that generates data worthy of being captured and stored in a database. Examples of transactions are a product manufactured, a service sold, a person hired, and a payroll check generated.

relational database model

is based on the concept of two-dimensional tables. A relational database generally is not one big table—usually called a flat file—that contains all of the records and attributes.

A cross-departmental process

is one that (1) originates in one department and ends in a different department or (2) originates and ends in the same department but involves other departments

tacit knowledge

is the cumulative store of subjective or experiential learning. In an organization, tacit knowledge consists of an organization's experiences, insights, expertise, know-how, trade secrets, skill sets, understanding, and learning.

The data dictionary

provides information on each attribute, such as its name, if it is a key, part of a key, or a non-key attribute, the type of data expected (alphanumeric, numeric, dates, etc.), and valid values. Data dictionaries can also provide information on why the attribute is needed in the database; which business functions, applications, forms, and reports use the attribute; and how often the attribute should be updated.

Cardinality

refers to the maximum number of times an instance of one entity can be associated with an instance in the related entity. Cardinality can be mandatory single, optional single, mandatory many, or optional many.

Key indicator

reports summarize the performance of critical activities. For example, a chief fi nancial offi cer might want to monitor cash fl ow and cash on hand.

A bit (binary digit)

represents the smallest unit of data a computer can process. binary means that a bit can consist only of a 0 or a 1.

Governance

requires that people, committees, and processes be in place. Companies that are effective in BI governance often create a senior level committee comprised of vice presidents and directors who (1) ensure that the business strategies and BI strategies are in alignment, (2) prioritize projects, and (3) allocate resources.

fulfillment process can follow two basic strategies:

sell-from-stock and configure-to order. Sell-from-stock involves fulfi lling customer orders directly using goods that are in the warehouse (stock). These goods are standard, meaning that the company does not customize them for buyers. In contrast, in confi gure-to-order, the company customizes the product in response to a customer request.

functional area information systems (FAISs)

supports a particular functional area in the organization by increasing each area's internal effi ciency and effectiveness. FAISs often convey information in a variety of reports, which you will see later in this chapter. Examples of FAISs are accounting IS, fi nance IS, production/ operations management (POM) IS, marketing IS, and human resources IS.

A transaction processing system (TPS)

supports the monitoring, collection, storage, and processing of data from the organization's basic business transactions, each of which generates data. The TPS collects data continuously, typically in real time—that is, as soon as the data are generated—and it provides the input data for the corporate databases. The TPSs are critical to the success of any enterprise because they support core operations.

Defi ning Big Data

technology research fi rm Gartner (www.gartner.com) defi nes Big Data as diverse, high-volume, high-velocity information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization. Second, the Big Data Institute (TBDI; www.the-bigdatainstitute.com) defi nes Big Data as vast data sets that perform the following: • Exhibit variety. • Include structured, unstructured, and semistructured data.

batch processing

the firm collects data from transactions as they occur, placing them in groups or batches. The system then prepares and processes the batches periodically (say, every night).

entity-relationship (ER) modeling

using an entity-relationship diagram. There are many approaches to ER diagramming. You will see one particular approach here, but there are others. The good news is that if you are familiar with one version of ER diagramming, then you will be able to easily adapt to any other version. ER diagrams consist of entities, attributes, and relationships.

Transaction data

which are generated and captured by operational systems, describe the business's activities, or transactions. ex: transaction data would be, respectively, "42-inch plasma television," "Samsung," "Best Buy," "Bill Roberts," "1234," "$2,000," and "April 20, 2014."

Organizations, business processes, and business activities operate with, and manage, financial transactions.

• Global stock exchanges: Financial markets operate in global, 24/7/365, distributed electronic stock exchanges that use the Internet both to buy and sell stocks and to broadcast real-time stock prices. • Managing multiple currencies: Global trade involves fi nancial transactions that are carried out in different currencies. The conversion ratios of these currencies are constantly in fl ux. Financial and accounting systems utilize fi nancial data from different countries, and they convert the currencies from and to any other currency in seconds. Reports based on these data, which formerly required several days to generate, can now be produced in only seconds. In addition to currency conversions, these systems manage multiple languages as well. • Virtual close: Companies traditionally closed their books (accounting records) quarterly, usually to meet regulatory requirements. Today, many companies want to be able to close their books at any time, on very short notice. Information systems make it possible to close the books quickly in what is called a virtual close. This process provides almost real-time information on the organization's financial health. • Expense management automation: Expense management automation (EMA) refers to systems that automate the data entry and processing of travel and entertainment expenses. EMA systems are Web-based applications that enable companies to quickly and consistently collect expense information, enforce company policies and contracts, and reduce unplanned purchases as well as airline and hotel expenses. They also allow companies to reimburse their employees more quickly because expense approvals are not delayed by poor documentation.

Intellectual capital

(or intellectual assets) is another term for knowledge.

an exabyte is

1 trillion terabytes

Putting Big Data to Use

1. Making Big Data Available. Making Big Data available for relevant stakeholders can help organizations gain value. For example, consider open data in the public sector. Open data is accessible public data that individuals and organizations can use to create new businesses and solve complex problems. In particular, government agencies gather very large amounts of data, some of which is Big Data. Making that data available can provide economic benefi ts. 2. Enabling Organizations to Conduct Experiments. Big Data allows organizations to improve performance by conducting controlled experiments. For example, Amazon (and many other companies such as Google and LinkedIn) constantly experiments by offering slight different "looks" on its Web site. These experiments are called A/B experiments, because each experiment has only two possible outcomes. 3. Microsegmentation of Customers. Segmentation of a company's customers means dividing them into groups that share one or more characteristics. Microsegmentation simply means dividing customers into very small groups, or even down to the individual level. For example, Paytronix Systems (www.paytronix.com) provides loyalty and rewards program software to thou sands of different restaurants. 4. Creating New Business Models. Companies are able to use Big Data to create new business models. For example, a commercial transportation company operated a large fl eet of large, long-haul trucks. The company recently placed sensors on all its trucks. These sensors wirelessly communicate large amounts of information to the company, a process called telematics. The sensors collect data on vehicle usage (including acceleration, braking, cornering, etc.), driver performance, and vehicle maintenance. By analyzing this Big Data, the transportation company was able to improve the condition of its trucks through near-real-time analysis that proactively suggested preventive maintenance 5. Organizations Can Analyze More Data. In some cases, organizations can even process all the data relating to a particular phenomenon, meaning that they do not have to rely as much on sampling. Random sampling works well, but it is not as effective as analyzing an entire dataset. In addition, random sampling has some basic weaknesses. To begin with, its accuracy depends on ensuring randomness when collecting the sample data. However, achieving such randomness is problematic. Systematic biases in the process of data collection can cause the results to be highly inaccurate.

byte

A group of eight bits, represents a single character. A byte can be a letter, a number, or a symbol.

what is called a data file or a table.

A logical grouping of related records . For example, a grouping of the records from a particular course, consisting of course number, professor, and students' grades, would constitute a data file for that course

database systems maximize the following: • Data security:

Because data are "put in one place" in databases, there is a risk of losing a lot of data at one time. Therefore, databases must have extremely high security measures in place to minimize mistakes and deter attacks.

Issues with Big Data

Big Data can come from untrusted sources: As we discussed above, one of the characteristics of Big Data is variety, meaning that Big Data can come from numerous, widely varied sources. These sources may be internal or external to the organization. For instance, a company might want to integrate data from unstructured sources such as e-mails, call center notes, and social media posts with structured data about its customers from its data warehouse. Big Data is dirty: Dirty data refers to inaccurate, incomplete, incorrect, duplicate, or erroneous data. Examples of such problems are misspelling of words and duplicate data such as retweets or company press releases that appear numerous times in social media. Big Data changes, especially in data streams: Organizations must be aware that data quality in an analysis can change, or the data itself can change, because the conditions under which the data are captured can change. For instance, imagine a utility company that analyzes weather data and smart-meter data to predict customer power usage.

Characteristics of Big Data

Big Data has three distinct characteristics: volume, velocity, and variety. These characteristics distinguish Big Data from traditional data.

Clickstream data

Data also come from the Web, in the form of clickstream data. Clickstream data are those data that visitors and customers produce when they visit a Web site and click on hyperlinks provide a trail of the users' activities in the Web site, including user behavior and browsing patterns.

An attribute

Each characteristic or quality of a particular entity For example, if our entities were a customer, an employee, and a product, entity attributes would include customer name, employee number, and product color.

This identifier field (or attribute) is called the primary key

Every record in the database must contain at least one field that uniquely identifies that record so that it can be retrieved, updated, and sorted. ex: SSN is primary key

Two other factors complicate data management

First, federal regulations The law also holds CEOs and CFOs personally responsible for such disclosures. If their companies lack satisfactory data management policies and fraud or a security breach occurs, the company officers could be held liable and face prosecution. Second, companies are drowning in data, much of which is unstructured. As you have seen, the amount of data is increasing exponentially. To be profitable, companies must develop a strategy for managing these data effectively.

big data functional areas

Human Resources. Employee benefits, particularly healthcare, represent a major business expense. Consequently, some companies have turned to Big Data to better manage these benefits. Caesars Entertainment (www.caesars.com), for example, analyzes health-insurance claim data for its 65,000 employees and their covered family members. Managers can track thousands of variables that indicate how employees use medical services, such as the number of emergency room visits and whether employees choose a generic or brand name drug. Marketing-Product Development. Big Data can help capture customer preferences and put that information to work in designing new products. For example, Ford Motor Company (www.ford .com) was considering a "three blink" turn indicator that had been available on its European cars for years. Unlike the turn signals on its U.S. vehicles, this indicator fl ashes three times at the driver's touch and then automatically shuts off. Marketing. Marketing managers have long used data to better understand their customers and to target their marketing efforts more directly. Today, Big Data enables marketers to craft much more personalized messages. Government Operations. With 55 percent of the population of the Netherlands living under the threat of fl ooding, water management is critically important to the Dutch government. The government operates a sophisticated water management system, managing a network of dykes or levees, canals, locks, harbors, dams, rivers, storm-surge barriers, sluices, and pumping stations.

Metadata

It is important to maintain data about the data, known as metadata, in the data warehouse. Both the IT personnel who operate and manage the data warehouse and the users who access the data need metadata. IT personnel need information about data sources; database, table, and column names; refresh schedules; and data-usage measures. Users' needs include data defi nitions, report/query tools, report distribution information, and contact information for the help desk.

The KMS Cycle

KMS follows a cycle that consists of six steps (see Figure 5.8). The reason the system is cyclical is that knowledge is dynamically refined over time. The knowledge in an effective KMS is never finalized because the environment changes over time and knowledge must be updated to reflect these changes. The cycle works as follows: 1. Create knowledge: Knowledge is created as people determine new ways of doing things or develop know-how. Sometimes external knowledge is brought in. 2. Capture knowledge: New knowledge must be identified as valuable and be represented in a reasonable way. 3. Refine knowledge: New knowledge must be placed in context so that it is actionable. This is where tacit qualities (human insights) must be captured along with explicit facts. 4. Store knowledge: Useful knowledge must then be stored in a reasonable format in a knowledge repository so that other people in the organization can access it. 5. Manage knowledge: Like a library, the knowledge must be kept current. It must be reviewed regularly to verify that it is relevant and accurate. 6. Disseminate knowledge: Knowledge must be made available in a useful format to anyone in the organization who needs it, anywhere and anytime

Financial and economic forecasting

Knowledge about the availability and cost of money is a key ingredient for successful financial planning. Cash fl ow projections are particularly important because they inform organizations what funds they need, when they need them, and how they will acquire them.

Users

Once the data are loaded in a data mart or warehouse, they can be accessed. At this point the organization begins to obtain business value from BI; all of the prior stages constitute creating BI infrastructure.

Control and Auditing

One major reason why organizations go out of business is their inability to forecast and/or secure a sufficient cash fl ow. Underestimating expenses, overspending, engaging in fraud, and mismanaging fi nancial statements can lead to disaster. Consequently, it is essential that organizations effectively control their fi nances and fi nancial statements. Let's examine some of the most common forms of financial control

Big Data is about predictions.

Predictions do not come from "teaching" computers to "think" like humans. Instead, predictions come from applying mathematics to huge quantities of data to infer probabilities. Consider the following examples: • The likelihood that an e-mail message is spam. • The likelihood that the typed letters "teh" are supposed to be "the." • The likelihood that the trajectory and velocity of a person jaywalking indicate that he will make it across the street in time, meaning that a self-driving car need only slow down slightly. Big Data systems perform well because they contain huge amounts of data on which to base their predictions. Moreover, these systems are configured to improve themselves over time by searching for the most valuable signals and patterns as more data are input.

Big Data

Such data consist of structured and unstructured data. the superabundance of data available today a collection of data so large and complex that it is diffi cult to manage using traditional database management systems

Data Quality.

The quality of the data in the warehouse must meet users' needs. If it does not, users will not trust the data and ultimately will not use it. Most organizations find that the quality of the data in source systems is poor and must be improved before the data can be used in the data warehouse.

• Velocity:

The rate at which data fl ow into an organization is rapidly increasing. Velocity is critical because it increases the speed of the feedback loop between a company, its customers, its suppliers, and its business partners. For example, the Internet and mobile technology enable online retailers to compile histories not only on fi nal sales but also on their customers' every click and interaction. Companies that can quickly utilize that information— for example, by recommending additional purchases—gain competitive advantage.

Database systems minimize the following problems Data redundancy:

The same data are stored in multiple locations.

• Variety:

Traditional data formats tend to be structured and relatively well described, and they change slowly. Traditional data include fi nancial market data, point-of-sale transactions, and much more. In contrast, Big Data formats change rapidly. They include satellite imagery, broadcast audio streams, digital music fi les, Web page content, scans of government documents, and comments posted on social networks.

Big Data generally consists of the following:

Traditional enterprise data: Examples are customer information from customer relationship management systems, transactional enterprise resource planning data, Web store transactions, operations data, and general ledger data. • Machine-generated/sensor data: Examples are smart meters; manufacturing sensors; sensors integrated into smartphones, automobiles, airplane engines, and industrial machines; equipment logs; and trading systems data. • Social data: Examples are customer feedback comments; microblogging sites such as Twitter; and social media sites such as Facebook, YouTube, and LinkedIn. • Images captured by billions of devices located throughout the world, from digital cameras and camera phones to medical scanners and security cameras.

Enterprise resource planning (ERP) systems

are designed to correct a lack of communication among the functional area IS. ERP systems resolve this problem by tightly integrating the functional area IS via a common database. For this reason, experts credit ERP systems with greatly increasing organizational productivity. systems adopt a business process view of the overall organization to integrate the planning, management, and use of all of an organization's resources, employing a common software platform and database.

Routine reports

are produced at scheduled intervals. They range from hourly quality control reports to daily reports on absenteeism rates. Although routine reports are extremely valuable to an organization, managers frequently need special information that is not included in these reports.

online transaction processing (OLTP)

business transactions are processed online as soon as they occur. For example, when you pay for an item at a store, the system records the sale by reducing the inventory on hand by one unit, increasing sales fi gures for the item by one unit, and increasing the store's cash position by the amount you paid.

A data file is a

collection of logically related records. In a fi le management environment, each application has a specific data file related to it. This file contains all of the data records the application requires. Over time, organizations developed numerous applications, each with an associated, application-specific data file.

The join operation

combines records from two or more tables in a database to obtain information that is located in different tables.

Comparative reports

compare, for example, the performances of different business units or of a single unit during different times.

Structured query language (SQL)

is the most popular query language used for interacting with a database. SQL allows people to perform complicated searches by using relatively simple statements or key words. Typical key words are SELECT (to choose a desired attribute), FROM (to specify the table or tables to be used), and WHERE (to specify conditions to apply in the query).

field

logical grouping of characters into a word, a small group of words, or an identification number . For example, a student's name in a university's computer files would appear in the "name" field, and her or his Social Security number would appear in the "Social Security number" field. Fields can also contain data other than text and numbers. They can contain an image, or any other type of multimedia

An instance

of an entity refers to each row in a relational table, which is a specific, unique representation of the entity. For example, your university's student database contains an entity called STUDENT. An instance of the STUDENT entity would be a particular student. For instance, you are an instance of the STUDENT entity in your university's student database.

In-House Logistics and Materials Management. Logistics management deals with

ordering, purchasing, inbound logistics (receiving), and outbound logistics (shipping) activities. Related activities include inventory management and quality control.

procurement process

originates when a company needs to acquire goods or services from external sources, and it concludes when the company receives and pays for them.

entity is a person

place, or thing that can be identified in the users' work environment.

Business rules are

precise descriptions of policies, procedures, or principles in any organization that stores and uses data to generate information. Business rules are derived from a description of an organization's operations, and help create and enforce business processes in that organization. Keep in mind that you determine these business rules, not the MIS department.

Benefits and Limitations of ERP Systems

• Organizational fl exibility and agility: As you have seen, ERP systems break down many former departmental and functional silos of business processes, information systems, and information resources. In this way, they make organizations more fl exible, agile, and adaptive. The organizations can therefore respond quickly to changing business conditions and capitalize on new business opportunities. • Decision support: ERP systems provide essential information on business performance across functional areas. This information signifi cantly improves managers' ability to make better, more timely decisions. • Quality and effi ciency: ERP systems integrate and improve an organization's business processes, generating signifi cant improvements in the quality of production, distribution, and customer service.

basic characteristics of data warehouses and data marts include

• Organized by business dimension or subject: Data are organized by subject—for example, by customer, vendor, product, price level, and region. This arrangement differs from transactional systems, where data are organized by business process, such as order entry, inventory control, and accounts receivable. • Use online analytical processing: Typically, organizational databases are oriented toward handling transactions. That is, databases use online transaction processing (OLTP), where business transactions are processed online as soon as they occur. The objectives are speed and effi ciency, which are critical to a successful Internet-based business operation. Data warehouses and data marts, which are designed to support decision makers but not OLTP, use online analytical processing. Online analytical processing (OLAP) involves the analysis of accumulated data by end users. We consider OLAP in greater detail in Chapter 12. • Integrated: Data are collected from multiple systems and then integrated around subjects. For example, customer data may be extracted from internal (and external) systems and then integrated around a customer identifi er, thereby creating a comprehensive view of the customer. • Time variant: Data warehouses and data marts maintain historical data (i.e., data that include time as a variable). Unlike transactional systems, which maintain only recent data (such as for the last day, week, or month), a warehouse or mart may store years of data. Organizations utilize historical data to detect deviations, trends, and long-term relationships. • Nonvolatile: Data warehouses and data marts are nonvolatile—that is, users cannot change or update the data. Therefore, the warehouse or mart refl ects history, which, as we just saw, is critical for identifying and analyzing trends. Warehouses and marts are updated, but through IT-controlled load processes rather than by users. • Multidimensional: Typically, the data warehouse or mart uses a multidimensional data structure. Recall that relational databases store data in two-dimensional tables. In contrast, data warehouses and marts store data in more than two dimensions. For this reason, the data are said to be stored in a multidimensional structure. A common representation for this multidimensional structure is the data cube.

three prominent examples of cross-departmental processes:

• The procurement process, which originates in the warehouse department (need to buy) and ends in the accounting department (send payment) • The fulfi llment process, which originates in the sales department (customer request to buy) and ends in the accounting department (receive payment) • The production process, which originates and ends in the warehouse department (need to produce and reception of fi nished goods), but involves the production department as well.

On-Premise ERP Implementation.

• The vanilla approach: In this approach, a company implements a standard ERP package, using the package's built-in confi guration options. When the system is implemented in this way, it will deviate only minimally from the package's standardized settings. The vanilla approach can enable the company to perform the implementation more quickly. However, the extent to which the software is adapted to the organization's specifi c processes is limited. Fortunately, a vanilla implementation provides general functions that can support the fi rm's common business processes with relative ease, even if they are not a perfect fi t for those processes. • The custom approach: In this approach, a company implements a more customized ERP system by developing new ERP functions designed specifi cally for that fi rm. Decisions concerning the ERP's degree of customization are specifi c to each organization. To utilize the custom approach, the organization must carefully analyze its existing business processes to develop a system that conforms to the organization's particular characteristics and processes. In addition, customization is expensive and risky because computer code must be written and updated every time a new version of the ERP software is released. Going further, if the customization does not perfectly match the organization's needs, then the system can be very diffi cult to use. • The best of breed approach: This approach combines the benefi ts of the vanilla and customized systems while avoiding the extensive costs and risks associated with complete customization. Companies that adopt this approach mix and match core ERP modules as well as other extended ERP modules from different software providers to best fi t their unique internal processes and value chains. Thus, a company may choose several core ERP modules from an established vendor to take advantage of industry best practices—for example, for fi nancial management and human resource management.