Introduction to How Businesses Use Information

What you’ll learn to do: discuss ways in which information is used in business

Traditionally we think about value in business in terms of assets—property, plants, equipment, inventory and even human resources. The explosion of technology over the last decade has made us re-think what is valuable. In fact, what many businesses today consider to be their most valuable asset cannot be held in your hand because it is the information generated by the collection of billions of bits of data. In fact, the data that businesses gather about their customers is, to the most progressive companies, invaluable! For example, when you visit a company’s website, data is captured about what you looked at: what colors you preferred, how long you remained on a page and yes, even your physical location.

Companies take that “data” and turn it into useful information. They can then use this information to push advertising to you, not just through their website but to your social media accounts, your email, and even your cell phone. As the collection of data becomes easier and more cost effective, businesses are constantly generating new and better information about the business environment. In this section you will learn the difference between data and information, the types of data that businesses collect, and, finally, how businesses use information.

1. Management Information Systems

What types of systems make up a typical company’s management information system?

Whereas individuals use business productivity software such as word processing, spreadsheet, and graphics programs to accomplish a variety of tasks, the job of managing a company’s information needs falls to management information systems: users, hardware, and software that support decision-making. Information systems collect and store the company’s key data and produce the information managers need for analysis, control, and decision-making.

Factories use computer-based information systems to automate production processes and order and monitor inventory. Most companies use them to process customer orders and handle billing and vendor payments. Banks use a variety of information systems to process transactions such as deposits, ATM withdrawals, and loan payments. Most consumer transactions also involve information systems. When you check out at the supermarket, book a hotel room online, or download music over the internet, information systems record and track the transaction and transmit the data to the necessary places.

Companies typically have several types of information systems, starting with systems to process transactions. Management support systems are dynamic systems that allow users to analyze data to make forecasts, identify business trends, and model business strategies. Office automation systems improve the flow of communication throughout the organization. Each type of information system serves a particular level of decision-making: operational, tactical, and strategic. Exhibit 13.6 shows the relationship between transaction processing and management support systems as well as the management levels they serve. Let’s take a more detailed look at how companies and managers use transaction processing and management support systems to manage information.

Transaction Processing Systems

A firm’s integrated information system starts with its transaction processing system (TPS). The TPS receives raw data from internal and external sources and prepares these data for storage in a database similar to a microcomputer database but vastly larger. In fact, all the company’s key data are stored in a single huge database that becomes the company’s central information resource. As noted earlier, the database management system tracks the data and allows users to query the database for the information they need.

The Operational manager’s domain is where internal and external data sources flow into a transaction processing system. This flows into an internal data base, and now is in the overlap domain of operational managers and middle managers. There are 4 branches from the internal database. First, information reporting system. The next 3 branches are overlapped by middle managers and top executives. Second branch goes to expert systems, and to suggested decisions. Third branch goes to decision support, then to possible solutions, under top executives only. The fourth branch goes to executive information systems, which are fed by external databases, and are top executive domain.

Exhibit 13.6 A Company’s Integrated Information System (Attribution: Copyright Rice University, OpenStax, under CC BY 4.0 license.)

The database can be updated in two ways: batch processing, where data are collected over some time period and processed together, and online, or real-time, processing, which processes data as they become available. Batch processing uses computer resources very efficiently and is well-suited to applications such as payroll processing that require periodic rather than continuous processing. Online processing keeps the company’s data current. When you make an airline reservation, the information is entered into the airline’s information system, and you quickly receive confirmation, typically through an e-mail. Online processing is more expensive than batch processing, so companies must weigh the cost versus the benefit. For example, a factory that operates around the clock may use real-time processing for inventory and other time-sensitive requirements but process accounting data in batches overnight.

Decisions, Decisions: Management Support Systems

Transaction processing systems automate routine and tedious back-office processes such as accounting, order processing, and financial reporting. They reduce clerical expenses and provide basic operational information quickly. Management support systems (MSS) use the internal master database to perform high-level analyses that help managers make better decisions.

Information technologies such as data warehousing are part of more advanced MSSs. A data warehousecombines many databases across the whole company into one central database that supports management decision-making. With a data warehouse, managers can easily access and share data across the enterprise to get a broad overview rather than just isolated segments of information. Data warehouses include software to extract data from operational databases, maintain the data in the warehouse, and provide data to users. They can analyze data much faster than transaction-processing systems. Data warehouses may contain many data marts, special subsets of a data warehouse that each deal with a single area of data. Data marts are organized for quick analysis.

Companies use data warehouses to gather, secure, and analyze data for many purposes, including customer relationship management systems, fraud detection, product-line analysis, and corporate asset management. Retailers might wish to identify customer demographic characteristics and shopping patterns to improve direct-mailing responses. Banks can more easily spot credit-card fraud, as well as analyze customer usage patterns.

According to Forrester Research, about 60 percent of companies with $1 billion or more in revenues use data warehouses as a management tool. Union Pacific (UP), a $19 billion railroad, turned to data warehouse technology to streamline its business operations. By consolidating multiple separate systems, UP achieved a unified supply-chain system that also enhanced its customer service. “Before our data warehouse came into being we had stovepipe systems,” says Roger Bresnahan, principal engineer. “None of them talked to each other. . . . We couldn’t get a whole picture of the railroad.”

UP’s data warehouse system took many years and the involvement of 26 departments to create. The results were well worth the effort: UP can now make more accurate forecasts, identify the best traffic routes, and determine the most profitable market segments. The ability to predict seasonal patterns and manage fuel costs more closely has saved UP millions of dollars by optimizing locomotive and other asset utilization and through more efficient crew management. In just three years, Bresnahan reports, the data warehouse system had paid for itself.¹²

At the first level of an MSS is an information-reporting system, which uses summary data collected by the TPS to produce both regularly scheduled and special reports. The level of detail would depend on the user. A company’s payroll personnel might get a weekly payroll report showing how each employee’s paycheck was determined. Higher-level mangers might receive a payroll summary report that shows total labor cost and overtime by department and a comparison of current labor costs with those in the prior year. Exception reports show cases that fail to meet some standard. An accounts receivable exception report that lists all customers with overdue accounts would help collection personnel focus their work. Special reports are generated only when a manager requests them; for example, a report showing sales by region and type of customer can highlight reasons for a sales decline.

Decision Support Systems

A decision support system (DSS) helps managers make decisions using interactive computer models that describe real-world processes. The DSS also uses data from the internal database but looks for specific data that relate to the problems at hand. It is a tool for answering “what if” questions about what would happen if the manager made certain changes. In simple cases, a manager can create a spreadsheet and try changing some of the numbers. For instance, a manager could create a spreadsheet to show the amount of overtime required if the number of workers increases or decreases. With models, the manager enters into the computer the values that describe a particular situation, and the program computes the results. Marketing executives at a furniture company could run DSS models that use sales data and demographic assumptions to develop forecasts of the types of furniture that would appeal to the fastest-growing population groups.

Companies can use a predictive analytics program to improve their inventory management system and use big data to target customer segments for new products and line extensions.

A photograph shows a doctor in a recover room, standing beside a patient.

Exhibit 13.7 Decision support systems help businesses by providing quantitative data and predictive models that aid problem-solving and decision-making. Now the health-care industry wants this technology in hospitals to improve the practice of medicine. Spearheading the effort for a clinical decision-support system is the American Medical Informatics Association, which believes a national DSS could help physicians with diagnosing and treating illnesses. What are the pros and cons to having medical professionals rely on a DSS for help in treating patients? (Credit: Axelle Geelen/ flickr/ Attribution 2.0 Generic (CC BY 2.0))

Executive Information Systems

Although similar to a DSS, an executive information system (EIS) is customized for an individual executive. These systems provide specific information for strategic decisions. For example, a CEO’s EIS may include special spreadsheets that present financial data comparing the company to its principal competitors and graphs showing current economic and industry trends.

Expert Systems

An expert system gives managers advice similar to what they would get from a human consultant. Artificial intelligence enables computers to reason and learn to solve problems in much the same way humans do, using what-if reasoning. Although they are expensive and difficult to create, expert systems are finding their way into more companies as more applications are found. Lower-end expert systems can even run on mobile devices. Top-of-the-line systems help airlines appropriately deploy aircraft and crews, critical to the carriers’ efficient operations. The cost of hiring enough people to do these ongoing analytical tasks would be prohibitively expensive. Expert systems have also been used to help explore for oil, schedule employee work shifts, and diagnose illnesses. Some expert systems take the place of human experts, whereas others assist them.

2. Data vs. Information

LEARNING OUTCOMES

Define and distinguish between “data” and “information”

decorative image Many people are under the impression that the terms “data” and “information” are interchangeable and mean the same thing. However, there is a distinct difference between the two words. Data can be any character, text, word, number, and, if not put into context, means little or nothing to a human. However, information is data formatted in a manner that allows it to be utilized by human beings in some significant way. An individual has an almost unlimited amount of data associated with him or herself. This data is of little use to business in its raw, unorganized form. It is not until the data is formatted or compiled into something meaningful that business has information about the individual. For example, suppose the department store Big Box is collecting data about its customers from a loyalty card program and online customer surveys. It collects the following data about a particular customer:

Age: 34

Big Box Account #: 123456

Gender: Female

Zip Code: 22322

Children: 2

Marital Status: Married

Last Purchase: Jogging Pants

These pieces of data alone are not particularly useful to Big Box. It is not until the data is compiled that Big Box begins to get a “picture” of the customer behind account #123456. Transforming this data into information, Big Box is able to know that this customer is a married female who has 2 children and enjoys jogging. They also know that because she lives in zip code 22322 that she is most likely to shop at their store at Halifax Mall since the mall is in the same zip code as the customer’s home address. If Big Box wants to market to her successfully, then they will use this information to include her in an upcoming active wear promotion. Also, since she has children they will also include her in promotions that include children’s wear. The key to collecting data and turning it into useful information for Big Box is that it is a continual process.

So, Big Box includes Customer #123456 in a future mailing and when she comes into the store and makes a purchase her loyalty card records that she purchased several items in the toddler clothing department. This data can be useful information when Big Box sends out information about their annual “Santa Comes to Town” promotion. They can use the purchase data to inform them that Customer #123456 has a toddler and toddlers love to come see Santa!

Later in the year, Customer #123456 makes an online purchase of a pair of men’s work boots and a men’s heavyweight coat. The data that comes into Big Box may look like this:

Customer #123456

Date: 10/5/2018

Item #56-9876 Cougar Work Boots, Size 11

Item #43-2341 Men’s Heavyweight Denim Coat, Size XL

Not very interesting data by itself. But, now Big Box can use this data to have even better information about Customer #123456. They know that Mr. #123456 probably works outdoors, possibly in a skilled trade; hence the need for work boots and a heavy weight coat. When Big Box spends their promotion dollars on a men’s suit sale they will not target Customer #123456 because they have “information” about them, gathered from these individual pieces of data. As Customer #123456 makes additional purchases, visits the company’s web site and responds to special offers they will collect more and more data. Every piece of data collected will be useful in giving Big Box more and more information about this particular customer. Now, imagine this data is collected on every customer for every purchase over a period of years. The quantity of raw data collected is staggering and the challenge for Big Box is to store this data in a manner that allows it to be turned into information. This is where data warehousing and data mining come into play.

3. Business Data

LEARNING OUTCOMES

Describe the different types of data businesses collect

Information flows in and out of a business in many different directions. The type of data a business collects is informed by a business’s goals and objectives. Computing systems can collect a dizzying array of data about the world around us. Businesses must decide what type of data they need to inform their business decisions and where and how that data can be collected. The types of data that businesses collect can be broken down into five broad categories: business process, physical world observations, biological data, public data and personal data. Let’s examine each of these categories of data in greater detail.

Business Process Data. In order to remain competitive businesses must find ways to increase efficiency while maintaining quality standards for their products, goods and services. In order to continuously improve their operations, businesses collect data regarding their business processes. This data can range from collecting data on the number of days it takes their customers to pay invoices to the time it takes to assemble and package a product. In order to collect this type of data, many businesses employ enterprise resource planning systems. ERP systems track business resources—cash, raw materials, production capacity—and the status of business commitments: orders, purchase orders, and payroll. The applications that make up the system share data across various departments (manufacturing, purchasing, sales, accounting, etc.) that provide the data.

Another source of process data is Point of Sale (POS) systems. We are all familiar with these – they are the systems that scan the barcodes on our purchases when we check out at the grocery store. When a cashier scans the barcode on an item that scan collects data that may be used in inventory management, loyalty programs, supplier records, bookkeeping, issuing of purchase orders, quotations and stock transfers, sales reporting and in some cases networking to distribution centers. The more data a business has about its processes the more likely it will find opportunities to improve or enhance those processes.

Physical-world observations. Technology has made it possible for business to capture real-time data about the physical world. This data is collected by the use of devices such as radio frequency identification (RFID), wireless remote cameras, GPS, sensor technology and wireless access points. By inserting computer chips into almost any object companies are able to track the movements of that item and in some cases control the object. One of the early adopters of such technology was the On-Star system installed in millions of U.S. automobiles. Through the use of a combination of RFID, GPS and satellites if car owners inadvertently locked their keys in the car they can make one call to On-Star and the doors to their vehicle would be unlocked.

In another application of RFID technology, Delta Airlines sends passengers real-time information about the location of their checked baggage. In 2016 Delta began sending fliers who check bags mobile notifications as bags are loaded onto and off of airplanes and when they arrive at carousels for pickup. By embedding RFID chips in each luggage tag, Delta has achieved an eye-popping 99.9% tracking success rate, according to the company. “In the same way that customers want information at their fingertips about flight changes, we know our customers want clear visibility to their checked bags,” says Tim Mapes, Delta’s chief marketing officer^[1].

Biological Data. If you have a newer smartphone, then you may be able to unlock your phone by simply looking at the screen. This is made possible by facial recognition software. Unlocking your laptop with your fingerprint is another example of biological data available to businesses. Although things like voice and face recognition, retinal scans and biometric signatures are currently used primarily for security purposes, it may be possible in the future for this type of data to allow for product and service customization.

Public Data. Businesses have an almost endless source of data available to them free from public sources. Whenever you log onto the Internet, use instant messaging, or send emails, an electronic footprint is left behind. For now this data is considered to be “public” and businesses collect, share and even sell this type of data every day. This has become a very controversial topic in the past several years and recent legislation by the EU regarding this type of data may be the first step in limiting the collection and use of this type of public data. For additional information on this groundbreaking legislation follow this link to the European Commission: European Commission and Data Protection

Personal Data. Much like data that is considered to be “public” data, as we use technology we provide a wealth of personal data that businesses can use to reveal much about our personal preferences, habits, pastimes, likes and dislikes. For example, Facebook uses information people provide — such as their age, gender and interests — to target ads to a specific audience. Advertisers tell Facebook which demographics they want to reach, and then the social media giant places the ads on related accounts. How businesses collect and use this data is also highly controversial as exemplified by recent disclosures that Facebook has been collecting and selling personal information gathered from subscribers’ activities on the social network. Much like the controversy surrounding publicly available data, what rights an individual has to his or her data is currently being debated globally.

The volume of data available to businesses continues to increase exponentially and as more and more data becomes available collecting, storing and analyzing that data becomes increasingly complex. This data explosion has made data warehousing and data mining of greater importance to businesses.

Kang, Ashton. "Delta Introduces Innovative Baggage Tracking Process." Delta News Hub. April 28, 2016. Accessed June 25, 2019. https://news.delta.com/delta-introduces-innovative-baggage-tracking-process-0. ↵

4. Data Mining and Warehousing

LEARNING OUTCOMES

Explain the difference between data mining and data warehousing

Billions and billions of bits of data flood into an organization’s information system, but how does that data get utilized effectively? The challenge lies not so much with the collection or storage of the data: today, it is possible to collect and even store vast amounts of information relatively cheaply. The main difficulty is figuring out the best and most efficient way to extract and manage the relevant data. In this section you will learn how organizations not only warehouse but then mine the data they collect.

Did you ever think about how much data you yourself generate? Just remember what you went through to start college. First, you had to fill out application forms asking you about test scores, high school grades, extracurricular activities, and finances, plus demographic data about you and your family. Once you’d picked a college, you had to supply data on your housing preferences, the curriculum you wanted to follow, and the party who’d be responsible for paying your tuition. When you registered for classes, you gave more data to the registrar’s office. When you arrived on campus, you gave out still more data to have your ID picture taken, to get your computer and phone hooked up, to open a bookstore account, and to buy an on-campus food-charge card. Once you started classes, data generation continued on a daily basis: your food card and bookstore account, for example, tracked your various purchases, and your ID tracked your coming and going all over campus. And you generated grades.

And all these data apply to just one aspect of your life. You also generated data every time you used your credit card and your cell phone. Who uses all these data? How are they collected, stored, analyzed, and distributed in organizations that have various reasons for keeping track of you?

Warehousing and Mining Data

How do businesses organize all of this data so that they can transform it into useful information? For most businesses this is where data warehousing comes into play. A data warehouse collects data from multiple sources (both internal and external) and stores the data to later be used in an analysis. The primary purpose of a data warehouse is to store the data in a way that it can later be retrieved for use by the business. Despite the name, Data Mining is not the process of getting specific pieces of data out of the data warehouse, but rather the goal of data mining is the identification of patterns and knowledge from large amounts of data. Large retailers such as WalMart and Target track sales on a minute-by-minute basis and data mining allows these large retailers to recognize changes in purchasing behavior in an extremely short amount of time. They can quickly make adjustments to inventory levels based on the information gathered from thousands of individual transactions as a result of data mining. Clearly understanding consumer behavior is a primary goal of data mining. The following video explains just how businesses use data mining to understand and predict consumer behavior.

You can view the transcript for “DATA MINING | The Checkout | ABC1” (opens in new window).

Today businesses are treating the Internet as a massive data warehouse and are using data mining techniques to gather data about not just existing customers, but potential customers. Data mining tools such as Scrapy, Nutch and Splash allow businesses to learn more about customers, competitors, compare prices and even find new customers and sales targets. As the quantity of data businesses can collect continues to grow, having an effective data warehousing system that can be easily mined has become increasingly critical to business success.

5. Information and Business

LEARNING OUTCOMES

Explain how businesses use information.

We can summarize how businesses use information by saying, “businesses use information to gain a competitive advantage.”

Simply put a competitive advantage is what makes a business’s goods or services superior to all of a customer’s other choices. Internally, however, we can examine closer how information is used in both primary and support activities within the business.

Information and Primary Business Activities

The primary activities are the functions that directly impact the creation of a product or service. The goal of the primary activities is to add more value than they cost. The primary activities are:

Inbound logistics: These are the functions performed to bring in raw materials and other needed inputs. Information can be used here to make these processes more efficient, such as with supply-chain management systems, which allow the suppliers to manage their own inventory.

Operations: Any part of a business that is involved in converting the raw materials into the final products or services is part of operations. From manufacturing to business process management, information can be used to provide more efficient processes and increase innovation through flows of information.

Outbound logistics: These are the functions required to get the product out to the customer. As with inbound logistics, information can be used here to improve processes, such as allowing for real-time inventory checks.

Sales/Marketing: The functions that will entice buyers to purchase the products are part of sales and marketing. Information is critical to every aspect of sales and marketing. From online advertising to online surveys, information can be used to innovate product design and reach customers like never before. The company website can be a sales channel itself as we have seen with Amazon.

Service: The functions a business performs after the product has been purchased to maintain and enhance the product’s value are part of the service activity. Service can be enhanced via technology as well, including support services through websites and mobile apps.

Information and Support Activities

The support activities are the functions in an organization that support, and cut across, all of the primary activities. The support activities are:

Firm infrastructure: This includes organizational functions such as finance, accounting, and quality control, all of which depend on information; the use of ERP systems is a good example of the impact that information can have on these functions.

Human resource (HR) management: This activity consists of recruiting, hiring, and other services needed to attract and retain employees. Using the Internet, HR departments can increase their reach when looking for candidates. There is also the possibility of allowing employees to use technology for a more flexible work environment.

Procurement: The activities involved in acquiring the raw materials used in the creation of products and services are called procurement. Business-to-Business e-commerce can be used to improve the acquisition of materials.

This brief analysis sheds some light onto how businesses can use information to gain a competitive advantage. As you can see, the use of information cuts across the entire organization. Although the uses may vary from area to area one thing that is consistent is the use of accurate, timely information can improve business processes and thereby enhance the customer experience.

When the customer experience is enhanced, revenues rise, profits rise and business flourishes. Information is quickly becoming the lifeblood of business and its importance in the long-term success of an organization cannot be overstated.