11 May Chapter One Case: Do You Trust Your Data? Explain how bad data will impact information, business intelligence, and knowledge.
Chapter One Case: Do You Trust Your Data?
Data is the new oil. Data drives fact-based decisions. As a manager, you are going to rely on data to drive your business decisions. Can you imagine making a critical business decision on bad data? Have you ever stopped to ask yourself if you trust your data? What will happen if you make a business decision on incorrect, inaccurate, or low-quality data? Obviously, chances are high you will make the wrong decision, and that is the primary risk when using data to drive your decisions. Here are a few examples of organizations that fell into the trap of making important decisions on incorrect data.
■Fidelity: A missing negative sign on a dividend report cost this financial company $2.6 billion.
■Harvard: Two professors reached an incorrect conclusion with an average formula that failed to pull all of the data.
■London Olympics: An accidental typo of 20,000 instead of 10,000 caused the sale of 10,000 additional tickets for the synchronized swimming event.
■MI5: The British intelligence agency accidentally bugged more than 1,000 wrong telephones based on a formatting error on a spreadsheet.
■TransAlta: This Canadian power company made a simple cut-and-paste error for buying power at the wrong price, which cost it $24 million.
■University of Toledo: A typo in a spreadsheet formula led to an overestimate of enrollment, overinflating revenue by $2.4 million. 3
There is a famous saying in the tech industry: “Garbage in is garbage out” (GIGO). I can be the greatest data analyst in my company, but if the data I am analyzing is wrong, then my analysis will be wrong. But many of us forget to ask about the quality of our data, and we respond too quickly and confidently. There is a common statistic stating that over 80 percent of spreadsheets have errors. Why are there so many errors in spreadsheets? It is simple. Spreadsheets are created by people and people make mistakes! It is important to remember that you should never assume that you have high-quality data. You should always do the upfront work to verify the quality of your data. This will require a great deal of work before you even begin your analysis but can pay off tremendously as you make decisions with greater confidence.
Bad data is costly. With data driving so many decisions in our lives, the cost of bad data truly impacts us all, whether or not we realize it. IBM estimates that bad data costs U.S. businesses over $3 trillion yearly. Most people who deal with data realize that bad data can be extremely costly, but page 22this number is truly stunning. The majority of businesses analyze customer data, but there is little chance of the business succeeding if the data is wrong.
Why do you believe data can be inaccurate?
What can a business do to ensure data is correct?
Explain how bad data will impact information, business intelligence, and knowledge.
Have you ever made a decision based on bad data? If so, be sure to share it with your peers and explain how you could have verified the data quality.
Argue for or against the following statement: “It is better to make a business decision with bad data than with no data.”
* LEARNING OUTCOME REVIEW
1.1Describe the information age and the differences among data, information, business intelligence, and knowledge.
We live in the information age, when infinite quantities of facts are widely available to anyone who can use a computer. The core drivers of the information age include data, information, business intelligence, and knowledge. Data are raw facts that describe the characteristics of an event or object. Information is data converted into a meaningful and useful context. Business intelligence (BI) is information collected from multiple sources such as suppliers, customers, competitors, partners, and industries that analyzes patterns, trends, and relationships for strategic decision making. Knowledge includes the skills, experience, and expertise, coupled with information and intelligence, that create a person’s intellectual resources. As you move from data to knowledge, you include more and more variables for analysis, resulting in better, more precise support for decision making and problem solving.
1.2Explain systems thinking and how management information systems enable business communications.
A system is a collection of parts that link to achieve a common purpose. Systems thinking is a way of monitoring the entire system by viewing multiple inputs being processed or transformed to produce outputs while continuously gathering feedback on each part. Feedback is information that returns to its original transmitter (input, transform, or output) and modifies the transmitter’s actions. Feedback helps the system maintain stability. Management information systems (MIS) is a business function, such as accounting and human resources, that moves information about people, products, and processes across the company to facilitate decision making and problem solving. MIS incorporates systems thinking to help companies operate cross-functionally. For example, to fulfill product orders, an MIS for sales moves a single customer order across all functional areas, including sales, order fulfillment, shipping, billing, and finally customer service. Although different functional areas handle different parts of the sale, thanks to MIS, to the customer the sale is one continuous process.
* REVIEW QUESTIONS
What is data? Why is data important to a business?
How can a manager turn data into information?
What is the relationship between data, information, business intelligence, and knowledge?
Why is it important for a company to operate cross-functionally?
What is MIS, and what role does it play in an organization?
Do you agree that MIS is essential for businesses operating in the information age? Why or why not?
What type of career are you planning to pursue? How will your specific career use data, information, business intelligence, and knowledge?
How does system thinking support business operations?
What are the three types of analytics?
What is the difference between a knowledge facilitator and knowledge assets?
* MAKING BUSINESS DECISIONS
1.View from a Flat World
Bill Gates, founder of Microsoft, stated that 20 years ago most people would rather have been a B student in New York City than a genius in China because the opportunities available to students in developed countries were limitless. Today, many argue that the opposite is now true due to technological advances making it easier to succeed as a genius in China than a B student in New York. As a group, discuss whether you agree or disagree with Bill Gates’s statement.
2.Is Technology Making Us Dumber or Smarter?
There are numerous articles on how Facebook can make you dumber and Twitter can impede your ability to make sound decisions. Do you believe technology is making humankind dumber? Choose a side and debate the following:
Side A Living in the information age has made us smarter because we have a huge wealth of knowledge at our fingertips whenever or wherever we need it.
Side B Living in the information age has caused people to become lazy and dumber because they are no longer building up their memory banks to solve problems; machines give them the answers they need to solve problems.
3.The Internet of Everything Is Everywhere
IoT is transforming our world into a living information system as we control our intelligent lighting from our smartphone and perform a daily health check from our smart toilet. Of course, with all great technological advances come unexpected risks, and you have to be prepared to encounter various security issues with IoT. Just imagine if your devices were hacked by someone who now can shut off your water, take control of your car, or unlock the doors of your home from thousands of miles away. We are just beginning to understand the security issues associated with IoT and M2M, and you can be sure that sensitive data leakage from your IoT devices is something you will most likely encounter in your life.
In a group, identify a few IoT devices you are using today. These can include fitness trackers that report to your iPhone, sports equipment that provides immediate feedback to an app, or even smart vacuum cleaners. If you are not using any IoT devices today, brainstorm a few you might purchase in the future. How could a criminal or hacker use your IoT to steal your sensitive data? What potential problems or issues could you experience from these types of data thefts? What might be some of the signs that someone had accessed your IoT data illegally? What could you do to protect the data in your device?
4.Working for the Best
Each year, Fortune magazine creates a list of the top 100 companies to work for. Find the most recent list. What types of data do you think Fortune analyzed to determine the company ranking? What issues could occur if the analysis of the data was inaccurate? What types of information can you gain by analyzing the list? Create five questions a student performing a job search could answer by analyzing this list.
5.Garbage in Is Garbage out
Many businesses fall into the trap of believing data even when their knowledge or common sense tells them the data is wrong. Studies conducted over decades have found that an alarming 88 percent of spreadsheets suffer from some type of error. Here are a few examples:
■London Olympics: A swimming event was oversold when a member of the staff made a single keystroke mistake and entered 20,000 remaining tickets into a spreadsheet instead of 10,000, the actual number of remaining tickets.
■page 25TIBCO Software Company: A spreadsheet error misstated the number of outstanding shares, causing the value of the company to be overstated by $100 million during its acquisition.
■Kodak: The payment of a $11 million severance package to an employee was the result of a faulty spreadsheet.
According to experts and academics who have researched spreadsheet effectiveness, three primary types of errors typically occur in spreadsheet models.
Mechanical error: Arises from mistakes in typing, cutting and pasting, or other simple manual operations. While a mechanical error may at first appear minor, incorrectly entered data can affect the integrity of an entire model. Furthermore, planning models tend to grow in size and complexity as available computing power increases. As the models grow, the errors created within them increase in both number and severity.
Logic error: An inappropriate algorithm is chosen or inappropriate formulas are created to implement the algorithm. The resulting flawed calculations affect not only the worksheet in which the error appears but the entire model as well.
Omission error: Critical components are left out of a model entirely. Errors of omission are hard to identify. As you work through large spreadsheets, the likelihood is great that a critical item will simply not be inserted, and its absence will not be noticed.
Review the list of spreadsheet errors above and rank them in order of easiest to hardest to identify and fix. Have you ever encountered a problem in your life due to a data error? What did you do to solve the problem? How did you find the error? What can you do to ensure you do not fall into the trap of believing the data over your own knowledge?
6. Categorizing Analytics
The three techniques for business analytics include descriptive analytics, predictive analytics, and prescriptive analytics. For each of the below examples, determine which analytical technique is being used.
Review the Case Study in Chapter 1: Do You Trust Your Data?
After reviewing the case, answer the following questions. Be sure to use outside resources and your textbook to validate your responses.
Why do you believe that data can be inaccurate?
What can a business do to ensure data is correct?
Explain how bad data will impact information, business intelligence, and knowledge.
Have you ever made a decision based on bad data? If so, please share how you could have verified the data quality.
Argue for or against the following statement “It is better to make a business decision with bad data than with no data”.
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