Rephrase Tufs

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Running head: CASE STUDY ABOUT TUFS 1

CASE STUDY ABOUT TUFS 2

 

Should Northern have invested in TUFS?

The Northern insurance company investment in the Technical Underwriting Financial System (TUFS) is controversial. The company had identified the potential of the system to have the positive impact of the anticipated benefits, but it failed to have the proper plan of implementing the idea. The Northern insurance company should not have had the hurry in the investing in the Technical Underwriting Financial System (TUFS) with the failure to plan to get the system deployment within the company to meet the anticipated benefits. The company was not ready to invest in the system and push to get the undertaking of projects within the company got the implementation of an application that saw the company make some loses. The idea of getting to develop or rather have an investment should get the concerned group through the undertaking of the initiative to assess the business opportunity for all articulated areas that the investment project will get to address and have the major effect. The investment in the Technical Underwriting Financial System (TUFS) would have had the Northern insurance company stand the chance of boosting the company underwriting process and the further way of the acquisition of the e-business capabilities. The Northern Insurance Company current system takes to provide the same mentioned anticipated actions to get to benefit from the Technical Underwriting Financial System (TUFS). From the basis of operations, the Northern insurance company staff takes to apply still the current system for the underwriting and could only apply the Technical Underwriting Financial System (TUFS) for clarification purposes. It gets to mean that the features of the TUFS are similar to the capabilities of the current system. It then projects out that the Northern insurance company should not have had the intention to invest in the application because the projected benefits were not defined and on the implementation of the application, it gets shown out clearly how useless the system is to the company. The phase of analysis could have seen the through conduct of feasibility relating to the company requirements and the capabilities of the system to provide the better understanding of identifying the appropriate system to get invested (Ardagna, 2012).

What Went Wrong With The TUFS Investment And What Can Be Done To Prevent These Problems In The Future?

The TUFS investment failed to show the potential opportunity for the Northern insurance company to invest as of the reasons from the beginning of the understanding of the case, the failure to define the requirements for the company. There was no feasibility undertaken for identification of the requirements that gets to define the issues that the system to get invested was to address. The project development team got too many assumptions relating to the Technical Underwriting Financial System (TUFS) and the intended benefit of the system to the company. The clear definition of requirements provides a clear understanding of the development team to design the correct system of which the company project team failed to get the task at home (Sage, & Rouse, 2009).

During the implementation of the Technical Underwriting Financial System (TUFS), the development team terribly failed to involve the stakeholders in the development process. It should get understood that stakeholder’s participation in the system development is crucial as they are the end decision makers within the company and furthermore are the financier of the project. They could have had the contribution to the determination of the requirements, and they could be willing to continue with the funding till the system gets shaped.

The inadequacy of the technical operation abilities and experience plus the skills of the staff had the failure to apply the Technical Underwriting Financial System (TUFS) within the company. The employees had no motivation to the use of the system since they had less knowledge and the understanding of the system operation. During the implementation of the Technical Underwriting Financial System (TUFS), there was no training conducted to have the staff get to learn on the application of the system, and this got the staff turning down the usage of the application.

The unrealistic expectation definition and the failure of involving the essential parties in the development and implementation of the application within the company were another prominent reason for the failure of the system. The staff thought the system was to ease their work instead the application was complicated only applied for the clarification of the task undertaken by the current system. The failure to involve the essential parties got the staff to lack the knowledge of the application of the system and the errors related to the system were to be human-oriented. The system might meet the available expectations as per its capability, and the exaggerations get the management in doubt of the system meeting the defined capabilities.

The mitigation should get the conduct training for the staff and the entire company stakeholders about the benefits of the system to have the support and the positive attitude towards the use of the application. According to Pieterse (2012), the company should get to involve the stakeholders in all activities relating to the Technical Underwriting Financial System (TUFS) investment project. The changing of the company management should be the other option in the case of the current failing to show the competency. Also, the company should get to fill the loops within the system by revisiting and working on the steps skipped during the development of the application.

What Does Northern Need to Do to Realize the Benefits That get Projected For TUFS?

The Northern Company should get to have the involvement of the application users in the implementation of the system. The process should get the conduct of training to the employees to have the better understanding and the skills plus the experience of using the system. The company should once more get to have the collaboration from the all respective departments within the company promotion to the application of the system, and this should also get to involve the shift to the system that the rely on the current system. The modules of the system should get defined clearly for the clear understanding of the capabilities that the system can get to meet. The expectations of the systems should get set accordingly, and a proper plan should get defined for the implementation of the system within the company. The success will depend on the application of the professional techniques of implementing the system and to some extend the technical support provision.

How Can They Measure These Benefits?

The measure of the benefits can get defined by the end user satisfaction to the application of the system for the undertaking of the defined tasks. The assessment of the system application relation skills and attitude by the audits of the end users, employees, and other stakeholder gets to give the measuring framework of the system benefits (Kellyne, 2006). The system impact to the overall company system will get to define the success and the failure of it within the company. The growth in the company business operations provides evidence to the positive impact of the system on the company, and this can get used as a measure of benefits related to the Technical Underwriting Financial System (TUFS). Also, the consideration of the system end user adoption may act as a primary system benefits measures. Furthermore, the other related measures may be the delivery of quality service, the meeting of the Northern insurance company governance criteria. (Remenyi, Money, Bannister, & Remenyi, 2007).

References

Ardagna, C. A. (2012). Business system management and engineering: From open issues to applications. Berlin: Springer.

http://www.ittoolkit.com/how-to-it/operations/end-user-training.html

Kellyne, W. (2006). The Learning Impact Measurement Framework (Nov 06). Media Tec Publishing Inc.

Pieterse, M.; (2012), Critical success factors in information technology projects; UJ Library and Information Centre.

Remenyi, D., Money, A. H., Bannister, F., & Remenyi, D. (2007). The effective measurement and management of ICT costs and benefits. Oxford: CIMA.

Sage, A. P., & Rouse, W. B. (2009). Handbook of systems engineering and management. Hoboken, N.J: John Wiley & Sons.

 
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I Need This Assignment Donw Today…Can Anyone Proficient With Word Complete This?

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Transitioning from College to Career

Courtesy of the College Plaacement Office

Most seniors, although excited about graduation, are seriously concerned about finding and starting that first post college job. The reality of leaving the collegiate environment and role of student to enter the business world and becoming a productive employee can be stressful. There are several issues that are key to making a successful transition:

Time Management

Job versus Career

Professionalism on the Job

A Current and Accurate Resume

Recognize that Grads get Entry Level Jobs

Understanding these key issues and trying to face them before leaving college will make your post college expectations more realistic.

Time Management

The scheduling of classes after noon or only in the evening might not have prepared you for the eight to five (or later) hours of the business world. You can’t hit the snooze on your alarm and show up to work forty-five minutes late. Additionally the long weekends and college breaks don’t exist in most work environments. Vacation is accumulated and not a given for every holiday or snow day that occurs. In addition to your longer hours at work you will have to manage a social life. The days of staying up till three in the morning won’t work when you have to be at work by eight. If you are employed and several of your friends are still in college you might have to learn to say no to events that could be considered unprofessional or prevent you from performing your job in a professional manner the next day. Understanding these time management issues and considering them before their encounter will help in the transition. Time management suggestions include:

Getting sufficient sleep

Arriving to work early

Avoid taking unnecessary time off

Job versus Career

Your first job might not be the dream job that you envisioned obtaining after four long years of college. Many first year grads will change their job in the first two years. What is important is that you use this first job to figure what you really want to do. Some degrees are more flexible allowing a wider range of possible career paths. Others, like engineering, are very specific. Look for a position that you feel will match your academic and personal skills.

Professionalism on the Job

In college a certain amount of irresponsibility is the right of passage. The result might be a lecture from a professor or a bad grade. In the business world irresponsibility often results in being fired. You need to be dependable and a self-starter to succeed in most careers. As a team member, you need to be able to be relied upon to contribute, meet deadlines, and accurately assess the contributions of other team members. In most business environments missing a deadline is unacceptable.

A Current and Accurate Resume

Looking for a job can be a full time job in itself. All resources need to be used and all leads followed. A resume for a current graduate should not be more than one page. Often the student tries to oversell his/her qualifications and leave the employer unimpressed. Your skills listed in your resume should be edited for each job that you are applying for and should indicate how this skill will contribute to the employer’s benefit. Stating that you were captain of the debate team is simply not enough to impress a future employer. Stating that the skills obtained as captain, such as organizing practices, selecting debate issues, and being a liaison between the faculty advisor, college administration, and other collegiate debate captains, says a lot more. Remember these five resume tips:

Keep your resume to one page

Edit your skills for each application

Use an easy to read font

Spell and grammar check

Make sure that your address and phone number are accurate

Recognize that Grads get Entry Level Jobs

Be realistic in your job expectations. In a bad economy, many jobs for college graduates are entry level and require long hours, lower than expected pay, and hard work. Don’t walk away from a job offer because one part of the job description does not appeal to you. Consider the entire package and the potential to move beyond this entry level position.

Let the College Plaacement Office Help You!

 
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Python

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Name Search

If you have downloaded the source code from this book’s companion Web site, you will find the following files in the Chapter 07 folder:

• GirlNames.txt—This file contains a list of the 200 most popular names given to girls born in the United States from the year 2000 through 2009.

• BoyNames.txt—This file contains a list of the 200 most popular names given to boys born in the United States from the year 2000 through 2009.

Write a program that reads the contents of the two files into two separate lists. The user should be able to enter a boy’s name, a girl’s name, or both, and the application will display messages indicating whether the names were among the most popular.

I uploaded these text files in this module covering managing lists in Python.  Additionally, you can download them from the hyperlinks listed below

 

 

boys names

Jacob Michael Joshua Matthew Daniel Christopher Andrew Ethan Joseph William Anthony David Alexander Nicholas Ryan Tyler James John Jonathan Noah Brandon Christian Dylan Samuel Benjamin Zachary Nathan Logan Justin Gabriel Jose Austin Kevin Elijah Caleb Robert Thomas Jordan Cameron Jack Hunter Jackson Angel Isaiah Evan Isaac Mason Luke Jason Gavin Jayden Aaron Connor Aiden Aidan Kyle Juan Charles Luis Adam Lucas Brian Eric Adrian Nathaniel Sean Alex Carlos Bryan Ian Owen Jesus Landon Julian Chase Cole Diego Jeremiah Steven Sebastian Xavier Timothy Carter Wyatt Brayden Blake Hayden Devin Cody Richard Seth Dominic Jaden Antonio Miguel Liam Patrick Carson Jesse Tristan Alejandro Henry Victor Trevor Bryce Jake Riley Colin Jared Jeremy Mark Caden Garrett Parker Marcus Vincent Kaleb Kaden Brady Colton Kenneth Joel Oscar Josiah Jorge Cooper Ashton Tanner Eduardo Paul Edward Ivan Preston Maxwell Alan Levi Stephen Grant Nicolas Omar Dakota Alexis George Collin Eli Spencer Gage Max Cristian Ricardo Derek Micah Brody Francisco Nolan Ayden Dalton Shane Peter Damian Jeffrey Brendan Travis Fernando Peyton Conner Andres Javier Giovanni Shawn Braden Jonah Cesar Bradley Emmanuel Manuel Edgar Erik Mario Edwin Johnathan Devon Erick Wesley Oliver Trenton Hector Malachi Jalen Raymond Gregory Abraham Elias Leonardo Sergio Donovan Colby Marco Bryson Martin

 

girls names

Emily Madison Emma Olivia Hannah Abigail Isabella Samantha Elizabeth Ashley Alexis Sarah Sophia Alyssa Grace Ava Taylor Brianna Lauren Chloe Natalie Kayla Jessica Anna Victoria Mia Hailey Sydney Jasmine Julia Morgan Destiny Rachel Ella Kaitlyn Megan Katherine Savannah Jennifer Alexandra Allison Haley Maria Kaylee Lily Makayla Brooke Mackenzie Nicole Addison Stephanie Lillian Andrea Zoe Faith Kimberly Madeline Alexa Katelyn Gabriella Gabrielle Trinity Amanda Kylie Mary Paige Riley Jenna Leah Sara Rebecca Michelle Sofia Vanessa Jordan Angelina Caroline Avery Audrey Evelyn Maya Claire Autumn Jocelyn Ariana Nevaeh Arianna Jada Bailey Brooklyn Aaliyah Amber Isabel Danielle Mariah Melanie Sierra Erin Molly Amelia Isabelle Madelyn Melissa Jacqueline Marissa Shelby Angela Leslie Katie Jade Catherine Diana Aubrey Mya Amy Briana Sophie Gabriela Breanna Gianna Kennedy Gracie Peyton Adriana Christina Courtney Daniela Kathryn Lydia Valeria Layla Alexandria Natalia Angel Laura Charlotte Margaret Cheyenne Mikayla Miranda Naomi Kelsey Payton Ana Alicia Jillian Daisy Mckenzie Ashlyn Caitlin Sabrina Summer Ruby Rylee Valerie Skylar Lindsey Kelly Genesis Zoey Eva Sadie Alexia Cassidy Kylee Kendall Jordyn Kate Jayla Karen Tiffany Cassandra Juliana Reagan Caitlyn Giselle Serenity Alondra Lucy Kiara Bianca Crystal Erica Angelica Hope Chelsea Alana Liliana Brittany Camila Makenzie Veronica Lilly Abby Jazmin Adrianna Karina Delaney Ellie Jasmin

 
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Physical Evidence Paper

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You are on a team of crime scene investigators. Your team was instructed to collect the physical evidence at a crime scene. Arriving at the crime scene your team observes the following:

 

  • Shell casings
  • Three sets of footprints (two muddy sets and one bloody set) throughout the house
  • Bloody fingerprints
  • Tire tracks by the side entrance of the house

 

Write a 1,050- to 2,100-word paper that includes the following:

 

  • Identify the various types of physical evidence encountered at the crime scene.
  • Describe the preservation and collection of the firearms evidence.
  • Describe the preservation and collection of fingerprints, footprints and tire tracks.
  • Describe the legal issues regarding physical evidence encountered at the crime scene.
  • Identify the significance of physical evidence.

 

Include information learned this week from the MyCrimeKit Virtual Lab activities in your paper.

Format your paper consistent with APA guidelines. Remember to cite and list your source material.

 
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Word Document Assignment

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  1. Open the EmergencyProcedures-02.docx start file. If the document opens in Protected View, click the Enable Editing button so you can modify it.
  2. The file will be renamed automatically to include your name. Change the project file name if directed to do so by your instructor, and save it.
  3. Change the theme to Integral and the theme color to Red.
  4. Change the top, bottom, left, and right margins to 0.75″.
  5. Select the entire document and change the font size to 12 pt.
  6. Format the title of the document.
    1. Select the title of the document and apply Heading 1 style.
    2. Open the Font dialog box, apply All caps effect, and change the font size to 16 pt.
    3. Change the Before paragraph spacing to 0 pt.
    4. Add a bottom border to the title using the Borders drop-down list.
  7. Apply and modify the Heading 2 style and delete blank lines.
    1. Apply the Heading 2 style to each of the bold section headings.
    2. Select the first section heading (“Emergency Telephones [Blue Phones]”).
    3. Change Before paragraph spacing to 12 pt. and After paragraph spacing to 3 pt.
    4. Apply small caps effect.
    5. Update Heading 2 style to match selection. All the section headings are updated.
    6. Turn on Show/Hide and delete all the blank lines in the document.
  8. Select the bulleted list in the first section and change it to a numbered list.
  9. Apply numbering format and formatting changes, and use the Format Painter.
    1. Apply numbering to the text below the section headings in the following sections: “Assaults, Fights, or Emotional Disturbances”; “Power Failure”; “Fire”; “Earthquake”; and “Bomb Threat.”
    2. Select the numbered list in the “Bomb Threat” section.
    3. Open the Paragraph dialog box, set Before and After paragraph spacing to 2 pt., deselect the Don’t add space between paragraphs of the same style check box, and click OK to close the dialog box.
    4. Use the Format Painter to copy this numbering format to each of the other numbered lists.
    5. Reset each numbered list so it begins with 1 (right-click the first item in each numbered list and select Restart at 1 from the context menu).
  10. Customize a bulleted list and use the Format Painter.
    1. Select the text in the “Accident or Medical Emergency” section.
    2. Create a custom bulleted list and use a double right-pointing triangle symbol (Webdings, Character code 56).
    3. Open the Paragraph dialog box and confirm the left indent is 0.25″ and hanging indent is 0.25″. If not, change the settings.
    4. Set Before and After paragraph spacing to 2 pt. and deselect the Don’t add space between paragraphs of the same style check box.
    5. Use the Format Painter to apply this bulleted list format to the following text in the following sections: “Tips to Professors and Staff” and “Response to Students.”
  11. Change indent and paragraph spacing and apply a style.
    1. Select the text below the “Emergency Telephone Locations” heading.
    2. Set a 0.25″ left indent.
    3. Set Before and After paragraph spacing to 2 pt.
    4. Confirm the Don’t add space between paragraphs of the same style box is unchecked (Paragraph dialog box).
    5. Apply Book Title style to each of the telephone locations in the “Emergency Telephone Locations” section. Select only the location, not the text in parentheses or following text.
  12. Change left indent and paragraph spacing and set a tab stop with a dot leader.
    1. Select the text below the “Emergency Phone Numbers” heading.
    2. Open the Paragraph dialog box and set a 0.25″ left indent for this text.
    3. Set Before and After paragraph spacing to 2 pt. and confirm the Don’t add space between paragraphs of the same style box is unchecked.
    4. Open the Tabs dialog box, set a right tab stop at 7″, and use a dot leader (2).
    5. Press Tab before the phone number (after the space) on each of these lines. The phone numbers align at the right margin with a dot leader between the text and phone number.
  13. Apply the Intense Reference style to the paragraph headings in the “Accident or Medical Emergency” section (“Life-Threating Emergencies” and “Minor Emergencies”). Include the colon when selecting the paragraph headings.
  14. Use the Replace feature to replace all instances of “Phone 911” with “CALL 911” with bold font style. Note: If previous Find or Replace criteria displays in the Replace dialog box, remove this content before performing this instruction.
  15. Insert a footer with document property fields and the current date that appears on every page.
    1. Edit the footer on the first page and use the ruler to move the center tab stop to 3.5″ and the right tab stop to 7″.
    2. Insert the Title document property field on the left. Use the right arrow key to deselect the document property field.
    3. Tab to the center tab stop and insert the Company document property field at center. Use the right arrow key to deselect the document property field.
    4. Tab to the right tab stop, insert (not type) the date (use January 1, 2020 format), and set it to update automatically.
    5. Change the font size of all the text in the footer to 10 pt.
    6. Add a top border to the text in the footer using the Borders drop-down list and close the footer.
  16. Use the Borders and Shading dialog box to insert a page border on the entire document.
    1. Use Shadow setting and solid line style.
    2. Select the fifth color in the first row of the Theme Colors (Dark Red, Accent 1) and 1 pt. line width.
  17. Center the entire document vertically (Hint: use the Page Setup dialog box).
  18. View the document in Side to Side page movement view [View tab, Page Movement group] and then return to Vertical page movement view.
 
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Please Make Sure You Follow The Template STEP BY STEP. Thank You

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Lab Deliverable for Lab n

Your Name

Date

Title: Creating, Using, Removing System Restore Points for Windows 8.1

Operating Environment:

1. Operating System: Windows 8.1 Pro

2. Hardware

3. Software

Description:

 

Notes, Warnings, & Restrictions:

Resources (Further Reading):

Procedures:

[First Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

[Second Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

 

[Last Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

 

 

 

Title:

Operating Environment:

1. Hardware

2. Software

Description:

 

Notes, Warnings, & Restrictions:

Resources (Further Reading):

Procedures:

 

[First Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

[Second Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

 

[Last Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

Title:

Operating Environment:

1. Hardware

2. Software

Description:

 

Notes, Warnings, & Restrictions:

Resources (Further Reading):

Procedures:

[First Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

[Second Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

 

[Last Section Heading & Brief Intro / Explanation]

[Step-by-Step]

 

1

 
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Microsoft Access Work

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IfSuccessful_Status GA_Status_Icon SAM_Logo
true
false
ID FirstName LastName AssignmentGUID UserID
false Paulette West {01121CA1-7441-4AA2-A108-DA94031C3B66} {01121CA1-7441-4AA2-A108-DA94031C3B66}
ID FirstName LastName ProjectName SubmissionNum MaxScore Score EngineVersion
ID StepNumber Description IfSuccessful StepScore StepMaxScore ErrorText ActionName StepActionOrder
Category Description
Adventure Adventure experiences include skiing, scuba, biking, hiking, and climbing tours.
Eco Eco experiences work on environmental or ecological projects.
CustNo FName LName Street City State Zip Phone FirstContact
1 Mindi Scott 52411 Oakmont Rd Kansas City MO 64144 5554441234 Friend
2 Jacob Alman 2505 McGee St Waukee IA 50288 5551116931 Friend
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4 Jane Taylor 8206 Marshall Dr Lenexa KS 66214 5552229101 Mail
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26 Robert Nelson 201 Birch St Overland Park KS 66206 5558884514 Internet
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30 Sharol Wood 2330 Shawnee Dr Westwood KS 66205 5553332434 Friend
31 Zohra Bell 506 Lowell St Point Lookout MO 65727 5556664404 Mail
32 Lisa Gomez 345 College Rd Ridgedale MO 65777 5557778504 Mail
33 Shirley Cruz 46 Maple Rd Hollister MO 65727 5551110403 Radio
34 Kori James 5234 Ash Rd Ridgedale MO 65777 5552228503 Radio
35 Jeanette Gray 4435 Main St Branson MO 65726 5552228773 Radio
36 Brad Long 123 Duck Creek Dr Johnston IA 50800 5553328888 Internet
37 Madison West 57 West 159th St Cushing PA 87087 5552887722 Radio
38 Nancy Cole 123 Duck Creek Dr Johnston IA 50800 5553328888 Internet
39 Tim Hayes 8206 Marshall Dr Lenexa KS 66214 5552229101 Mail
40 Hannah Hunter 66900 College Rd Overland Park KS 66210 5552225102 Radio
41 Marcus Mason 66900 College Rd Overland Park KS 66210 5552225102 Radio
42 Kris Shaw 900 Barnes St West Des Moines IA 50265 5556661324 Mail
43 Lois Gordon 900 Barnes St West Des Moines IA 50265 5556661324 Mail
44 Samuel Livingston 1551 Switzer St Gardner KS 66303 5556633366 Internet
45 Julie Livingston 1551 Switzer St Gardner KS 66303 5556633366 Internet
46 Aaron Wheeler 5989 Washington Ave Hollister MO 67827 5556766677 Internet
47 Kelsey Silva 7800 West 16th St Manhattan KS 66502 5556655522 Internet
48 Orlando Berry 7722 Mastin St Mission KS 63552 5558887722 Internet
49 Maria Armstrong 7722 Mastin St Mission KS 63552 5558887722 Internet
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98 8/8/20 36 44
99 8/11/20 38 44
100 8/11/20 42 46
101 8/12/20 43 46
102 8/12/20 20 45
103 8/13/20 21 45
104 8/14/20 44 3
105 8/15/20 45 3
106 8/15/20 46 3
107 8/15/20 47 3
StateName StateAbbreviation
Alaska AK
Alabama AL
Arkansas AR
Arizona AZ
California CA
Colorado CO
Connecticut CT
District of Columbia DC
Delaware DE
Florida FL
Georgia GA
Hawaii HI
Iowa IA
Idaho ID
Illinois IL
Indiana IN
Kansas KS
Kentucky KT
Louisiana LA
Massachusetts MA
Maryland MD
Maine ME
Michigan MI
Minnesota MN
Missouri MO
Mississippi MS
Montana MT
North Carolina NC
North Dakota ND
Nebraska NE
New Hampshire NH
New Jersey NJ
New Mexico NM
Nevada NV
New York NY
Ohio OH
Oklahoma OK
Oregon OR
Pennsylvania PA
Rhode Island RI
South Carolina SC
South Dakota SD
Tennessee TN
Texas TX
Utah UT
Virginia VA
Vermont VT
Washington WA
Wisconsin WS
West Virginia WV
Wyoming WY
TripNo TripName TripStartDate Duration City StateAbbrev Category Price
1 Stanley Bay Cleanup 7/4/20 3 Captiva FL Eco ¤ 750.00
2 Red Reef Cleanup 7/4/20 3 Islamorada FL Eco ¤ 1,500.00
3 Breckenridge Reconstruction 12/31/20 7 Breckenridge CO Eco ¤ 850.00
4 Boy Scout Project 8/1/20 7 Vail CO Eco ¤ 1,000.00
5 Bridgewater Country Study 8/5/20 10 Aspen CO Eco ¤ 2,000.00
6 Rocky Mountain Mission 8/9/20 3 Breckenridge CO Adventure ¤ 1,700.00
7 Monmouth Festival 11/10/20 7 Monmouth CO Eco ¤ 1,800.00
8 Great Fish Count 9/1/20 9 Denver CO Eco ¤ 700.00
9 Bikers for Ecology 9/10/20 5 Georgetown CO Adventure ¤ 800.00
10 Golden Hands Venture 5/21/20 4 Orlando FL Adventure ¤ 900.00
11 Hummer Trail Study 5/28/20 7 Key West FL Eco ¤ 1,250.00
12 Coastal Shore Cleanup 5/28/20 7 Captiva FL Adventure ¤ 1,000.00
13 High Adventurers 6/3/20 7 Ft. Lauderdale FL Adventure ¤ 1,400.00
15 Patriots in Disneyland 6/10/20 7 Orlando FL Adventure ¤ 1,500.00
17 Tropical Sailboat Voyage 6/17/20 7 Key West FL Adventure ¤ 1,450.00
18 Eagle Hiking Club 6/17/20 7 Aspen CO Adventure ¤ 1,500.00
19 Paradise Water Club 6/24/20 7 Fort Collins CO Adventure ¤ 1,400.00
20 Team Discovery 6/27/20 5 Breckenridge CO Adventure ¤ 1,200.00
21 Gulfside Birdwatchers 6/27/20 7 Tampa Bay FL Adventure ¤ 1,550.00
22 Perfect Waves Project 6/25/20 5 Huntington Beach CA Adventure ¤ 800.00
23 Outrigger Cleanup 6/27/20 7 Leadville CO Eco ¤ 1,000.00
24 Blue Canyon Youth Project 6/29/20 7 Breckenridge CO Adventure ¤ 1,400.00
25 Emmanuel Youth Club 6/29/20 7 Aspen CO Eco ¤ 900.00
26 Prairie Restoration Project 6/30/20 3 West Denver CO Eco ¤ 200.00
27 Butterfly House Construction 6/30/20 3 Denver CO Eco ¤ 400.00
28 Bass Habitat Project 6/30/20 4 Broomfield CO Eco ¤ 500.00
29 Silver Country Venture 7/11/20 14 Sacramento CA Adventure ¤ 3,500.00
30 Monterey Mysteries 7/12/20 7 Monterey CA Adventure ¤ 1,800.00
36 California Coastline Cleanup 8/22/20 7 San Diego CA Eco ¤ 1,200.00
37 Cactus Ecosystem 9/12/20 7 San Diego CA Eco ¤ 800.00
38 Water Education Foundation 9/19/20 14 Fresno CA Eco ¤ 1,300.00
39 Oakland Museum of Science 7/18/20 7 Oakland CA Eco ¤ 1,000.00
40 Redwood Forest Lab 9/27/20 14 Mill Valley CA Eco ¤ 1,500.00
41 Langguth Environment 10/17/20 10 Napa CA Eco ¤ 2,900.00
43 Japanese California Connection 8/17/20 4 Bolinas CA Eco ¤ 900.00
44 Bear Valley Adventures 8/17/20 3 Sacramento CA Adventure ¤ 800.00
45 Black Sheep Hikers 8/24/20 14 El Dorado Hills CA Adventure ¤ 3,000.00
46 Bigfoot Rafting Club 9/11/20 4 Placerville CA Adventure ¤ 850.00
47 Yosemite Park Cleanup 7/18/20 3 Sacramento CA Eco ¤ 1,250.00
48 Kings Canyon Bridge Builders 7/11/20 10 Three Rivers CA Eco ¤ 2,800.00
49 Golden State Tours 7/18/20 10 Sacramento CA Adventure ¤ 2,300.00
51 Mark Twain Forest Project 11/29/20 7 Branson CO Eco ¤ 1,200.00
52 Colorado Bald Eagle Watch Club 8/11/20 7 Estes Park CO Adventure ¤ 1,100.00
53 Convoy of Hope 12/31/20 14 San Diego CA Eco ¤ 1,000.00
54 Durango Wildfire Project 9/1/20 10 Durango CO Eco ¤ 1,400.00
55 Student Last Name Project 8/1/20 5 Boulder CO Eco ¤ 500.00
SELECT TourCategories.Category FROM TourCategories;
PARAMETERS __TripNo Value; SELECT DISTINCTROW * FROM CustomerSales AS CustomerRoster WHERE ([__TripNo] = TripNo);
SELECT States.StateAbbreviation, States.StateName FROM States;
SELECT DISTINCTROW * FROM States;
SELECT DISTINCTROW * FROM Trips;
SELECT Customers.CustNo, [LName] & “, ” & [FName] AS CustomerName FROM Customers ORDER BY [LName] & “, ” & [FName];
SELECT [States].[StateName], [States].[StateAbbreviation] FROM States ORDER BY [StateAbbreviation];
SELECT Sales.SalesNo, Customers.LName, Customers.FName, Trips.TripName FROM Trips INNER JOIN (Customers INNER JOIN Sales ON Customers.CustNo = Sales.CustNo) ON Trips.TripNo = Sales.TripNo ORDER BY Customers.LName, Customers.FName, Trips.TripName;
SELECT States.StateAbbreviation, States.StateAbbreviation, States.StateName FROM States ORDER BY States.StateName;
SELECT Categories.Category FROM Categories;
SELECT [Categories].[Category], [Categories].[Description] FROM Categories;
PARAMETERS __Category Value; SELECT DISTINCTROW * FROM Trips AS TripsByCategory WHERE ([__Category] = Category);
SELECT TourCategories.Category FROM TourCategories;
SELECT TourCategories.Category FROM TourCategories;
SELECT Trips.TripNo, Trips.TripName, Trips.TripStartDate, Trips.Duration, Trips.City, Trips.StateAbbrev, Trips.Category, Trips.Price FROM Trips ORDER BY Trips.TripName;
SELECT Customers.* FROM Customers WHERE (([State]<>”MO” Or [State] IS Null));
SELECT Trips.*, Categories.Description, Categories.Category AS Category_Categories FROM Categories INNER JOIN Trips ON Categories.Category = Trips.Category;
SELECT DISTINCTROW * FROM Trips;
SELECT Customers.CustNo, Customers.FName, Customers.LName, Customers.Street, Customers.City, Customers.State, Customers.Zip, Customers.Phone, Sales.SalesNo, Sales.SaleDate, Sales.CustNo, Trips.TripNo, Trips.TripName, Trips.TripStartDate, Trips.Duration, Trips.City, Trips.StateAbbrev, Trips.Category, Trips.Price FROM Trips INNER JOIN (Customers INNER JOIN Sales ON Customers.CustNo = Sales.CustNo) ON Trips.TripNo = Sales.TripNo WHERE (((Sales.SalesNo)=[Forms]![Switchboard]![cboFindCustomer]));
SELECT Customers.CustNo, Customers.FName, Customers.LName, [LName] & “, ” & [FName] AS CustomerName, Customers.Street, Customers.City, Customers.State, Customers.Zip, Customers.Phone, Customers.FirstContact, Sales.TripNo, Sales.CustNo, Sales.SaleDate FROM Customers INNER JOIN Sales ON Customers.CustNo = Sales.CustNo;
SELECT [FirstName] & ” ” & [LastName] AS StudentName, [_GradingReport].ProjectName, “Submission #” & [SubmissionNum] AS SubmissionCt, [_GradingReport].Score, [_GradingReport].MaxScore, “Score is: ” & [Score] & ” out of ” & [MaxScore] AS Grade, [_GradingReportSteps].[StepNumber] & “. ” & [Description] AS Step, [_GradingReportSteps].[StepScore] & “/” & [_GradingReportSteps].[StepMaxScore] AS StepGrade, [_GradingReportSteps].ActionName, IIf([ifsuccessful]=True,Null,[ErrorText]) AS Feedback, [_GradingReportSteps].IfSuccessful, [_GradingIcons].GA_Status_Icon, [_GradingReportSteps].StepNumber, [_GradingReportSteps].StepActionOrder, [_GradingReportSteps].ID, [_GradingIcons].SAM_Logo, [_GradingReportSteps].Description, [_GradingReport].EngineVersion FROM _GradingReport, _GradingIcons INNER JOIN _GradingReportSteps ON [_GradingIcons].[IfSuccessful_Status] = [_GradingReportSteps].IfSuccessful;
SELECT Categories.Category, Categories.Description FROM Categories;
SELECT Trips.TripName, Customers.LName, Customers.FName, Sales.SaleDate FROM Trips INNER JOIN (Customers INNER JOIN Sales ON Customers.CustNo = Sales.CustNo) ON Trips.TripNo = Sales.TripNo ORDER BY Trips.TripName, Customers.LName;
SELECT Trips.TripName, Trips.TripStartDate, Trips.Duration, Trips.City, Trips.StateAbbrev, Trips.Category, Trips.Price, Sales.SaleDate, Customers.FName, Customers.LName, Customers.Street, Customers.City, Customers.State, Customers.Zip, Customers.Phone, Customers.FirstContact FROM Trips INNER JOIN (Customers INNER JOIN Sales ON Customers.CustNo = Sales.CustNo) ON Trips.TripNo = Sales.TripNo ORDER BY Trips.TripName;
SELECT Categories.Category, Trips.TripName, Trips.TripStartDate, Trips.Price FROM Categories INNER JOIN Trips ON Categories.Category = Trips.Category ORDER BY Categories.Category, Trips.TripName;
SELECT States.StateName, Trips.TripName, Trips.TripStartDate, Trips.Price, States.StateAbbreviation FROM States INNER JOIN Trips ON States.StateAbbreviation = Trips.StateAbbrev WHERE (((States.StateAbbreviation)=[Forms]![Switchboard]![cboFindState])) ORDER BY States.StateName, Trips.TripName;
 
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Excel Chapter 9 Mid-Level 2 – Pizza Sales

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Exp19_Excel_Ch09_ML2_Pizza_Sales_Instructions.docx
Grader – Instructions Excel 2019 Project

Exp19_Excel_Ch09_ML2_Pizza_Sales
Project Description:
You manage a chain of pizza restaurants in Augusta, Lewiston, and Portland, Maine. Each store manager created a workbook containing the quarterly sales for each type of sale (dine-in, carryout, and delivery). You want to create links to a summary workbook for the yearly totals.

Steps to Perform:
Step

Instructions

Points Possible

1

Start Excel. Download and open the file named Exp19_Excel_Ch09_ML2_Pizza.xlsx. Grader has automatically added your last name to the beginning of the filename.

0

2

You want to enter totals from the Augusta workbook into the Pizza workbook. Display the Augusta worksheet; in cell B4, insert a link to the Dine-In total in cell F4 in the Exp19_Excel_Ch09_ML2_Augusta workbook. Edit the formula to make the cell reference relative.

5

3

You want to copy the formula down the column but preserve the original formatting. Use AutoFill to copy the formula from cell B4 to the range B5:B7 in the Augusta worksheet. Close the Augusta workbook; keep the Pizza workbook open.

6

4

You want to enter totals from the Portland workbook into the Pizza workbook. Display the Portland worksheet; in cell B4 insert a link to the Dine-In total in cell F4 in the Exp19_Excel_Ch09_ML2_Portland workbook. Edit the formula to make the cell reference relative.

5

5

You want to copy the formula down the column but preserve the original formatting. Use AutoFill to copy the formula from cell B4 to the range B5:B7 in the Portland worksheet. Close the Portland workbook; keep the Pizza workbook open.

6

6

You want to enter totals from the Lewiston workbook into the Pizza workbook. Display the Lewiston worksheet; in cell B4 insert a link to the Dine-In total in cell F4 in the Exp19_Excel_Ch09_ML2_Lewiston workbook. Edit the formula to make the cell reference relative.

5

7

You want to copy the formula down the column but preserve the original formatting. Use AutoFill to copy the formula from cell B4 to the range B5:B7 in the Lewiston worksheet. Close the Lewiston workbook; keep the Pizza workbook open.

6

8

The Summary sheet should contain the same formatting as the other sheets. Select the range A1:B7 in the Lewiston worksheet. Group the Lewiston and Summary worksheets. Fill formatting only across the grouped worksheets.

5

9

Ungroup the worksheets and change the width of column B to 16 in the Summary worksheet.

2

10

You are ready to insert functions with 3-D references in the Summary worksheet. In cell B4, insert a SUM function that calculates the total Dine-In sales for the three cities.

5

11

Copy the formula in cell B4 and use the Paste Formulas option in the range B5:B7 to preserve the formatting.

5

12

You are ready to display the Contents worksheet and insert hyperlinks. • Insert a hyperlink in cell A3 that links to cell B7 in the Augusta sheet. Include the ScreenTip text: Augusta total sales (no period). • Insert a hyperlink in cell A4 that links to cell B7 in the Portland sheet. Include the ScreenTip text: Portland total sales (no period). • Insert a hyperlink in cell A5 that links to cell B7 in the Lewiston sheet. Include the ScreenTip text: Lewiston total sales (no period). • Insert a hyperlink in cell A6 that links to cell B7 in the Summary sheet. Include the ScreenTip text: Total sales for all locations (no period).

10

13

You want to create a data validation rule. Select the range B3:B5 on the Future worksheet and add the following data validation rule: • Allow Date between 3/1/2021 and 10/1/2021. • Enter the input message title: Proposed Date (no period). • Enter the input message: Enter the proposed opening date for this location. (including the period). • Select the Information error alert style. • Enter the error alert title: Confirm Date (no period). • Enter the error message: Confirm the date with the VP. (including the period).

13

14

You should test the validation rule to ensure it works correctly. Enter 10/5/2021 in cell B5 and click OK in the Confirm Date message box.

3

15

You want to unlock a range on the Future worksheet to enable changes by users. Unlock the range B3:B5.

6

16

Now that the cells are unlocked, you are ready to protect the Future worksheet. Protect the worksheet without a password and using the default settings.

6

17

Hide the Future worksheet.

6

18

Create a footer with your name on the left side, the sheet name code in the center, and the file name code on the right side of the five visible worksheets.

6

19

Mark the workbook as final. Note: Mark as Final is not available in Excel for Mac. Instead, use Always Open Read-Only on the Review tab.

0

20

Save and close Exp19_Excel_Ch09_ML2_Pizza.xlsx. Exit Excel. Submit the file as directed.

0

Total Points

100

Created On: 09/03/2020 1 Exp19_Excel_Ch09_ML2 – Pizza Sales 1.1

Amy_Exp19_Excel_Ch09_ML2_Pizza.xlsx
Contents
Pizza Workbook
Augusta
Portland
Lewiston
Summary
Augusta
Augusta
Category Total
Dine-In
Pick-up
Delivery
Total
Portland
Portland
Category Total
Dine-In
Pick-up
Delivery
Total
Lewiston
Lewiston
Category Total
Dine-In
Pick-up
Delivery
Total
Summary
Regional Totals
Category Total
Dine-In
Pick-up
Delivery
Total
Future
Plans for New Locations
Eugene 5/1/2021
Salem 7/1/2021
Hillsboro 9/1/2021
Exp19_Excel_Ch09_ML2_Portland.xlsx
Portland
Portland Location
Category 1st Quarter 2nd Quarter 3rd Quarter 4th Quarter Total
Dine-In $ 45,000 $ 41,000 $ 38,000 $ 33,000 $ 157,000
Pick-up $ 19,000 $ 21,000 $ 20,000 $ 20,000 $ 80,000
Delivery $ 25,000 $ 35,000 $ 45,000 $ 55,000 $ 160,000
Total $ 89,000 $ 97,000 $ 103,000 $ 108,000 $ 397,000
&A &F

Exp19_Excel_Ch09_ML2_Lewiston.xlsx
Lewiston
Lewiston Location
Category 1st Quarter 2nd Quarter 3rd Quarter 4th Quarter Total
Dine-In $ 12,000 $ 10,000 $ 8,000 $ 6,000 $ 36,000
Pick-up $ 27,000 $ 26,000 $ 26,000 $ 26,000 $ 105,000
Delivery $ 25,000 $ 34,000 $ 46,000 $ 60,000 $ 165,000
Total $ 64,000 $ 70,000 $ 80,000 $ 92,000 $ 306,000
&A &F

Exp19_Excel_Ch09_ML2_Augusta.xlsx
Augusta
Augusta Location
Category 1st Quarter 2nd Quarter 3rd Quarter 4th Quarter Total
Dine-In $ 40,000 $ 34,000 $ 29,000 $ 22,000 $ 125,000
Pick-up $ 62,000 $ 63,000 $ 62,000 $ 63,000 $ 250,000
Delivery $ 22,000 $ 26,000 $ 35,000 $ 42,000 $ 125,000
Total $ 124,000 $ 123,000 $ 126,000 $ 127,000 $ 500,000
&A &F

Exp19_Excel_Ch09_ML2_Pizza_final_result.jpg

 
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Discussion 13

/in /by

Adjust your audio

This is a narrated slide show. Please adjust your audio so you can hear the lecture.

If you have problems hearing the narration on any slide show please let me know.

 

1

© 2016 John Wiley & Sons, Inc.

Chapter 12 Knowledge Management, Business Intelligence, and Analytics

2

 

Opening Case: Netflix

What gave Netflix assurance that House of Cards would be a success?

What gives Netflix a competitive advantage?

© 2016 John Wiley & Sons, Inc.

3

Data from 33 million people provided strong evidence that the British version was very popular and Kevin Spacey was also a popular actor

Again, data from millions of users, showing what they watch, when they pause, when they rewind, etc. Patterns emerge that speak volumes about preferences. Their analytics algorithms provide better data than focus groups (and in real time).

3

More Real World Examples

Caesar’s and Capital One both collect and analyze customer data.

Result: They can determine who are the most profitable customers and then follow up with them.

Caesar’s: frequent gamblers

Capital One: charge a lot and pay off slowly

They provide products that would appeal to the profitable customers.

© 2016 John Wiley & Sons, Inc.

4

 

4

A Real World Example from Sports

Oakland As and Boston Red Sox baseball teams

Crunched the numbers on the potential players, such as on-base percentage

Others who did not do the analysis failed to recognize the talent

© 2016 John Wiley & Sons, Inc.

5

 

5

Five Ways Data Analytics can Help an Organization (McKinsey and Co.)

Making data more transparent and usable more quickly

Exposing variability and boosting performance

Tailoring products and services

Improving decision-making

Improving products

© 2016 John Wiley & Sons, Inc.

6

Terminology

Knowledge management: The processes needed to generate, capture, codify and transfer knowledge across the organization to achieve competitive advantage

Business intelligence: The set of technologies and processes that use data to understand and analyze business performance

Business analytics: The use of quantitative and predictive models, algorithms, and evidence-based management to drive decisions

© 2016 John Wiley & Sons, Inc.

7

Data, Information, and Knowledge (reprise)

© 2016 John Wiley & Sons, Inc.

8

8

 

The Value of Managing Knowledge

Value Sources of Value
Sharing best practices Avoid reinventing the wheel Build on valuable work and expertise
Sustainable competitive advantage Shorten innovation life cycle Promote long term results and returns
Managing overload Filter data to find relevant knowledge Organize and store for easy retrieval
Rapid change Build on/customize previous work for agility Streamline and build dynamic processes Quick response to changes
Embedded knowledge from products Smart products can gather information Blur distinction between manufacturing/service Add value to products
Globalization Decrease cycle times by sharing knowledge globally Manage global competitive pressures Adapt to local conditions
Insurance for downsizing Protect against loss of knowledge when departures occur Provide portability for workers who change roles Reduce time to acquire knowledge

© 2016 John Wiley & Sons, Inc.

9

Dimensions of Knowledge

 

 

Explicit

Teachable

Articulable

Observable in use

Scripted

Simple

Documented

Tacit

Not teachable

Not articulable

Not observable

Rich

Complex

Undocumented

Examples:

Estimating work

Deciding best action

Examples:

Explicit steps

Procedure manuals

© 2016 John Wiley & Sons, Inc.

10

10

 

Four Modes of Knowledge Conversion (and examples)

 

Transferring by mentoring, apprenticeship

 

Transferring by models, metaphors

 

Learning by doing; studying manuals

 

Obtaining and following manuals

© 2016 John Wiley & Sons, Inc.

11

Knowledge Management – Four Processes

Generate – discover “new” knowledge

Capture – scan, organize, and package it

Codify – represent it for easy access and transfer (even as simple as using hash tags to create a folksonomy)

Transfer – transmit it from one person to another to absorb it

© 2016 John Wiley & Sons, Inc.

12

12

 

Measures of KM Project Success

Example of specific benefits of a KM project:

Enhanced effectiveness

Revenue generated from extant knowledge assets

Increased value of extant products and services

Increased organizational adaptability

More efficient re-use of knowledge assets

Reduced costs

Reduced cycle time

© 2016 John Wiley & Sons, Inc.

13

13

 

Components of Business Analytics

Component Definition Example
Data Sources Data streams and repositories Data warehouses; weather data
Software Tools Applications and processes for statistical analysis, forecasting, predictive modeling, and optimization Data mining process; forecasting software package
Data-Driven Environment Organizational environment that creates and sustains the use of analytics tools Reward system that encourages the use of the analytics tools; willingness to test or experiment
Skilled Workforce Workforce that has the training, experience, and capability to use the analytics tools Data scientists, chief data officers, chief analytics officers, analysts, etc. Netflix, Caesars and Capital One have these skills

© 2016 John Wiley & Sons, Inc.

14

 

14

Data Sources for Analytics

Structured (customers, weather patterns) or unstructured (Tweets, YouTube videos)

Internal or external

Data warehouses full of a variety of information

Real-time information such as stock market prices

 

© 2016 John Wiley & Sons, Inc.

15

Data Mining

Combing through massive amounts of customer data, usually focused on:

Buying patterns/habits (for cross-selling)

Preferences (to help identify new products/ features/enhancements to products)

Unusual purchases (spotting theft)

It also identifies previously unknown relationships among data.

Complex statistics can uncover clusters on many dimensions not known previously

(e.g., People who like movie x also like movie y)

© 2016 John Wiley & Sons, Inc.

16

Four Categories of Data Mining Tools

Statistical analysis: Answers questions such as “Why is this happening?”

Forecasting/Extrapolation: Answers questions such as “What if these trends continue?”

Predictive modeling: Answers questions such as “What will happen next?”

Optimization: Answers questions such as “What is the best that can happen?”

 

© 2016 John Wiley & Sons, Inc.

17

How to be Successful

Achieve a data driven culture

Develop skills for data mining

Use a Chief Analytics Officer (CAO) or Chief Data Officer (CDO)

Shoot for high maturity level (see next slide)

© 2016 John Wiley & Sons, Inc.

18

Level Description Source of Business Value
1 – Reporting What happened? Reduce costs of summarizing, printing
2 – Analyzing Why did it happen? Understanding root causes
3 – Describing What is happening now Real-time understanding & corrective action
4 – Predicting What will happen? Can take best action
5 – Prescribing How should we respond? Dynamic correction

Five Maturity Levels of Analytical Capabilities

© 2016 John Wiley & Sons, Inc.

19

BI and Competitive Advantage

There is a very large amount of data in databases.

Big data: techniques and technologies that make it economical to deal with very large datasets at the extreme end of the scale: e.g., 1021 data items

Large datasets can uncover potential trends and causal issues

Specialized computers and tools are needed to mine the data.

Big data emerged because of the rich, unstructured data streams that are created by social IT.

 

© 2016 John Wiley & Sons, Inc.

20

Practical Example

Asthma outbreaks can be predicted by U. of Arizona researchers with 70% accuracy

They examine tweets and Google searches for words and phrases like

“wheezing” “sneezing” “inhaler” “can’t breathe”

Relatively rare words (1% of tweets) but 15,000/day

They examine the context of the words:

“It was so romantic I couldn’t catch my breath” vs

“After a run I couldn’t catch my breath”

Helps hospitals make work scheduling decisions

© 2016 John Wiley & Sons, Inc.

21

Sentiment Analysis

Can analyze tweets and Facebook likes for

Real-time customer reactions to products

Spotting trends in reactions

Useful for politicians, advertisers, software versions, sales opportunities

 

 

© 2016 John Wiley & Sons, Inc.

22

Google Analytics and Salesforce.com

Listening to the community: Identifying and monitoring all conversations in the social Web on a particular topic or brand.

Learning who is in the community: Identifying demographics such as age, gender, location, and other trends to foster closer relationships.

Engaging people in the community: Communicating directly with customers on social platforms such as Facebook, YouTube, LinkedIn, and Twitter using a single app.

Tracking what is being said: Measuring and tracking demographics, conversations, sentiment, status, and customer voice using a dashboard and other reporting tools.

Building an audience: Using algorithms to analyze data from internal and external sources to understand customer attributes, behaviors, and profiles, then to find new similar customers

© 2016 John Wiley & Sons, Inc.

23

Google Analytics

Web site testing and optimizing: Understanding traffic to Web sites and optimizing a site’s content and design for increasing traffic.

Search optimization: Understanding how Google sees an organization’s Web site, how other sites link to it, and how specific search queries drive traffic to it.

Search term interest and insights: Understanding interests in particular search terms globally, as well as regionally, top searches for similar terms, and popularity over time.

Advertising support and management: Identifying the best ways to spend advertising resources for online media.

© 2016 John Wiley & Sons, Inc.

24

Internet of Things (IoT)

Much big data comes from IoT

Sensor data in products can allow the products to:

Call for service (elevators, heart monitors)

Parallel park, identify location/speed (cars)

Alert you to the age of food (refrigerator)

Waters the lawn when soil is dry (sprinklers)

Self-driving cars find best route (Google)

© 2016 John Wiley & Sons, Inc.

25

Intellectual Capital vs Intellectual Property

Intellectual Capital: the process for managing knowledge

Intellectual Property: the outputs; the desired product for the process

Intellectual Property rights differ remarkably by country

© 2016 John Wiley & Sons, Inc.

26

Closing Caveats

These are emerging concepts and disciplines

Sometimes knowledge should remain hidden (tacit) for protection

We should remain focused on future events, not just look over the past

A supportive culture is needed in a firm to enable effective KM and BI

 

© 2016 John Wiley & Sons, Inc.

27

Summary

After you have listened to this lecture and read Chapter 12 of your text

Go to Discussion Board 13 and answer the discussion prompt

Finally complete Quiz 12

 

28

© 2016 John Wiley & Sons, Inc.

 

28

 
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3-F Method

/in /by

Analysis on the Demand of Top Talent Introduction in Big Data and Cloud Computing Field in China

Based on 3-F Method Zhao Linjia, Huang Yuanxi, Wang Yinqiu, Liu Jia

National Academy of Innovation Strategy, China Association for Science and Technology, Beijing, P.R.China

Abstract—Big data and cloud computing, which can help China to implement innovation-driven development strategy and promote industrial transformation and upgrading, is a new and emerging industrial field in China. Educated, productive and healthy workforces are necessary factor to develop big data and cloud computing industry, especially top talents are essential. Therefore, a three-step method named 3-F has been introduced to help describing the distribution of top talents globally and making decision whether they are needed in China. The 3-F method relies on calculating the brain gain index to analysis the top talent introduction demand of a country. Firstly, Focus on the high-frequency keywords of a specific field by retrieving the highly cited papers. Secondly, using those keywords to Find out the top talents of this specific field in the Web of Science. Finally, Figure out the brain gain index to estimate whether a country need to introduce top talents of a specific field abroad. The result showed that the brain gain index value of China’s big data and cloud computing field was 2.61, which means China need to introduce top talents abroad. Besides P. R. China, those top talents mainly distributed in the United States, the United Kingdom, Germany, Netherlands and France.

I. INTRODUCTION Big data and cloud computing is a new and emerging

industrial field[1], and increasing widely used in China[2-4]. Talents’ experience is a source of technological mastery[5], essentially for developing and using big data technologies. Most European states consider the immigration of foreign workers as an important factor to decelerate the decline of national workforces[6]. Lots of universities and research institutes have set up undergraduate and/or postgraduate courses on data analytics for cultivating talents[7]. EMC corporation think that vision, talent, and technology are necessary elements to providing solutions to big data management and analysis, insuring the big data success[8].

Bibliometrics research has appeared as early as 1917[9], and has been proved an effective method for assessing or identifying talents. Based on analyses of publication volume, journals and their impact factors, most cited articles and authors, preferred methods, and represented countries, Gallardo-Gallardo et. al[10] assess whether talent management should be approached as an embryonic, growth, or mature phenomenon.

In this paper, we intend to analysis whether China need to introduce top talents in the field of big data and cloud computing by using bibliometrics. In section 2, the 3-F method

for top talent introduction demand analysis will be discussed. In section 3, we will analysis the demand of top talent introduction in big data and cloud computing field in China.

II. METHOD In general, metering indicators contain the most productive

authors, journals, institutions, and countries, and the collaboration networks between authors and institutions[11, 12]. Based on the commonly used bibliometrics method, 3-F method for top talent introduction demand analysis is proposed. 3-F method has three steps:

Firstly, searching the literature database and forming a high-impact literature collection in a specific field. Focusing on the high-frequency keywords in the high-impact literature collection by using the text analysis method as the research hotspots. Just to be clear, the high-impact literature refers to the journal literature whose number of cited papers ranked in the top 1% in the same discipline and in the same year.

Secondly, retrieving those keywords in the Web of Science to find out where those top talents of this specific field are. Find the top talents by collected the information about talents’ country distribution, the institutions distribution and so on through the high-impact literature collection. Among them, the top talent refers to the first author or the communication author of the high-impact literatures.

Finlly, Figure out the brain gain index to determine the top talents introduction demand of a certain country. The brain gain index is calculated as following formulas:

Iik = (Twk / Tik) / (Pw / Pi) (1) Among them, Iik means the brain gain index value of

country (i) in the field (k), Twk means the number of world’s top talents in the field (k), Tik means the number of country’s (i) top talents in the field (k), Pw means the world population, Pi means the country’s (i) population. If Iik was more than 1, that means the country (i) has less top talents in the field (k), therefore the talent introduction demand will be relatively strong. In contrast, if Iik was less than 1, that means the country’s (i) has greater top talents in the field (k) than the world average, and the talent introduction demand will not be so strong.

Additionally, the literature information mainly from the ISI Web of Science (SCI, CPCI-S), and the the data analysis and visualization tools are TDA and Tableau.

2017 Proceedings of PICMET ’17: Technology Management for Interconnected World

978-1-890843-36-6 ©2017 PICMET

 

 

III. CASE STUDY

Using 3-F method to analysis the top talents introduction demand in the big data and cloud computing field. We collected the high-impact literatures from January 1, 2006 to July 31, 2016. The literature Language was English and the literature type was article. Combining with the above conditions, we got 546 high-impact literatures in the big data and cloud computing field. Then the high-frequency keywords have been obtained (Table 1) and served as the research hotspots set.

TABLE I. THE RESEARCH HOTSPOTS OF THE HIGH-IMPACT LITERATURES IN BIG DATA AND CLOUD COMPUTING FIELD

Order Keywords Frequency

1 cloud computing 48

2 big data 24

3 virtualization 11

4 cloud manufacturing 9

5 internet of things (IoT) 8

6 mobile cloud computing 8

7 bioinformatics 6

8 climate change 6

9 Hadoop 6

10 software-defined networking (SDN) 6

……

 

At the same time, we displayed the frequency distribution of research hotspots in the way of cloud chart(fig. 1).

Fig. 1. The cloud chart of research hotspots that in the field of big data and

cloud computing

Then, we find the information about nationality (Table 2), institutes (Table 3) of top talents in the high-impact literature collection. Results showed there were 662 top talents worldwide in the big data and cloud computing field. The top ten countries or regions who had the most top talents were the United States, P.R.China, the United Kindom, Germany, the Netherlands, France, Canada, Australia, Italy and Switzerland and Spain tied for the tenth.

TABLE II. THE NATIONALITY DISTRIBUTION OF TOP TALENTS IN THE BIG DATA AND CLOUD COMPUTING FIELD

Order Country or Region Number of the top talent 1 US 268 2 P. R. China 48 3 UK 47 4 Germany 39 5 Netherlands 28 6 France 27 7 Canada 22 8 Australia 21 9 Italy 19 10 Switzerland 13 Spain 13 12 Japan 10 13 Korea 8 Malaysia 8 15 Singapore 7 New Zealand 7 17 Austria 6 18 Belgium 5 Sweden 5 India 5 Chinese Taipei 5 ……

TABLE III. THE INSTITUTES DISTRIBUTION OF TOP TALENTS IN THE BIG DATA AND CLOUD COMPUTING FIELD

Order Country or Region Number of the top talent

1 Harvard University (US) 10

2 Purdue University (US) 7

University of Malaya (Malaysia) 7

University of Maryland (US) 7

Unversity of Melbourne (Australia) 7

University of Missouri (US) 7

7 Oxford Unversity (UK) 6

8 Chinese Academy of Sciences (P.R.China) 5

ETH Zurich (Switzerland) 5

Massachusetts General Hospital (US) 5

Northwestern University (US) 5

University of British Columbia (Canada) 5

UC, Berkeley (US) 5

UC, San Diego (US) 5

University of Texas at Austin (US) 5

University of Washington (US) 5

……

 

2017 Proceedings of PICMET ’17: Technology Management for Interconnected World

 

 

From table 2 and 3 we can see that China was in the second place worldwide. However, China’s top talent is much less than the United States. In addition, the overall strength of Chinese research institutions is not strong. So, whether China should introduce top talents from other countries is need to be discussed.

According to the formula of the brain gain index, and using the world population data as well as the Chinese mainland population data released by the World Bank, the value of the Chinese brain gain index of big data and cloud computing was 2.61. In comparison, the brain gain index value of the United States was 0.11. That means China need to introduce top talent in the field of big data and cloud computing.

IV. CONCLUSION

In the knowledge economy era, the international flow of top talent has become convenient and frequent. Facing the world’s top talent shortage, China and the world’s major countries have developed overseas top talent introduction programs. Until 2007, almost all European countries had introduced some skillselective migration policies in order to attract the top talents. To make the overseas top talent introduction programs more effective and targeted is helpful for occupying the strategic high ground in the global top talent competition.

This paper improved the traditional talent evaluation function of bibliometric method, and presented the 3-F analysis method, which was applied to analyze the demand of top talents. The 3F method could help the government official to make decision whether need to introduce top talents to develop a new industry field and lock these top talents geographic location.

REFERENCES [1] .Xu, B.M., X.G. Ni. Development Trend and Key Technical Progress of

Cloud Computing[J]. Bulletin of the Chinese Academy of Sciences, 2015. 30(2), pp. 170-180.

[2] Xiao, Y., Y. Cheng, Y.J. Fang, Research on Cloud Computing and Its Application in Big Data Processing of Railway Passenger Flow, in Iaeds15: International Conference in Applied Engineering and Management, P. Ren, Y. Li, and H. Song, Editors. 2015, Aidic Servizi Srl: Milano. pp. 325-330.

[3] Zhu, Y.Q., P. Luo, Y.Y. Huo et. al, Study on Impact and Reform of Big Data on Higher Education in China, in 2015 3rd International Conference on Social Science and Humanity, G. Lee and Y. Wu, Editors. 2015, Information Engineering Research Inst, USA: Newark. p. 155-161.

[4] Wang, X., L.C. Song, G.F. Wang et.al. Operational Climate Prediction in the Era of Big Data in China: Reviews and Prospects[J]. Journal of Meteorological Research, 2016. 30(3), pp. 444-456.

[5] Dahlman, C., L. Westphal, Technological effort in industrial development——An Interpretative Survey of Recent Research[R]. 1982.

[6] Cerna, L., M. Czaika, European Policies to Attract Talent: The Crisis and Highly Skilled Migration Policy Changes, in High-Skill Migration and Recession. 2016, Springer. pp. 22-43.

[7] Jin, X., B.W. Wah, X. Cheng et. al. Significance and challenges of big data research[J]. Big Data Research, 2015. 2(2), pp. 59-64.

[8] Fang, H., Z. Zhang, C.J. Wang et. al. A survey of big data research[J]. IEEE Network, 2015. 29(5), pp. 6-9.

[9] Cole, F.J., Eales, N. B. The history of comparative anatomy[J]. science Progress, 1917. 11, pp. 578-596.

[10] Gallardo-Gallardo, E., S. Nijs, N. Dries et. al. Towards an understanding of talent management as a phenomenon-driven field using bibliometric and content analysis[J]. Human Resource Management Review, 2015. 25, pp. 264-279.

[11] Clarke, B.L. Multiple authorship trends in scientific papers[J]. Science, 1964. 143(3608), pp. 822-824.

[12] Gonzalez-Valiente, C.L., J. Pacheco-Mendoza, R. Arencibia-Jorge. A review of altmetrics as an emerging discipline for research evaluation[J]. Learned Publishing, 2016. 29(4), pp. 229-238.

 

2017 Proceedings of PICMET ’17: Technology Management for Interconnected World

 

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