CHAPTER SEVEN MANAGING ANALYTICAL PEOPLE CULTIVATING THE SCARCE INGREDIENT THAT MAKES ANALYTICS WORK Flashcards
1
Q
What is the scarce ingredient that makes analytics work?
A
People
2
Q
What is a common analytical urban legend involving diapers and beer?
A
A grocery retailer found a correlation between beer and diaper sales, leading to increased sales when the products were placed near each other
3
Q
Who ultimately needs to interpret the patterns identified by data mining software?
A
A smart human analyst
4
Q
What is required for analytics to be useful beyond interpretation?
A
A decision maker must make a decision and take action
5
Q
What can hinder the effectiveness of analytics in decision-making?
A
Lack of trust and credibility between analysts and decision makers
6
Q
What three groups are involved in analytical skills and orientations within organizations?
A
- Senior management team (including CEO)
- Professional analysts
- Analytical amateurs (the rest of the employees)
7
Q
What is one key change in the role of humans in analytics due to technology?
A
Machine learning is changing how analytical models are generated
8
Q
What type of decisions are likely to be automated in the future?
A
Tactical and repetitive decisions
9
Q
Why is it important for senior executives to understand analytical outputs?
A
To prevent analysts from being relegated to the back office and ensure competition is based on analytics
10
Q
What example illustrates a failure to act on analytical findings?
A
A New York bank did not close branches despite profitability analyses
11
Q
What is a characteristic of analytical leaders?
A
Passion for analytical and fact-based decision making
12
Q
What should senior executives appreciate regarding analytical tools?
A
The kinds of tools suitable for business problems and their limitations
13
Q
What is essential for companies to succeed as analytical competitors?
A
Willingness to act on the results of analyses
14
Q
What should be managed in a company that uses analytics widely?
A
A meritocracy
15
Q
Which companies were noted for competing on analytics from their inception?
A
- Amazon
- Capital One
- Netflix
16
Q
What did Barry Beracha, former CEO of Earthgrains, emphasize in decision making?
A
The need for better data and using it for decisions
17
Q
What can happen if differences in employee performance are visible but not acted on?
A
Nothing good results, and better employees may become disheartened
18
Q
What is a critical cultural attribute of analytical competitors?
A
Desire to make decisions based on real-world evidence
19
Q
Fill in the blank: A smart human still needs to ______ the patterns identified by data mining software.
A
interpret
20
Q
True or False: High-level managers are likely to lose their jobs to autonomous systems.
A
False
21
Q
What is a significant factor in the emergence of analytical leadership in organizations?
A
Desire to compete analytically from the beginning or through new executive leadership
22
Q
What was the concept of personalization based on?
A
Statistical algorithms and web transaction data
23
Q
Which companies were noted for their early analytical competition?
A
Amazon, Capital One, Netflix, Google
24
Q
Who were the executives that brought new analytical strategies to established companies?
A
Gary Loveman at Caesars, Tom Ricketts at the Chicago Cubs
25
What role did Joe Gallo play at E. & J. Gallo?
Focused on data and analysis, particularly in sales
26
Who influenced the New England Patriots towards analytical decision-making?
Jonathan Kraft
27
What is one key role of the CFO in analytical competition?
Responsibility for financial processes and information
28
What additional areas should a CFO focus on beyond finance?
Cost control, claims, actuarial, and marketing analytics
29
Who led Deloitte's analytics efforts internally?
Frank Friedman, the CFO
30
What was the primary focus of a retail company's CFO regarding analytics?
Customer orientation and developing measures for customer relationships
31
What was Al de Molina's contribution to Bank of America?
Instigator of analytical activity and integration of data
32
What is the primary responsibility of the CIO in relation to analytics?
Changing the culture and analytical behaviors of employees
33
How did Shaygan Kheradpir aim to change the culture at Verizon?
Through continuous exposure to information and performance metrics
34
What is one traditional approach CIOs take regarding analytics?
Crafting an enterprise information strategy
35
What is the role of the CDAO?
Ensuring data, organizational capabilities, and mindset for analytics
36
What responsibilities does the CDAO have according to Gartner?
Data strategy, governance, control, policy development, exploitation
37
What can stimulate demand for analytics in organizations lacking executive commitment?
Improving IT infrastructure and encouraging analytical software usage
38
What is a common joke about analytical professionals?
What did the math PhD say to the MBA graduate? Would you like fries with that?
39
What types of degrees do most analytical professionals possess?
Advanced degrees, often PhDs in analytical fields
40
What roles do analytical professionals play in organizations?
Designing experiments, defining algorithms, performing data analysis
41
What is the impact of the rise in demand for analytical talent?
Companies are avidly recruiting analytical professionals
42
Fill in the blank: The primary impetus for more analytics at Procter & Gamble came from the firm’s two _______.
Vice chairmen
43
True or False: The CFO is responsible for all analytical projects in an organization.
False
44
What new role has emerged in analytical competitors as highlighted in the text?
Chief Data and Analytics Officer (CDAO)
45
Fill in the blank: The _______ focuses on information behaviors, management practices, and technology practices.
Information orientation
46
What are the primary roles of analytical professionals?
Design and carry out experiments, define and refine analytical algorithms, perform data mining and statistical analyses
## Footnote
Analytical professionals create predictive and prescriptive analytics applications for organizations.
47
What educational qualifications do most analytical professionals have?
Advanced degrees, often PhDs in fields such as statistics, data science, econometrics, mathematics, operations research, logistics, physics, and marketing research
## Footnote
Some may have master's degrees in applied analytics, informatics, and data science.
48
Who is Katrina Lane and what is her significance in the field of analytics?
Vice president of channel marketing at Caesars Entertainment, with a PhD in experimental physics
## Footnote
Lane's career trajectory and skills exemplify the rarity of individuals with such a combination of analytical and business expertise.
49
What is the role of data scientists in organizations?
Bring structure to unstructured data, create models, and interpret results for key decisions
## Footnote
Data scientists are often described as holding 'the sexiest job of the 21st century' due to high demand.
50
What was the growth of data scientists at Procter & Gamble from 2013 to 2017?
From one data scientist in 2013 to over thirty in 2017
## Footnote
This reflects the increasing demand for analytics in large traditional firms.
51
What challenges does Google face in hiring analytical professionals?
Difficulty attracting talent despite generous salaries and benefits
## Footnote
Many talented statisticians prefer careers in biotech, making recruitment challenging.
52
How many analytical professionals are typically needed in organizations?
Ranges from about a dozen to several hundred, depending on organizational goals
## Footnote
GE aimed to hire four hundred data scientists for its software and analytics business.
53
What organizational structure do most companies adopt for their analytical professionals?
Centralized to some degree, with variations over time
## Footnote
Companies like Procter & Gamble and AIG have centralized and then decentralized their analytical groups for efficiency.
54
What is the argument for central coordination of analytical teams?
Advanced statistical methods require extensive knowledge that is impractical to distribute broadly
## Footnote
Centralized groups can perform sophisticated analyses and experiments more effectively.
55
What is a hub-and-spoke organization in analytics?
A model where analysts are allocated among business units and corporate functions for specialization
## Footnote
The centralized hub is responsible for knowledge sharing and best practices.
56
What is the importance of relationships between analysts and decision makers?
Building trust is crucial for effective analytics
## Footnote
Analysts must understand both the business context and specific needs of decision makers.
57
What are 'front room statisticians'?
Analytical professionals who have strong business orientation and interpersonal skills
## Footnote
Contrasted with 'backroom statisticians' who may lack business acumen.
58
What skills are typically required for a data scientist?
PhD in a quantitative field, hands-on experience in predictive modeling, strong problem-solving ability, and communication skills
## Footnote
Familiarity with programming languages and statistical software is also essential.
59
What are some typical differences between analysts and data scientists?
* Data structure: Analysts focus on structured data; Data scientists handle unstructured data
* Educational background: Analysts often have degrees in operations research/statistics; Data scientists in computer science/data science
* Mindset: Analysts are more traditional; Data scientists are more entrepreneurial
## Footnote
These differences may be diminishing as the roles evolve.
60
What critical success factors did executives identify for analytical groups?
* Building a sustainable pipeline of projects
* Maintaining a close relationship with IT
* Effective governance and funding
* Managing internal politics
* Ensuring algorithms are user-friendly
## Footnote
These factors contribute to the long-term success and integration of analytics in businesses.
61
What is knowledge process outsourcing in analytics?
Outsourcing analytical tasks to firms in countries like India and China
## Footnote
Companies like Mu Sigma and Evalueserve specialize in analytical services.
62
What is a potential drawback of outsourcing analytical professionals?
Difficulty in developing trusting relationships with decision makers due to geographical distance
## Footnote
This can hinder effective collaboration and understanding of business needs.
63
What are some key fields involved in data analytics?
Data mining, algorithm development, quantitative finance
## Footnote
Firms like Mu Sigma, Evalueserve, and Genpact have substantial practices in these domains
64
What challenges do analysts face when working with decision makers from a distance?
Developing a trusting relationship
## Footnote
Successful business models may combine onshore and offshore capabilities
65
What is the role of onshore analysts in an analytical business model?
Work closely with decision makers
## Footnote
Offshore specialists handle back-office analytical work
66
What is the importance of defining analytical applications before development?
Successful outsourcing of algorithm development
## Footnote
Clear description by business owners increases chances of success
67
What is a key issue regarding the skills of frontline workers in analytics?
Determining needed analytical sophistication
## Footnote
Skills will vary by company and industry
68
What type of analysts does Capital One primarily hire?
Analytical amateurs
## Footnote
Many have some analytical background but not PhDs
69
What is the significance of the Boston Red Sox's approach in 2003?
Spreading analytical orientations throughout the organization
## Footnote
Example of needing analytical skills in various roles
70
What challenges did a beer manufacturer face with its new supply chain optimization software?
Beer flow coordinators lacked necessary skills
## Footnote
No hiring or substantial training was done
71
What was the outcome of the polymer chemicals company’s supply chain optimization effort?
Lack of skills became a bottleneck
## Footnote
New roles required higher analytical sophistication
72
Why do companies need to ensure employees have exposure to analytics?
To thrive in an analytical environment
## Footnote
Managers and analysts need to conduct data-driven experiments
73
What skills are essential for information workers in an analytical competitor?
* Experimental
* Numerate
* Data literate
## Footnote
These skills help in interpreting and using data effectively
74
What is the role of experimentation in business analytics?
Managers must apply scientific experimentation principles
## Footnote
Understanding experimental design is crucial for data analysis
75
What does it mean to be numerate in the context of analytics?
Ability to interpret and use numeric data
## Footnote
Understanding statistical methods is essential for business users
76
What is data literacy?
Ability to find, manipulate, manage, and interpret data
## Footnote
It includes understanding various forms of data like text and images
77
What challenges do amateur analysts face with IT tools?
Choosing the right analytical tools
## Footnote
Options include powerful statistical tools, prescriptive models, and spreadsheets
78
What are the strengths of spreadsheets in analytics?
* Easy to use
* Widely understood format
* Inexpensive
## Footnote
However, they pose issues like errors and lack of consistency
79
What is the emerging role of the 'citizen data scientist'?
Individuals who perform analytics with support from intelligent machines
## Footnote
They handle complex data management tasks
80
What is autonomous decision making in analytics?
Automated decisions made with minimal human intervention
## Footnote
Examples include credit extension and pricing decisions
81
What are some limitations of automated decision-making applications?
Fiduciary, legal, or ethical issues may require human involvement
## Footnote
Management needs to retain skilled personnel for oversight
82
What is the override issue in automated decision systems?
How amateurs should handle automated decisions they disagree with
## Footnote
Companies like Caesars discourage overrides, while Marriott encourages them
83
Why might a company want to discourage overrides of automated systems?
Evidence shows automated systems yield better results
## Footnote
Example: Caesars restricts overrides to maintain system effectiveness
84
What is the approach of Marriott regarding overrides of revenue management systems?
Encourages overrides to account for local anomalies
## Footnote
Decision-making is seen as a combination of human judgment and automation
85
What is encouraged for Partners HealthCare’s physicians regarding automated decision systems?
Override automated decision systems when in the best interest of the patient
## Footnote
Physicians at Partners HealthCare are often professors at Harvard Medical School, indicating a high level of expertise.
86
What is suggested about the combination of humans and automated decision rules?
The best result is probably from the combination of humans and automated decision rules.
87
What do companies with lower levels of analytical skills at the front line prefer regarding overrides?
They may prefer to take a hard line on overrides.
88
How can the effectiveness of overrides be determined?
Empirically, by assessing if overrides usually result in better decisions.
89
What should companies do if they decide to allow overrides?
Develop systematic means of capturing the reasons for overrides.
90
What process is followed at Partners when physicians override the automated system?
Physicians are asked to give a reason for the override.
91
What happens if physicians constantly override a particular system recommendation?
They are interviewed about their reasoning.
92
What is the key message regarding human resources in analytical competition?
The human resource is perhaps the most important capability an analytical competitor can cultivate.
93
What do analytical competitors find challenging about executing their strategies?
Getting the right kinds of analytical people in sufficient numbers.
94
What do hardware and software alone fail to create in terms of analytical strategies?
The kinds of capabilities that analytical strategies require.
95
Who has a role in making analytical competition successful?
Everyone, including senior executives, analytical professionals, data scientists, and frontline analytical amateurs.
