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fundamental of analytics > Module 3 > Flashcards

Module 3 Flashcards

1
Q

Introduced by the Analytics Association of the Philippines

A

Professional Maturity Model

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2
Q

This model recommends maximum proficiency levels for various roles within the analytics domain, streamlining workforce skills for enhanced organizational efficiency.

A

Professional Maturity Model

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3
Q

In the role of Data Steward, they should have the following proficiency in these competencies:

A

(DDOD w/ 21CR)
- Domain Knowledge (X)
- Data Governance (X)
- Operational Analytics (X)
- Data Visualization (I)
- 21st Century Skills
- Computing (E)
- Research Methods (E)

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4
Q

Data Steward proficiency levels:

A

(DDOD w/ 21CR)
(XXXI w/ 21EE)
- Domain Knowledge (X)
- Data Governance (X)
- Operational Analytics (X)
- Data Visualization (I)
- 21st Century Skills
- Computing (E)
- Research Methods (E)

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5
Q

In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ 21CR)

A

Data Steward

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6
Q

In the role of Data Engineer, they should have the following proficiency in these competencies:

A

(DDOD w/ DR. CMS, age 21)
- Domain Knowledge (E)
- Data Governance (I)
- Operational Analytics (X)
- Data Visualization (E)
- Data Engineering (X)
- Research Methods (E)
- Computing (E)
- Methods and Algorithms (E)
- Statistical Techniques (E)
- 21st Century Skills

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7
Q

Data Engineer proficiency levels:

A

(DDOD w/ DR. CMS, age 21)
(EIXE w/ XE. EEE, age 21)
- Domain Knowledge (E)
- Data Governance (I)
- Operational Analytics (X)
- Data Visualization (E)
- Data Engineering (X)
- Research Methods (E)
- Computing (E)
- Methods and Algorithms (E)
- Statistical Techniques (E)
- 21st Century Skills

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8
Q

In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ DR. CMS, age 21)

A

Data Engineer

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9
Q

In the role of Data Scientist, they should have the following proficiency in these competencies:

A

(DDOD w/ 21CD-RMS)
- Domain Knowledge (I)
- Data Governance (I)
- Operational Analytics (X)
- Data Visualization (I)
- 21st Century Skills
- Computing (X)
- Data Engineering (X)
- Research Methods (X)
- Methods and Algorithms (X)
- Statistical Techniques (X)

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10
Q

Data Scientist proficiency levels:

A

(DDOD w/ 21CD-RMS)
(IIXI w/ 21XX-XXX)
- Domain Knowledge (I)
- Data Governance (I)
- Operational Analytics (X)
- Data Visualization (I)
- 21st Century Skills
- Computing (X)
- Data Engineering (X)
- Research Methods (X)
- Methods and Algorithms (X)
- Statistical Techniques (X)

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11
Q

In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ 21CD-RMS)

A

Data Scientist

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12
Q

In the role of Functional Analyst, they should have the following proficiency in these competencies:

A

(DDOD w/ 21RaceCars)
- Domain Knowledge (X)
- Data Governance (I)
- Operational Analytics (X)
- Data Visualization (X)
- 21st Century Skills
- Research Methods (E)
- Computing (E)

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13
Q

Functional Analyst proficiency levels:

A

(DDOD w/ 21RaceCars)
(XIXX w/ 21EE)
- Domain Knowledge (X)
- Data Governance (I)
- Operational Analytics (X)
- Data Visualization (X)
- 21st Century Skills
- Research Methods (E)
- Computing (E)

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14
Q

In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ 21RaceCars)

A

Functional Analyst

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15
Q

In the role of Analytics Manager, they should have the following proficiency in these competencies:

A

(DDOD w/ MCR and 21DaysofSummer)
- Domain Knowledge (X)
- Data Governance (X)
- Operational Analytics (X)
- Data Visualization (X)
- Methods and Algorithms (E)
- Computing (E)
- Research Methods (E)
- 21st Century Skills
- Data Engineering (E)
- Statistical Techniques (E)

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16
Q

Analytics Manager proficiency levels:

A

(DDOD w/ MCR and 21DaysofSummer)
(XXXX w/ EEE and 21EE)
- Domain Knowledge (X)
- Data Governance (X)
- Operational Analytics (X)
- Data Visualization (X)
- Methods and Algorithms (E)
- Computing (E)
- Research Methods (E)
- 21st Century Skills
- Data Engineering (E)
- Statistical Techniques (E)

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17
Q

In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ MCR and 21DaysofSummer)

A

Analytics Manager

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18
Q

When was the general idea of the DELTA+ Model first introduced?

A

2007 in a book made by Thomas Davenport and Jeanne Harris.

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19
Q

What was the initial name of the DELTA+ Model?

A

“5 Stages of Analytics Maturity” in the book: “Competing Analytics: The New Science of Winning.”

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20
Q

When was the DELTA Model formally introduced?

A

In 2010 by Robert Morison, it had 5 components (D-E-L-T-A) described in the book: “Analytics at Work: Smarter Decisions, Better Results”

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21
Q

When did the DELTA Model introduce two new components?

A

In 2017, it became the “DELTA+ Model” in the book: “Competing Analytics: The New Science of Winning”

22
Q

This model became the industry standard for evaluating organizational analytics maturity.

A

DELTA+ Model

23
Q

True or False?
DELTA+ Model offers a summarized assessment of analytical capabilities, from data collection to strategic use.

A

False.
DELTA+ Model offers a COMPREHENSIVE ASSESSMENT of analytical capabilities, from data collection to strategic use.

24
Q

DELTA+ Model compromises of what components?

A
  • D = Data
  • E = Enterprise
  • L = Leadership
  • T = Targets
  • A = Analytics Professional
  • +T = Technology
  • +A = Analytical Techniques
25
Q

This framework centers on Data Quality, Accessibility, and Security.

A

D - Data

26
Q

Uses five levels to gauge an organization’s status and offers concise evaluation, guiding from foundational assessments to advanced practices in these critical areas.

A

D - Data

27
Q

Levels of D - Data

A

(IDKCC)
1 - Inconsistent, low quality, and unstandardized data.
2 - Data is primarily standardized and structured.
3 - Key data domain have been identified.
4 - Central repositories contain integrated, accurate, and commonly shared data.
5 - Continuous pursuit of new data and metrics.

28
Q

This framework is centered around the effective management of analytics resources, emphasizing seamless coordination and collaboration across the entirety of the enterprise.

A

E - Enterprise

29
Q

Levels of E - Enterprise

A

(ATEKS)
1 - Absence of an enterprise-wide perspective on data.
2 - The existence of islands of data, technology, and expertise.
3 - Emphasis on analytics.
4 - Key data, technology, and analytics professionals are strategically managed.
5 - Strategic focus is directed toward aligning key analytical resources.

30
Q

This framework is anchored in robust and committed leadership that possesses a profound understanding of the significance of analytics.

A

L - Leadership

31
Q

Unwavering commitment is evident through consistent advocacy for the integration of analytics in decision-making processes and actions throughout the organization.

A

L - Leadership

32
Q

Levels of L - Leadership

A

(M-L-Sd-Sp-E)
1 - Minimal awareness of or interest in analytics.
2 - Local leaders are emerging.
3 - Senior leaders demonstrate a recognition of the crucial importance of analytics.
4 - Senior leaders are proactively involved in formulating analytical plans.
5 - Effective leaders exhibit analytical behavior.

33
Q

This framework is crafted with a central focus on the strategic identification and selection of pivotal organizational targets.

A

T - Targets

34
Q

Carefully chosen targets serve as the cornerstone, laying the foundation for a comprehensive analytics roadmap.

A

T - Targets

35
Q

Levels of T - Targets

A

(Tc-Te-Ae-Ai-A)
1 - The current landscape presents a challenge.
2 - The existing scenario features multiple disconnected targets.
3 - Analytical efforts are converging around a concise set of critical targets.
4 - Analytics initiatives are concentrated on a select few key business domains.
5 - Analytics has become an integral component.

36
Q

This framework is designed with a central focus on cultivating and fostering a cadre of high-performing analytics professionals.

A

A - Analytics Professional

37
Q

Prioritizes the development and support of individuals with expertise in analytics to ensure excellence and effectiveness within the organization.

A

A - Analytics Professional

38
Q

Levels of A - Analytics Professional

A

(L-I-A-ToA-ToB)
1- Limited number of skills.
2 - Isolated pockets of analytics professionals.
3 - Analytics professionals are acknowledged as key talent.
4 - The organization actively recruits, develop, deploys, and engages analytics professionals.
5 - The organization boasts a cadre of world-class professional analytics experts.

39
Q

This framework is built around the strategic integration of technologies to bolster analytics capabilities across the organization, ensuring a cohesive and efficient use of advanced tools for informed decision-making.

A

+T - Technology

40
Q

Levels of +T - Technology

A

(T-A-Tem-TemP-ToB)
1 - The current state involves desktop technology.
2 - Analytical efforts are conducted through individual initiatives.
3 - The organization employs an enterprise-wide analytical plan
4 - The organization employs an enterprise-wide analytical plan AND PROCESSES
5 - The organization boasts a sophisticated, enterprise-wide big data and analytics infrastructure.

41
Q

This framework revolves around the incorporation of a diverse range of analytical techniques, spanning from fundamental descriptive statistics to advanced machine learning methodologies.

A

+A - Analytical Techniques

42
Q

Levels of +A - Analytical Techniques

A

(Tc-A-Ta-U-To)
1 - The current analytical approach is predominantly ad-hoc.
2 - Analytical methods encompass basic statistics.
3 - The analytical approach involves employing simple predictive analysis.
4 - Utilizing advanced predictive methods.
5 - The organization leverages cutting-edge technologies.

43
Q

What are the 5 Stages of Analytics Maturity?

A

(AI-LA-AA-Compa-Compe)
- Analytically Impaired
- Localized Analytics
- Analytical Aspirations
- Analytical Companies
- Analytical Competitors

44
Q

The organization faces challenges in conducting serious analytical work due to the absence of one or several prerequisites, including insufficient data, a shortage of analytical skills, or limited interest from senior management.

A

AI - Analytically Impaired

45
Q

While there are pockets of analytical activity within the organization, there is a lack of coordination and strategic focus.

A

LA - Localized Analytics

46
Q

Disparate efforts may not alight with overarching strategic targets, hindering the organization’s ability to maximize the impact of its analytical initiatives.

A

LA - Localized Analytics

47
Q

The organization aspires towards a more analytical future and has successfully established analytical capabilities with several significant initiatives currently underway.

A

AA - Analytical Aspirations

48
Q

The pace of progress is hindered by challenges, often stemming from the difficulty of implementing critical factors necessary for advancement in this analytical journey.

A

AA - Analytical Aspirations

49
Q

While the organization possesses the requisite human and technological resources and consistently applies analytics throughout its operations, there is a notable absence of a strategic focus grounded in analytics.

A

Compa - Analytical Companies

50
Q

Despite realizing benefits across various aspects of the business, the organization has yet to leverage analytics as a distinct competitive advantage.

A

Compa - Analytical Companies

51
Q

The organization has elevated analytics to a distinctive business capability, regularly leveraging it as a core strength.

A

Compe - Analytical Competitors

52
Q

Adopting an enterprise-wide approach, the organization benefits from committed and involved leadership, resulting in the achievement of large-scale, transformative results through the strategic application of analytics.

A

Compe - Analytical Competitors

fundamental of analytics (3 decks)

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