Module 3 Flashcards by Russ Unknown | Brainscape

Q

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

A

Professional Maturity Model

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Q

The Professional Maturity Model was introduced by what association?

A

Analytics Association of the Philippines

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Q

A Data Steward should have proficiency in which competencies?

A

(DDODRC2)
Domain Knowledge
Data Governance
Operational Analytics
Data Visualization
Research Methods
Computing
21st Century Skills

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Q

For a Data Steward, what should be their level of proficiency in Domain Knowledge?

A

(DDODRC2) [312]
Entry - Expert

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Q

For a Data Steward, what should be their level of proficiency in Data Governance?

A

(DDODRC2) [312]
Entry - Expert

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Q

For a Data Steward, what should be their level of proficiency in Operational Analytics?

A

(DDODRC2) [312]
Entry - Expert

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Q

For a Data Steward, what should be their level of proficiency in Data Visualization?

A

(DDODRC2) [312]
Entry - Intermediate

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Q

For a Data Steward, what should be their level of proficiency in Research Method?

A

(DDODRC2) [312]
Entry

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Q

For a Data Steward, what should be their level of proficiency in Computing?

A

(DDODRC2) [312]
Entry

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Q

A Data Engineer should have proficiency in which competencies?

A

(ODe DgDDRMSC2) [216]
Operational Analytics
Data Engineering
Data Governance
Domain Knowledge
Data Visualization
Research Methods
Methods and Algorithms
Statistical Techniques
Computing
21st Century Skills

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Q

For a Data Engineer, what should be their level of proficiency in Operational Analytics?

A

(ODe DgDDRMSC2) [216]
Entry - Expert

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Q

For a Data Engineer, what should be their level of proficiency in Data Engineering?

A

(ODe DgDDRMSC2) [216]
Entry - Expert

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Q

For a Data Engineer, what should be their level of proficiency in Data Governance?

A

(ODe DgDDRMSC2) [216]
Entry - Intermediate

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Q

For a Data Engineer, what should be their level of proficiency in Domain Knowledge?

A

(ODe DgDDRMSC2) [216]
Entry

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Q

For a Data Engineer, what should be their level of proficiency in Data Visualization?

A

(ODe DgDDRMSC2) [216]
Entry

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Q

For a Data Engineer, what should be their level of proficiency in Research Method?

A

(ODe DgDDRMSC2) [216]
Entry

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Q

For a Data Engineer, what should be their level of proficiency in Methods and Algorithms?

A

(ODe DgDDRMSC2) [216]
Entry

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Q

For a Data Engineer, what should be their level of proficiency in Statistical Techniques?

A

(ODe DgDDRMSC2) [216]
Entry

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Q

For a Data Engineer, what should be their level of proficiency in Computing?

A

(ODe DgDDRMSC2) [216]
Entry

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Q

A Data Scientist should have proficiency in which competencies?

A

(ODeRMSC DDD2) [630]
Operational Analytics
Data Engineering
Research Methods
Methods and Algorithms
Statistical Techniques
Computing
Domain Knowledge
Data Visualization
Data Governance
21st Century Skills

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Q

For a Data Scientist, what should be their level of proficiency in Operational Analytics?

A

(ODeRMSC DDD2) [630]
Entry - Expert

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Q

For a Data Scientist, what should be their level of proficiency in Data Engineering?

A

(ODeRMSC DDD2) [630]
Entry - Expert

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Q

For a Data Scientist, what should be their level of proficiency in Research Methods?

A

(ODeRMSC DDD2) [630]
Entry - Expert

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Q

For a Data Scientist, what should be their level of proficiency in Methods and Algorithms?

A

(ODeRMSC DDD2) [630]
Entry - Expert

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Q

For a Data Scientist, what should be their level of proficiency in Statistical Techniques?

A

(ODeRMSC DDD2) [630]
Entry - Expert

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Q

For a Data Scientist, what should be their level of proficiency in Computing?

A

(ODeRMSC DDD2) [630]
Entry - Expert

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Q

For a Data Scientist, what should be their level of proficiency in Domain Knowledge?

A

(ODeRMSC DDD2) [630]
Entry - Intermediate

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Q

For a Data Scientist, what should be their level of proficiency in Data Visualization?

A

(ODeRMSC DDD2) [630]
Entry - Intermediate

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Q

For a Data Scientist, what should be their level of proficiency in Data Governance?

A

(ODeRMSC DDD2) [630]
Entry - Intermediate

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Q

A Functional Analyst should have proficiency in which competencies?

A

(DDOa DgRC2) [312]
Domain Knowledge
Data Visualization
Operational Analytics
Data Governance
Research Methods
Computing

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Q

For a Functional Analyst, what should be their level of proficiency in Operational Analytics?

A

(DDOa DgRC2) [312]
Entry - Expert

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Q

For a Functional Analyst, what should be their level of proficiency in Data Visualization?

A

(DDOa DgRC2) [312]
Entry - Expert

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Q

For a Functional Analyst, what should be their level of proficiency in Domain Knowledge?

A

(DDOa DgRC2) [312]
Entry - Expert

Q

For a Functional Analyst, what should be their level of proficiency in Data Governance?

A

(DDOa DgRC2) [312]
Entry - Intermediate

Q

For a Functional Analyst, what should be their level of proficiency in Computing?

A

(DDOa DgRC2) [312]
Entry

Q

For a Functional Analyst, what should be their level of proficiency in Research Methods?

A

(DDOa DgRC2) [312]
Entry

Q

For a Analytics Manager, what should be their level of proficiency in Operational Analytics?

A

(ODDD DeRMSC2) [405]
Entry - Expert

Q

An Analytics Manager should have proficiency in which competencies?

A

(ODDD DeRMSC2) [405]
Operational Analytics
Data Visualization
Data Governance
Domain Knowledge
Data Engineering
Research Methods
Methods and Algorithms
Statistical Methods
Computing

Q

For a Analytics Manager, what should be their level of proficiency in Domain Knowledge?

A

(ODDD DeRMSC2) [405]
Entry - Expert

Q

For a Analytics Manager, what should be their level of proficiency in Data Governance?

A

(ODDD DeRMSC2) [405]
Entry - Expert

Q

For a Analytics Manager, what should be their level of proficiency in Data Visualization?

A

(ODDD DeRMSC2) [405]
Entry - Expert

Q

For a Analytics Manager, what should be their level of proficiency in Data Engineering?

A

(ODDD DeRMSC2) [405]
Entry

Q

For a Analytics Manager, what should be their level of proficiency in Research Methods?

A

(ODDD DeRMSC2) [405]
Entry

Q

For a Analytics Manager, what should be their level of proficiency in Methods and Algorithms?

A

(ODDD DeRMSC2) [405]
Entry

Q

For a Analytics Manager, what should be their level of proficiency in Statistical Techniques?

A

(ODDD DeRMSC2) [405]
Entry

Q

For a Analytics Manager, what should be their level of proficiency in Computing?

A

(ODDD DeRMSC2) [405]
Entry

Q

True or False. The general idea of the model is first introduced in 2007 by book of Thomas Davenport and Jeanne Harris.

A

True

Q

True or False. in 2010, Robert Morison joined and formally introduced the DELTA+ model in the book “Analytics at Work: Smarter Decisions, Better Results”.

A

False
(introduced the DELTA Model)

Q

It is introduced as the “5 Stages of Analytics Maturity” in the book “Competing on Analytics: The New Science of Winning.”

A

DELTA + Model

Q

In 2017, the DELTA+ Model was introduced with two new components in the updated “Competing Analytics: The New Science of Winning”.

A

True

Q

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

A

DELTA + Model

Q

True or False. The DELTA+ Model offers a comprehensive assessment of analytical capabilities, from data creation to strategic use.

A

False
(From data GATHERING to strategic use)

Q

True or False. The DELTA+ Model’s adaptability ensures continued relevance in the evolving landscape of data analytics.

A

True

Q

What are the components of the DELTA+ Model?

A

(DELTATA)
Data
Enterprise
Leadership
Targets
Analytics Professional
+
Technology
Analytical Techniques

Q

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

A

Data

Q

It offers a concise evaluation, guiding from foundational assessments to advanced practices in these critical areas.

A

Data
(offers a concise evaluation)

Q

What are the 5 levels in Data

A

(IDKCCp)
- Inconsistent, low-quality, and unstandardized
- Data is primarily standardized
- Key data domains have been identified
- Central repositories contains integrated, accurate, and commonly shared data.
- Continuous pursuit of new data and metrics

Q

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

A

Enterprise
(Centered around the effective management)

Q

What are the 5 levels in Enterprise?

A

(ATEKS)
- Absence of an enterprise-wide perspective
- The existence of islands of data
- Emphasis on analytics
- Key data, technology, and analytics professionals are strategically managed
- Strategic focus is directed towards aligning key analytical resources

Q

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

A

Leadership
(Robust and committed leadership)

Q

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

A

Leadership
(consistent advocacy for the integration of analytics in decision-making)

Q

What are the 5 levels of Leadership

A

(MLS SpE)
- Minimal awareness
- Local leaders are emerging
- Senior leaders demonstrate a recognition
- Senior leaders are proactively involved
- Effective leaders exhibit analytical behavior

Q

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

A

Targets
(Focus on the strategic identification and selection)

Q

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

A

Targets
(Carefully chosen)

Q

What are the 5 levels of Targets

A

(TcTe AeAiA)
- The current landscape presents a challenge
- The existing scenario features multiple disconnected targets
- Analytical efforts are converging
- Analytics incentives are concentrated
- Analytics has become an integral component

Q

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

A

Analytics Professional
(development and support of individuals with expertise)

Q

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

A

Analytics Professional
(focus on cultivating and fostering a cadre)

Q

What are the 5 levels of Analytics Professionals

A

(LIARC)
- Limited number of skills are associated
- Isolated pockets of analytics professionals
- Analytics professionals are acknowledged
- The organization actively recruits
- The organization boasts a cadre

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

Technology
(built around the strategic integration of technologies)

Q

What are the 5 levels of Technology

A

(Cs AI POb)
- The current state involves desktop technology
- Analytical efforts are conducted through individual initiatives
- The organization employs an enterprise-wide analytical plan, incorporating dedicated tools
- The organization employs an enterprise-wide analytical plan and processes
- The organization boasts a sophisticated, enterprise-wide big data and analytics infrastructure

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

Analytical Techniques
(diverse range of analytical techniques)

Q

What are the 5 levels of Analytical Techniques?

A

The current analytical approach is predominantly ad-hoc
Analytical methods encompass basic statistics
The analytical approach involves employing basic predictive analytics
Utilizing advanced predictive methods
The organization leverages cutting edge technologies

Q

What are the 5 stages of Analytics Maturity?

A

(IAACC)
- Analytically Impaired
- Localized Analytics
- Analytical Aspirations
- Analytical Companies
- Analytical Competitors

Q

In this stage of Analytics Maturity, 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

Analytically Impaired
(faces challenges, absence of one or several prerequisites)

Q

In this stage of Analytics Maturity, while there are pockets of analytical activity within the organization, there is a lack of coordination and strategic focus.

A

Localized Analytics
(pockets of analytical activity, lack of coordination and strategic focus)

Q

In this stage of Analytics Maturity, disparate efforts may not align with overarching strategic targets, hindering the organization’s ability to maximize the impact of its analytical initiatives.

A

Localized Analytics
(efforts may not align, hindering the organization’s ability to maximize)

Q

In this stage of Analytics Maturity, the organization aspires towards a more analytical future and has successfully established analytical capabilities with several significant initiatives currently underway.

A

Analytical Aspirations
(towards a more analytical future and has successfully established analytical capabilities)

Q

In this stage of Analytics Maturity, 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

Analytical Aspirations
(hindered by challenges, stemming from the difficulty of implementing critical factors)

Q

In this stage of Analytics Maturity, 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

Q

A

Q

In this stage of Analytics Maturity, the organization has elevated analytics to a distinctive business capability, regularly leveraging it as a core strength.

A

Q

In this stage of Analytics Maturity, 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