Module 3 Flashcards by Russ Unknown

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

Professional Maturity Model

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The Professional Maturity Model was introduced by what association?

Analytics Association of the Philippines

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A Data Steward should have proficiency in which competencies?

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

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

(DDODRC2) [312]
Entry - Expert

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

(DDODRC2) [312]
Entry - Expert

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

(DDODRC2) [312]
Entry - Expert

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

(DDODRC2) [312]
Entry - Intermediate

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

(DDODRC2) [312]
Entry

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

(DDODRC2) [312]
Entry

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A Data Engineer should have proficiency in which competencies?

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

(ODe DgDDRMSC2) [216]
Entry - Expert

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

(ODe DgDDRMSC2) [216]
Entry - Expert

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

(ODe DgDDRMSC2) [216]
Entry - Intermediate

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

(ODe DgDDRMSC2) [216]
Entry

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

(ODe DgDDRMSC2) [216]
Entry

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

(ODe DgDDRMSC2) [216]
Entry

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

(ODe DgDDRMSC2) [216]
Entry

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

(ODe DgDDRMSC2) [216]
Entry

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

(ODe DgDDRMSC2) [216]
Entry

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A Data Scientist should have proficiency in which competencies?

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

(ODeRMSC DDD2) [630]
Entry - Expert

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

(ODeRMSC DDD2) [630]
Entry - Expert

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

(ODeRMSC DDD2) [630]
Entry - Expert

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

(ODeRMSC DDD2) [630]
Entry - Expert

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

(ODeRMSC DDD2) [630] Entry - Expert

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

(ODeRMSC DDD2) [630] Entry - Expert

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

(ODeRMSC DDD2) [630] Entry - Intermediate

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

(ODeRMSC DDD2) [630] Entry - Intermediate

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

(ODeRMSC DDD2) [630] Entry - Intermediate

A Functional Analyst should have proficiency in which competencies?

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

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

(DDOa DgRC2) [312] Entry - Expert

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

(DDOa DgRC2) [312] Entry - Expert

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

(DDOa DgRC2) [312] Entry - Expert

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

(DDOa DgRC2) [312] Entry - Intermediate

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

(DDOa DgRC2) [312] Entry

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

(DDOa DgRC2) [312] Entry

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

(ODDD DeRMSC2) [405] Entry - Expert

An Analytics Manager should have proficiency in which competencies?

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

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

(ODDD DeRMSC2) [405] Entry - Expert

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

(ODDD DeRMSC2) [405] Entry - Expert

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

(ODDD DeRMSC2) [405] Entry - Expert

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

(ODDD DeRMSC2) [405] Entry

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

(ODDD DeRMSC2) [405] Entry

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

(ODDD DeRMSC2) [405] Entry

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

(ODDD DeRMSC2) [405] Entry

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

(ODDD DeRMSC2) [405] Entry

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

True

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

False (introduced the DELTA Model)

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

DELTA + Model

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

True

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

DELTA + Model

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

False (From data GATHERING to strategic use)

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

True

What are the components of the DELTA+ Model?

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

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

Data

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

Data (offers a concise evaluation)

What are the 5 levels in Data

(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

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

Enterprise (Centered around the effective management)

What are the 5 levels in Enterprise?

(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

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

Leadership (Robust and committed leadership)

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.

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

What are the 5 levels of Leadership

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

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

Targets (Focus on the strategic identification and selection)

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

Targets (Carefully chosen)

What are the 5 levels of Targets

(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

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

Analytics Professional (development and support of individuals with expertise)

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

Analytics Professional (focus on cultivating and fostering a cadre)

What are the 5 levels of Analytics Professionals

(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

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.

Technology (built around the strategic integration of technologies)

What are the 5 levels of Technology

(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

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

Analytical Techniques (diverse range of analytical techniques)

What are the 5 levels of Analytical Techniques?

- 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

What are the 5 stages of Analytics Maturity?

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

Module 3 Flashcards

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