Module 3 Flashcards by MATTHEW BARCELON

Introduced by the Analytics Association of the Philippines

Professional Maturity Model

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

Professional Maturity Model

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

(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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Data Steward proficiency levels:

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

Data Steward

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

(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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Data Engineer proficiency levels:

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

Data Engineer

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

(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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Data Scientist proficiency levels:

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

Data Scientist

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

(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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Functional Analyst proficiency levels:

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

Functional Analyst

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

(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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Analytics Manager proficiency levels:

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

Analytics Manager

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

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

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What was the initial name of the DELTA+ Model?

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

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When was the DELTA Model formally introduced?

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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When did the DELTA Model introduce two new components?

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

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

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DELTA+ Model

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

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

DELTA+ Model compromises of what components?

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

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

D - Data

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

D - Data

Levels of D - Data

(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.

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

E - Enterprise

Levels of E - Enterprise

(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.

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

L - Leadership

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

L - Leadership

Levels of L - Leadership

(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.

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

T - Targets

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

T - Targets

Levels of T - Targets

(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.

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

A - Analytics Professional

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

A - Analytics Professional

Levels of A - Analytics Professional

(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.

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.

+T - Technology

Levels of +T - Technology

(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.

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

Levels of +A - Analytical Techniques

(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.

What are the 5 Stages of Analytics Maturity?

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

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.

AI - Analytically Impaired

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

LA - Localized Analytics

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

LA - Localized Analytics

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

AA - Analytical Aspirations

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

AA - Analytical Aspirations

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.

Compa - Analytical Companies

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

Compa - Analytical Companies

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

Compe - Analytical Competitors

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.

Compe - Analytical Competitors

Module 3 Flashcards

(52 cards)