Module 3 Flashcards
Introduced by the Analytics Association of the Philippines
Professional Maturity Model
This model recommends maximum proficiency levels for various roles within the analytics domain, streamlining workforce skills for enhanced organizational efficiency.
Professional Maturity Model
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)
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)
In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ 21CR)
Data Steward
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
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
In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ DR. CMS, age 21)
Data Engineer
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)
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)
In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ 21CD-RMS)
Data Scientist
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)
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)
In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ 21RaceCars)
Functional Analyst
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)
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)
In the role of _____________, they should have the following proficiency in these competencies: (DDOD w/ MCR and 21DaysofSummer)
Analytics Manager
When was the general idea of the DELTA+ Model first introduced?
2007 in a book made by Thomas Davenport and Jeanne Harris.
What was the initial name of the DELTA+ Model?
“5 Stages of Analytics Maturity” in the book: “Competing Analytics: The New Science of Winning.”
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”
When did the DELTA Model introduce two new components?
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.
DELTA+ Model
True or False?
DELTA+ Model offers a summarized assessment of analytical capabilities, from data collection to strategic use.
False.
DELTA+ Model offers a COMPREHENSIVE ASSESSMENT of analytical capabilities, from data collection to strategic use.
DELTA+ Model compromises of what components?
- 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
