Introduction to Analytics

Module 1: Introduction to Analytics

• Learners should be able to define data, identify data value chain, differentiate analytics disciplines, identify analytics professions, and define decision support systems.

Data in the Digital World

• Data is the new resource that is mined and refined.
• Analytics is the process of refining data.

Data Value Chain

• It is a complete process, including creation, collection, processing, analyzing, and extracting the value of data within an organization.
• It covers the lifecycle of data, from the origin to its use in decision-making and insights.

Data Creation

• The creation can be generated from Biodata, Machine Logs, Bank Information, Medical Records, Social Media Posts

Data Collection, Storage and Usage

• Most data originates from human activity, fundamentally relying on human involvement.
• Data is systematically collected and securely stored within applications used by organizations.

Information Extraction and Consolidation

• Involves extracting and consolidating data into a single repository.
• Steps include Data Cleaning, Data Categorization, Data Transformation, and Data Aggregation.
• Data becomes information after this.
• This process answers the question "what happened".

Insights Discovery

• Uncovers patterns and trends
• It helps answer the questions: Why did it happen? and What is likely to happen next?

Imperatives Translation

• Analyzed data is translated into imperatives and recommendation for future actions.
• It helps answer the question: What steps should be taken next?

Disciplines in the Data Value Chain

• Data
  • Data Governance: Policies for quality and compliance.
  • Data Management: End-to-end oversight of data processes.
  • Data Security: Safeguarding data from unauthorized access.
  • Data Ethics: Responsible and ethical data handling.
• Information
  • Data Engineering: Building data systems.
  • Data Warehousing: Managing structured data.
  • Data Architecture: Designing data systems.
  • Business Intelligence: Extracts insights for decisions.
  • Descriptive Analytics Summarizes historical data
• Insights
  • Data Mining: Extracting insights.
  • Algorithms: Processing data steps.
  • Machine Learning: To learn and improve from experience.
  • Diagnostic Analytics: It is responsible for analyzing data why an event occurred.
  • Predictive Analytics identifies future events based on historical data.
• Imperatives
  • Optimization: Enhances efficiency.
  • Simulation: Models real-world scenarios.
  • Prescriptive Analytics: Responsible for recommending actions derived from descriptive and predictive analysis.

Analytics Professions

• Data Steward: Develops, enforces and maintains data security and data usage, also called Data Gatekeepers. -Business and Industry Domains -Related job include Data Privacy Officer, Data Security Officer, Data Governance Manager, Data Curator and Data Librarian
• Data Engineers: Designs, constructs, tests and maintains data, typically involved with ETL (Extract, Transform, Load). -Involved with Information Technology, Information Science and Computer Science -They work with Data Repositories where the transformed data are stored. -Related job include ETL Developer, Data architect, Data Warehousing Professional, Big Data Engineer
• Data Scientist: Applies statistical techniques and creates statistical models to make data driven predictions. -Involved with Mathematics and Statistics to make Data Driven Prediction. -Related job include Statistician, Statistical Modeler, Advanced Analytics Professional
• Function Analyst: Utilizes data, leverages derived insights, and validates data scientist insights to craft prescriptions for stakeholders. -Involved with Business and Industry Domains -Related job include Research Analyst, Human Resource Analyst, Marketing Analyst, Financial Analyst, Operations Analyst
• Analytics Manager: Develops and guides data-driven projects and uses project management. -Project Management

Decision Support System (DSS)

• Analytics is referred to as a decision support system, which enables organizations to make data-driven decisions.
• A DSS allows businesses to have a Data Value Chain, which provides end user with data, information, insights and prescribed actions.
• The end user has the final say regarding whether to act upon the tool that gives options, not a command for decision-making.
• Analytics using a decision support system drives digital process such as smart appliances, self-driving cars, manufacturing, all AI applications are supported by Analytics.

Module 2: Analytics Skills and Competencies

• Learners should be able to identify analytics competencies, define business skills, define technical skills and define workplace skills.

Analytics Competencies

• Encompasses Business and Organization Skills, Technical Skills, and Workplace Skills.
• Have three level proficiency expectations, including entry level, immediate level and expert level.

Business and Organization Skills

• Includes Domain Knowledge and Application, Data Management and Governance, Operational Analytics, Data Visualization and Presentation.

Domain Knowledge and Application

• It involves domain-related knowledge and insights to effectively contextualize data.
• This defines a functional analyst with industry knowledge, business experience and domain expertise.
• Entry Level: Comprehend the collected data, and grasp the methods by which they are managed and applied within the specific industry domain.
• Immediate Level: Craft a comprehensive content strategy and design an effective information architecture tailored to support the unique needs of a given industry domain and its diverse audiences.
• Expert Level: Formulate compelling business cases aimed at enhancing domain-related procedures by leveraging data-driven decision-making strategies.

Data Management and Governance Skills

• It involves developing and implementing data management strategies, enforcing privacy and data security, implementing data policies and regulations, and understanding ethical considerations.
• This defines a data steward which are also known as the data gatekeepers.
• Entry Level: Maintain vigilant awareness and consistently implement policies and measures to uphold data security, privacy, intellectual property, and ethical standards.
• Immediate Level: Effectively implement and enforce policies and procedures pertaining to data security, privacy, intellectual property, and ethical considerations.
• Expert Level: Formulate comprehensive policies addressing data security, privacy, intellectual property, and ethical considerations.

Operation Analytics

• Involves general and specialized knowledge of business analytics and insight derivation for decision-making.
• These skills define an Analytics manager as they have project management skills.
• Entry Level: Conduct comprehensive business analysis on designated tasks and datasets.
• Immediate Level: Determine the business implications arising from identified trends and patterns.
• Expert Level: Discover fresh opportunities to leverage historical data for optimizing organizational processes.

Data Visualization and Presentation.

• Creating and communicating compelling and actionable insights using data visualization techniques like storytelling.
• Entry Level: Create data visualization reports or narratives according to specified requirements.
• Immediate Level: Design infographics to facilitate the effective presentation and communication of actionable outcomes.
• Expert Level: Choose suitable visualization methods and innovate new approaches tailored to a specific industry.

Technical Skills

• Five distinct competencies are research methods, data engineering principles, statistical techniques, data analytics, methods, and algorithms, and computing.

Research Methods

• It involves utilizing scientific and engineering methods to discover and create new knowledge and insights.
• They encompass strategies, processes, and techniques. They are utilized in collection of data to uncover new information.
• -Entry Level: Employ the 4-step research model, comprising hypothesis formulation, research methods selection, artifact creation, and evaluation, to enhance understanding and application in research endeavors.
• -Immediate Level: Formulate research questions centered on identified issues within established research or business process models.
• -Expert Level: Create experiments incorporating both passive and active data collection methods to facilitate hypothesis testing and effective problem-solving.

Data Engineering Principles

• Utilize software and system engineering to develop data analytics applications.
• It defines a Data Engineer, it encompasses the ETL Method (Extract, Transform, Load), bringing data into one repository.
• -Entry Level: Proficiency in programming selected SQL and NoSQL platforms for data storage and access, with a specific focus on writing Extract, Transform, Load (ETL) scripts.
• -Immediate Level: Architect and construct both relational and non-relational databases, ensuring the implementation of efficient Extract, Transform, Load (ETL) processes tailored for large datasets.
• -Expert Level: Demonstrated advanced expertise in leveraging modern Big Data technologies for processing diverse data types sourced from multiple channels.

Statistical Techniques

• Involves applying statistical concepts and methodologies for data analysis.
• This is defined by Data Scientist, it encompasses Mathematics and Statistics to analyze raw data especially from a research data to extract information.
• -Entry Level: Possess proficiency in employing statistical methods, including sampling, ANOVA, hypothesis testing, descriptive statistics, regression analysis, and other relevant methodologies.
• -Immediate Level: Evaluate and recommend the most suitable statistical methods and tools tailored to specific tasks and datasets.
• -Expert Level: Recognize issues within collected data and propose corrective measures, encompassing additional data collection, inspection, and pre-processing as needed.

Data Analytics, Methods and Algorithms

• It involves implementing and evaluating machine learning methods and algorithms to derive insights from data for decision making.
• It encompasses Algorithm and Machine Learning and used to identify the best methods or algorithms to extract insights from the data.
• -Entry Level: Illustrate comprehension of statistical hypothesis testing and proficiently conduct such tests, providing clear explanations regarding the statistical significance of collected data.
• -Immediate Level: Apply quantitative techniques, such as time series analysis, optimization, and simulation, to deploy suitable models for analysis and prediction.
• -Expert Level: Evaluate data reliability and appropriateness, while choosing suitable approaches to consider impact on analysis and the quality of results.

Computing

• Apply information technology, computational thinking, programming languages for analysis, and software and hardware solutions..
• It is defined by both Data Scientist and Data Engineering.
• -Entry Level: Conduct fundamental data manipulation, analysis, and visualization tasks proficiently.
• -Immediate Level: Utilize computational thinking to translate formal data models and algorithmic processes into program code.
• -Expert Level: Choose suitable application and statistical programming languages, as well as development platforms, tailored to specific processes and datasets.

Workplace Skills

• It is a necessary skill for any type of field with essential skills, including critical thinking, communication, collaboration, creativity/attitude, planning/organizing, business fundamentals, customer focus, working with tools/technology, dynamic (self) re-skilling, professional networking, and ethics.

Module 3: Analytics Maturity Model

• Learners should be able to define the professional maturity model, define DELTA+ model and define organizational maturity models.

Professional Maturity Model

• Introduced by the Analytics Association of the Philippines and recommends maximum proficiency levels for various roles within the analytics domain and streamlining workforce skills for enhanced organizational efficiency.
• Data Steward Domain Knowledge ,Data Governance, Operational Analytics, Data Visualization ,Research Methods , Computing and 21st Century Skills
• Data Engineer Domain Knowledge, Data Governance, Operational Analytics, Data Visualization ,Research Methods, Data Engineering ,Statistical Techniques ,Methods and Algorithms ,Computing and 21st Century Skills
• Data Scientist Domain Knowledge ,Data Governance ,Operational Analytics, Data Visualization Data Engineering, Statistical Techniques, Methods and Algorithms ,Computing and 21st Century Skills
• Functional Analyst Domain Knowledge Data Governance, Operational Analytics Data Visualization, Research Methods, Computing and 21st Century Skills
• Analytics Manager Domain Knowledge, Data Governance, Operational Analytics, Data Visualization, Research Methods ,Data Engineering ,Statistical Techniques, Methods and Algorithms ,Computing and 21st Century Skill

DELTA+ Model

• First Introduced in 2007 by Thomas Davenport and Jeanne Harris as the 5 stages of Analytics Maturity.
• Robert Morison joined as part of DELTA and it has five components and described in the book "Analytics at Work: Smarter Decisions, Better Results" which later updated in 2017 and updated the first book "Competing Analytics: The New Science of Winning”. This makes the DELTA+ Model which introduces two new components.
• The current industry standard for evaluating organizational analytics maturity that is adaptable in analytical capabilities from data collection to strategic use.
• The seven components are Data (D), Enterprise (E), Leadership (L), Targets (T),Analytics Professional (A),Technology and Analytical Techniques.

Model by Level

• Data framework is centered on Data Quality, Accessibility, and Security that uses five levels to gauge an organization's status, guiding from foundation assessments to advanced practices.
  • Level 1: Inconsistent, low-quality, unstandardized data
  • Level 2: Standardized structured silos of data without management discussion from senior executives
  • Level 3: Key data domains identified, and central data repositories established
  • Level 4: Central repositories contains integrated, accurate, and commonly shared data but IT concerned, little attention to needs
  • Level 5: Pursuit of new structured/unstructured data and metrics and considers data a strategic asset
• Enterprise: Centered around the effective management of analytics resources by emphasizing coordination and collaboration across the entirety of the enterprise that has five levels of evaluation.
  • Level 1: Absence of an enterprise-wide data/analytics, Poorly integrated systems
  • Level 2: Signifies localized data, technology, and expertise within specific areas/departments
  • Level 3: Analytics primarily centered around specific processes or business units
  • Level 4: Analytics are strategically managed from an enterprise-wide perspective.
  • Level 5: Aligned key analytical resources with enterprise priorities and fostering differentiation
• Leadership: Framework is anchored in robust and committedleadership that has the significance of analytics evident through advocacy for integration.
  • Level 1: Minimal awareness/interest in analytics within organization.
  • Level 2: Emergence local leaders but limited collaboration among them
  • Level 3: Senior leaders demonstrate crucial importance towards developing analytical capabilities
  • Level 4: Senior Leaders proactively involved regarding robust analytical capabilities
  • Level 5: Leaders Foster culture of analytical competition
• Targets: Crafted with focus on the strategic identification/selection or pivotal organizational targets to serve as cornerstone for analytics roadmap.
  • Level 1: Challenges without discernible opportunities for strategic interventions
  • Level 2: Features Lack strategic significance organizational
  • Level 3: Concise set of critical targets showcases strategic alignment on key objectives
  • Level 4: Focus on key domains and clear achievement of explicit and ambitious outcomes
  • Level 5: Integral component has become apart of the company's capability
• Analytical Professional Design with center focus on cultivating cadres of high performing individuals and supporting excellence.
  • Level 1: Limited number of functions for the skills
  • Level 2: Pockets without coordinated skill
  • Level 3: Talent acknowledged and expertise for business
  • Level 4: Highly capable
  • Level 5: Analytical experts
• Technology: Built around strategic integration of technology to bolster analytics capabilitoes to insure cohesive and efficient use.
  • Level 1: Office packages
  • Level 2: Efforts by packages
  • Level 3: Dedicates tools
  • Level 4: Clouded solution
  • Level 5: Cognitive technologies
• Analytical Techniques: Revolves around analytical techniques with statistics to methods.
  • Level 1: AD-HOC
  • Level 2: Query
  • Level 3: Prediction
  • Level 4: Uncover
  • Level 5: Cutting edge to push boundaries

Organizational Maturity Model

• Is the general idea of DELTA+ Model that is first introduced in 2007 and is used to measure an organization’s analytical maturity describe as the “5 Stages of Analytics Maturity” in their book “Competing on Analytics: The New Science of Winning
• Five Stages
  • Organizationally Impaired: Challenges in analytical work due to absence of prerequisites and data.
  • Localized Analytics: Pockets of analytical activity, lacks coordination and strategic focus.
  • Analytical Aspirations: Aims for a analytical future with established capabilities with significant initiatives underway where hindering the progress due to critical factors
  • Analytical Companies: Has the human and technological resources with a notable absence of a strategic focus.
  • Analytical Competitors: Leverages strong analytical core, organization with committed and involved leadership with large transformative results.