Data Management Masterclass – The Complete Course

Introduction
1 Introduction
2 How to get the most out of this course

The Basics of Data Management
3 What is Data Management
4 Data Management Subject Areas

Basics of effectively working with data for Data Management
5 Step 1 – Summarize the data
6 Data Centrality – Mean number
7 Data Centrality – Median number
8 Data Centrality – Mode number
9 Data Dispersion
10 Data Dispersion – Range
11 Data Dispersion – Standard Deviation
12 Data Dispersion – Inter-Quartile range
13 Data Replication
14 Data Shape – Histograms
15 EXERCISE – Summarize the data
16 Step 2 – Drill-Down on the data
17 EXERCISE – Drill-down on the data

Data Management in the AI Era
18 What Data Management in the AI Era really means
19 How AI relies on good data (the garbage in, garbage out problem)
20 Key differences between traditional data and AI data
21 What kinds of data AI uses (text, numbers, images, videos, logs)
22 Training data vs. real-time (operational) data
23 Why AI needs large, diverse, and accurate datasets
24 Data quality in AI (clean, complete, unbiased)
25 Who owns AI training data (data ownership & responsibility)
26 Privacy rules in the AI era (GDPR, consent, sensitive data)
27 Where AI data is stored (lakes, warehouses, cloud systems)
28 How data flows into AI tools (simple pipeline concepts)
29 Monitoring AI systems (checking results and correcting mistakes)
30 What can go wrong – bias, hallucinations, privacy leaks
31 Data ethics – fairness, transparency, accountability
32 The role of humans in AI data management (stewards, reviewers, auditors)

Hands-on Practice – AI for Data Management
33 Data Quality Analysis with AI (duplicates, gaps, formatting, errors and more)
34 Data Quality Management – fixing data issues with AI
35 Creating Data Quality Business Rules with AI
36 Data standardization using AI (ChatGPT)
37 Data categorization using AI (ChatGPT)
38 Comparing good vs bad data and how AI reacts
39 Identifying bias in a sample dataset (e.g., over-represented group)
40 Checking for privacy risks in a dataset (PII like names, IDs, emails)
41 Fixing the dataset privacy risks with ChatGPT

Hands-on Practice – End to End Data Analysis with AI
42 Project intro – exploring the synthetic dataset
43 Quick summarization of unknown file
44 Summary statistics
45 Check Relationships Quickly (Correlation Heatmap)
46 Trends analysis using AI
47 Scatter plot and regression analysis using ChatGPT
48 Behavior analysis using box plotting
49 Regression analysis to see main drivers for success
50 Clustering analysis using AI
51 Summary dashboard creation using AI
52 Prepare professional final report for leadership
53 Close the project with communication preparation

AI Data Management Case Studies
54 Case study – when AI chatbot gone wrong
55 Case study – AI trained on biased data (e.g., hiring, credit scoring)
56 Case study – AI privacy scandal (e.g., personal data leaks)
57 Case study – AI success story from good data management
58 Case study – Government & healthcare data challenges in AI

How to boost your career with AI
59 Step 1 – Understand the scope of the AI job opportunity
60 Step 2 – AI foundational knowledge
61 Step 3 – Apply AI to Your Domain
62 Step 4 – Propose a project or showcase portfolio
63 Step 5 – Keep on learning and seize opportunities

Importance of Data Ethics for Data Management
64 Intro to Data Ethics
65 Case Study – Example of impact of poor data ethics
66 Goals of Data Ethics
67 Risk of Unethical Data Handling
68 Example of bad Data Ethics – part 1
69 Example of bad Data Ethics – part 2
70 Example of bad Data Ethics – part 3
71 Example of bad Data Ethics – part 4
72 Key Data Ethics Activities
73 Deliverables of Data Ethics
74 Data Ethics Frameworks
75 OECD Data Ethics Principles
76 EU AI Act
77 ISO – IEC 38507
78 NIST AI Risk Management Framework
79 DCAM & DAMA – Ethical Data Governance
80 IEEE 7000 Series (7001, 7002, 7003, 7010)
81 UNESCO AI Ethics
82 World Economic Forum – Responsible Data Framework
83 Summary – Comparing Leading Data Ethics Frameworks
84 Establishing Ethical Data Culture

Data Governance – The Umbrella of Data Management
85 Data Governance in simple language
86 Why is Data Governance Important
87 Relationship to Other Data Principles intro
88 Governance + Data Quality = Trusted Data
89 Governance + Data Security = Safe Data
90 Governance + Data Architecture = Organized Data
91 Governance + Data Ethics = Responsible Data
92 Governance + Master Data Management = Consistent Data
93 Governance + Metadata Management = Contextual Data
94 Governance + Analytics & BI = Credible Insights
95 Governance + AI & Machine Learning = Accountable Intelligence
96 Data Governance Roles and Responsibilities

Data Architecture
97 What is Data Architecture
98 Data Architecture main components
99 Data Architecture Frameworks
100 Foundational Structural Paradigms
101 Centralized Data Architecture
102 Federated Data Architecture
103 Distributed Data Architecture
104 Functional Orientation – Operational (OLTP) vs Analytical Data Architecture (OLAP
105 Operational Data Architecture (OLTP)
106 Analytical Data Architecture (OLAP)
107 How OLTP and OLAP Work Together
108 Data Storage & Integration Patterns in Data Architecture
109 Data Warehouse Architecture
110 Data Lake and Data Lakehouse Architectures
111 Modern Enterprise Evolution Patterns
112 Data Mesh Architecture
113 Data Fabric Architecture
114 Data Architecture Best Practices

Data Modeling and Design
115 What is Data Modeling
116 Difference between Data Architecture and Data Modeling
117 The 3 Levels of Data Models
118 Data Modeling Process
119 Benefits of Data Modeling
120 Data Modeling Tools

Data Storage and Operations
121 What is data storage and operations
122 Why is data storage management important
123 Intro to Data Storage Systems
124 File Storage Systems
125 Block Storage Systems
126 Object Storage Systems
127 Relational Databases (RDBMS)
128 Non-Relational (NoSQL) Databases
129 Data Warehouses
130 Data Lakes
131 Hybrid and Cloud Storage
132 Distributed and Decentralized Storage
133 What daily storage management actually involves
134 The 4 key attributes of Data Storage Management
135 Data Backup, Recovery & Archiving
136 Data Storage Security & Access Control
137 Data Retention & Lifecycle Management
138 Performance Optimization & Monitoring
139 Best Practices to keep in mind
140 Trends & Emerging Technologies in Data Storage

Data Security
141 What is Data Security
142 Why is Data Security important
143 The Goals of Data Security
144 The Principles of Data Security
145 Types of Data Security
146 Data Security Risks Intro
147 Accidental Data Exposure
148 Phishing
149 Malware
150 Insider Threats
151 Password Attack
152 Denial-of-Service
153 Man-in-the-Middle (MITM)
154 SQL Injections
155 Zero-day Exploit
156 Data Security Activities intro
157 Identify Data Security Requirements
158 Define Data Security Policies
159 Define Data Security Standards
160 Best Practices for better Data Security

Data Integration and Interoperability
161 What is Data Integration
162 Example of Data Integration
163 Importance of Data Integration
164 Techniques for Data Integration
165 Manual Data Integration
166 Middleware Data Integration
167 Application Based Integration
168 Uniform Access Integration
169 Common Storage Integration (Data Warehousing)
170 Data Virtulization
171 ETL
172 ELT
173 ETL vs ELT
174 Data Integration Tools
175 Data Integration Best Practices

Document and Content Management
176 What is Document and Content Management
177 Why do we need Document & Conent Management
178 DMS (Document Management System)
179 CMS (Content Management System)
180 ECMS (Enterprise Content Management System)
181 Best Content Solutions to choose from

Master & Reference Data Management
182 What is Master Data
183 Example of how Master Data works
184 What is Reference Data
185 Example of Reference Data
186 Master Data vs Reference data – how to differentiate

Data Warehousing and Business Intelligence
187 What is Data Warehousing and Business Intelligence
188 Data Warehouse
189 Data Warehouse Components
190 Database vs Data Warehouse
191 Data Marts
192 Data Warehouses vs Data Lakes
193 Issues with the Data Warehousing approach
194 How Data Lakes solve some of the Data Warehousing limitations
195 What is Business Intelligence
196 Business Intelligence vs Business Analytics
197 Applications of Business Intelligence
198 Examples of Business Intelligence
199 BI analysis categories – Descriptive, Diagnostic, Predictive, Presriptive
200 Creating a Business Intelligence Strategy intro
201 BI Strategy Step 1 – Get Sponsorship
202 BI Strategy Step 2 – The BI team
203 BI Strategy Step 3 – Stakeholders
204 BI Strategy Step 4 – BI platforms and tools
205 BI Strategy Step 5 – Define the scope
206 BI Strategy Step 6 – The Roadmap
207 BI Strategy Step 7 – Implementation
208 BI and big data
209 The importance of self-service within Business Intelligence
210 Business Intelligence Tools

Metadata Management
211 What is Metadata
212 Example of Metadata
213 What is Metadata Management
214 Types of metadata Intro
215 Descriptive metadata
216 Structural metadata
217 Administrative metadata
218 Metadata Management tools
219 Hands-on – Creating Metadata Standards and Policies document (Template included)

Data Quality
220 Data Quality
221 Dimensions of Data Quality
222 Causes of Poor Data Quality
223 Assessing and Measuring Data Quality
224 Data Cleansing and Standardization
225 Data Quality Management Processes
226 Tools and Technologies for Data Quality
227 Data Quality Governance and Policies
228 Building a Data Quality Culture
229 Trends and Future of Data Quality
230 Practice – Data Quality Exercise with Excel
231 Practice Solution – Data Quality Exercise with Excel
232 Practice – Analyzing data quality of a file using AI
233 Practice – Communicate Data Issues to leadership
234 Practice – Data Deduplication
235 Practice – Fix formatting issues
236 Practice – Data Standardization
237 Practice – Creating data validation rules at the source
238 Practice – Prepare Data Quality Policy Document

Big Data and Data Science
239 What is Big Data
240 Case Study of Uber – using Big Data in the real world
241 What is Data Science
242 The Abate Information Triangle
243 The Goals of Big Data
244 Key Activities in Big Data
245 The Deliverables of Big Data
246 The Data Science Lifecycle

Clinical Data Management – quick intro
247 Why I am teaching you the basics of Clinical Data Management
248 What is Clinical Data Management
249 What are Clinical Trials
250 What is Clinical Research
251 Importance of data in CDM
252 Roles in Clinical Data Management

Understand the different roles and careers in Data Management
253 The Landscape of Data Management Careers
254 How to go about selecting the appropriate career path
255 Sample Career Pathways

Careers in Data Leadership & Strategy
256 Chief Data Officer (CDO)
257 Head – Director of Data
258 Data Strategy Manager – Lead
259 Data Portfolio – Program Manager
260 Data Product Director
261 Data PMO Lead

Careers in Data Governance, Quality & Compliance
262 Data Governance Manager
263 Data Steward
264 MDM Specialist
265 Data Quality Analyst
266 Data Catalog Specialist
267 Privacy & Compliance Officer
268 Data Ethics Officer
269 Records – Information Manager

Careers in Data Architecture & Infrastructure
270 Enterprise Data Architect
271 Solution – Domain Data Architect
272 Cloud Data Architect
273 Information Architect
274 Data Modeler
275 Database Administrator (DBA)
276 Data Warehouse Architect

Data Engineering & Integration
277 Data Engineer
278 Analytics Engineer
279 ETL – ELT Developer
280 Data Pipeline Developer
281 Integration Specialist
282 Big Data Engineer

Data Operations, Reliability & FinOps
283 DataOps Engineer
284 Observability Engineer
285 Data Reliability Specialist
286 FinOps Analyst (cloud data costs)
287 Incident Manager (data platforms)

Business Intelligence & Analytics
288 BI Analyst
289 BI Developer
290 Reporting Analyst

Advanced Analytics, Data Science & AI
291 Data Scientist
292 Machine Learning Engineer (MLE)
293 MLOps Engineer
294 AI – LLM Engineer (NLP, CV, GenAI)
295 Quantitative Analyst – Modeler

Data Security, Privacy & Risk
296 Data Security Engineer
297 IAM (Identity & Access Management) Specialist
298 Data Privacy Analyst – Engineer
299 Risk & Controls Analyst
300 Cybersecurity Data Specialist

Domain & Industry-Specific Data Teams
301 Clinical Data Manager (Healthcare)
302 Risk – Regulatory Data Analyst (Finance)
303 KYC – AML Data Analyst
304 Government Data Specialist (Public Sector)
305 IoT – Telemetry Data Engineer (Industrial)
306 GIS – Location Intelligence Specialist (Geospatial)
307 Digital Asset Manager – Ontologist (Media – Content)

Data Enablement, Culture & Education
308 Data Enablement Lead
309 Data Trainer – Educator
310 Community Practice Lead
311 Data Change Manager

Consulting, Advisory & Innovation
312 Independent Data Consultant
313 Fractional CDO
314 Data Solution Architect (consulting firms)
315 Data Innovation Lead
316 Data Researcher – Experimentation Specialist

Future Trends in Data Management Careers
317 The Evolution of Data Management careers
318 The Changing Nature of Data Roles
319 Technology Trends Shaping Data Careers
320 Impact of Technology on Career Paths
321 The Skills of the Future

What Next
322 Thank You
323 Bonus Lecture