11.1 VUCA Ecosystem
2367.2.2 Operationalised Analytics
21.2 Industrial Generations
2377.3 Connecting Decisions and Analytics
31.3 Four Types of Trends
2387.4 Data Analysis Methods Commonly used in Business
41.3.1 Social Trends
2397.4.1 Cluster Analysis
51.3.2 Organizational Trends
2407.4.2 Cohort Analysis
61.3.3 Business Trends
2417.4.3 Regression Analysis
71.3.4 Technology Trends
2427.4.4 Factor Analysis
81.4 The Experience Economy
2437.4.5 Text Analysis
91.5 Mechanization and Automation
2447.5 Summary
10Evolution
2457.6 Exercise
111.6 Digital Economy
246References
121.7 Intelligence Analysis
247Figure Resources
131.7.1 data
2488.1 What is Business Security?
141.7.2 Communication
2498.1.1 Tenets/ Fundamental Principles / CIA Triads
151.7.3 Information
2508.1.1.1 Confidentiality
161.7.4 Concept
2518.1.1.2 Integrity
171.7.5 Knowledge
2528.1.1.3 Availability
181.7.6 Intelligence
2538.2 Security Attacks
191.7.7 wisdom
2548.2.1 Interruption
201.7.8 Business Intelligence
2558.2.2 Interception
211.7.8.1 Business Intelligence Maturity Model
2568.2.3 Modification
221.7.8.2 Business Analytics
2578.2.4 Fabrication
231.7.8.3 Business Analytics Maturity Model
2588.2.5 Passive Attack
241.8 Intelligence Data Analysis (IDA)
2598.2.6 Active Attack
251.9 SMACT Technologies
2608.3 Threat v/s Vulnerability v/s Risk
261.10 Summary
2618.3.1 Risk
271.11 Exercise
2628.3.2 Threat
28References
2638.3.3 Vulnerability
29Figure Resources
2648.4 Security Controls
302.1 Business Process Management (BPM)
2658.4.1 Types
312.1.1 Definition
2668.4.1.1 Physical
322.1.2 Steps Involved
2678.4.1.2 Logical
332.1.2.1 Design
2688.4.1.3 Administrative
342.1.2.2 Model
2698.4.2 Control
352.1.2.3 Execute
2708.4.2.1 Preventative
362.1.2.4 Monitor
2718.4.2.2 Detective
372.1.2.5 Optimize
2728.4.2.3 Corrective
382.2 Business Process Reengineering (BPR)
2738.5 Protection and Defence
391. Outlining the present condition of your business procedures
274• Save only what is necessary.
402. Analysing and finding process disconnections or delays
275• Keep a list of information.
413. Seeking for upgrading prospects and authorizing them
276• Keep abreast of your network security.
424. Designing a leading-edge imminent process map
277• Safely store physical documents.
435. Instigating impending state variations by being aware of needs
278• Use a business credit card to pay the fee.
442.3 Collaborative Technologies
279• Establish internal controls to prevent employee fraud.
452.3.1 Definition
280• Monitor your employees’ accounts.
462.3.2 Types
281• Sign a strong work agreement.
472.3.2.1 Synchronous or Real-Time Collaborative Software
282• Plan your response to data breaches.
482.3.2.1 Asynchronous or Non-Real Time Collaborative Software
283• Use a firewall
492.3.3 Tools
284• Document your cybersecurity policy
502.3.4 Collaborative Management
285• Mobile Device Planning
512.4 Summary
286• Educating All Employees
522.5 Exercise
287• Regularly back up all data
53References
288• Install Anti-Malware Software
54Figure Resources
2898.6 Access Control
553.1 Business Approach
2908.6.1 Access Control Models
563.1.1 Strategy
2918.6.1.1 Discretionary Access Control (DAC)
573.1.2 Porter’s Contribution
2928.6.1.2 Mandatory access control (MAC)
583.1.2.1 Porter’s Five Forces
2938.6.1.3 Role-based access control (RBAC)
593.1.2.2 Porter’s Generic Strategies
2948.7 Elements of Access Control
601. The Cost Leadership Strategy
2958.7.1 Identification
612. The Differentiation Strategy
2968.7.2 Authentication
623. The Focus Strategy
2978.7.3 Authorization
633.1.2.3 Porter’s Value Chain
2988.8 Summary
643.2 Information Technology (IT)
2998.9 Exercise
653.2.1 Development and Progress
300References
663.2.2 Business-IT alignment
301Figure Resources
673.3.3 Business Value
3029.1 NoSQL Database
683.2 Information System (IS): 3.3.1 Development and Progress
3039.1.1 Introduction
693.4 Business Strategy parallel to IT/IS
3049.1.2 Types
703.5 IT Governance: 3.5.1 Governance Frameworks
3059.1.1.1 Document Databases
713.6 Data-Driven Decision Making (DDDM)
3069.1.1.2 Key-Value Databases
723.7 Enterprise Data Warehouse
3079.1.1.3 Column-Oriented Databases
733.8 Summary
3089.1.1.4 Graph Databases
743.9 Exercise
3099.1.2 CAP Theorem
75References
3109.1.2.1 Consistency
76Figure Resources
3119.1.2.2 Availability
774.1 An Insight to Data Mining
3129.1.2.3 Patience Tolerance
784.1.1 Data Mining Cycle
3139.1.3 CAP Theorem in NoSQL
794.1.2 Methods
3149.1.3.1 CP Database
804.1.2.1 Classification
3159.1.3.2 AP Database
814.1.2.2 association
3169.1.3.3 CA Database
824.1.2.3 Prediction
3179.1.4 ACID and BASE Philosophy
834.1.2.4 Clustering
3189.1.4.1 ACID Model
844.1.2.5 Regression
319A – Atomicity:
854.1.2.6 Sequential Pattern
320C – Consistency:
864.1.2.7 Decision Trees
321I – Isolated:
874.1.3 Supervised v/s Unsupervised Methods
322D – Durability:
884.1.4 Results
3239.1.4.2 BASE Model
894.2 Business and Corporate Problems
324B – Basically, A – Available:
904.3 Business Questions with Data Science Answers
325S – Soft State:
914.3.1 Business Strategy
326E – Eventually Consistent:
924.3.2 Supply Chain
3279.2 NoSQL based – applications
934.3.3 Human Resources
3289.3 NoSQL Business Drivers
944.3.4 Sales and Marketing
3299.3.1 Volume
954.4 Top Questions for Business and Data Science
3309.3.2 Velocity
964.4.1 Business Strategy
3319.3.3 Variability
974.4.2 Customer
3329.3.4 Agility
984.4.3 Operations
3339.4 Summary
994.5 Summary
3349.5 Exercise
1004.6 Exercise
335References
101References
336Figure Resources
102Figure Resources
33710.1 Big Data Business Model Maturity Index
1035.1 Defining Big Data
338(BDBMMI)
1045.2 Five V’s of Big Data (Characteristics)
33910.1.1 Introduction
1055.2.1 Volume
34010.1.2 Applying BDBMMI
1065.2.2 Velocity
341Phase 1: Business Monitoring
1075.2.3 Variety
342Phase 2: Business Monitoring
1085.2.4 Veracity
343Phase 3: Business Optimization
1095.2.5 Value
344Phase 4: Insights Optimization
1105.3 Big Data Management
345Phase 5: Metamorphosis Phase
1115.3.1 Definition
34610.2 Big Data Maturity Model
1125.3.2 Challenges
34710.2.1 Descriptive: 10.2.1.1 Big data and analytics maturity model (IBM model)
1131. Data Storage
34810.2.2 Comparative
1143. Data Size
34910.2.2.1 TDWI big data analytics maturity model
1153. Architectural Complexity
350Nascent
1163. Data Quality
351Pre-adoption
1174. Lack of Trained Staff
352Early adoption (The Chasm)
1185. Lack of Administrative Support
353Corporate adoption
1196. Adopting Big Data culture
354Mature
1205.3.3 Best Practices
35510.2.3 Prescriptive: 10.2.3.1 Radcliffe big data maturity model
1211. Active Participation of Employees
35610.3 Summary
1222. Written Approach
35710.4 Exercise
1233. Data Security
358References
1244. Access Management
359Figure Resources
1255. Training Personnel
36011.1 Business Intelligence Platforms
1266. Appointing CDO
36111.1.1 Tableau
1275.3.4 Benefits
36211.1.2 Looker
1281. Increased Profit
36311.1.3 Microsoft Power BI
1292. Enhanced User Experience
36411.1.4 IBM Watson Analytics
1303. Improved Marketing
36511.1.5 Salesforce Einstein Analytics
1314. Amplified Efficacy
36611.2 Business Intelligence System Entities
1325. Decreased Expenses
36711.2.1 Data Mart
1336. New Services
36811.2.2 Business Activity Monitoring
1347. Precise Analytics
36911.2.3 Real-Time BI
1358. Competitive Lead
370 11.2.4 Portals
1365.3.5 Services
371 11.2.5 Business Performance Management
1375.3.6 Vendors
37211.3 Business Intelligence Users
1385.4 Big Data Architecture
37311.3.1 Business Users
1395.4.1 Definition
37411.3.2 IT Users
1405.4.2 Layers
37511.3.3 Professional Data Analyst
1415.4.2.1 Sources Layer
37611.3.4 Executive/ Head of Compny
1425.4.2.2 Management and Storage Layer
37711.4 Self Service Business Intelligence (SSBI)
1435.4.2.3 Analysis Layer
378Operators can reply to their own questions
1445.4.2.4 Consumption Layer
379Decisions and understandings supported by data
1455.4.2.5 BI Layer
380Effectiveness permits businesses to acquire a competitive edge
1465.4.2.6 Ingestion Layer
381Sanction your IT & analyst workers emphasis on advanced priorities
1475.4.2.7 Processing Layer
38211.5 Summary
1485.4.2.8 Collector Layer
38311.6 Exercise
1495.4.2.9 Query Layer
384References
1505.4.2.10 Security Layer
385Figure Resources
1515.4.2.11 Monitoring Layer
38612.1: Case Study 1: Walmart – Revolutionizing
1525.4.2.12 Visualisation Layer
387Supermarket Performance using Big Data
1535.4.3 Technology Stack
38812.2: Case Study 2: Netflix – Using Big Data for
154Flume
389Entertainment Programmes
155Pulsar
39012.3: Case Study 3: LinkedIn – Transforming
156Kudu
391Resumes and Jobs
157Spark
39212.4: Case Study 4: Uber – Big Data for
158Hive
393Transportation
159Kylin
39412.5: Case Study 5: BBC – In Media and
160Kubernetes
395Broadcasting
161Azkaban
396The challenge
162Zookeeper
397The action
163Yarn
398Putting insight in the driving seat.
1645.4.4 Processes
399The result
1655.4.5 Best Practices
40012.6: Case Study 6: Amazon’s Dynamo
166Initial Phase
40112.7: Case Study 7: eBay – Customer’s Journey
167Data Sources
402Managing the customer journey: The true value of analytics
168Extract, Transform, Load
40312.8: Case Study 8: Rolls Royce – Engine and
1695.4.6 Generic Big Data Architecture
404Big Data
1705.5 Big Data Types
40512.9: Case Study 9: Airbnb – Space and Big Data
1715.5.1 Structured
406Data –The Lifeblood of Business at Airbnb
1725.5.2 Unstructured
407Data Science at Airbnb
1735.5.3 Semi-Structured
4081) A/B Testing
1745.6 Advantages
4092) Image Recognition and Analysis
1755.7 Summary
4103) Natural Language Processing
1765.8 Exercise
4114) Predictive Modelling
177References
4125) Regression Analysis
178Figure Resources
4136) Collaborative Filtering
1796.1 Decision in Data Science
414Hadoop Workflow System at Airbnb – Airflow
1806.1.1 Types
415How Airbnb used big data to propel its growth?
1811. Programmed and non-programmed decisions
4161) Enhanced Search Features
1822 Routine and strategic decisions
4172) Guiding Hosts to the Perfect Price
1833. Tactical (Policy) and operational decisions
4183) Driving Company Growth
1844. Organizational and personal decisions
41912.10: Case Study 10: Spotify – Music and Big Data
1855 Major and minor decisions
420Big Data and Spotify:
1864. Individual and group decisions
421How Spotify collects data:
1876.1.2 Scopes
422How Spotify processes data:
1886.2 Data Analytics for Business Decision Making
423Processing Concepts:
1896.2.1 Descriptive Analytics
424Strengths:
1906.2.2 Diagnostic Analytics
425Weaknesses:
1916.2.3 Predictive Analytics
426Opportunities:
1926.2.4 Prescriptive Analytics
427Threats/Challenges:
1936.3 Decision–Making
42812.11: Case Study 11: Facebook – In Social Media
1946.3.1 Decision Problem Classification
429The Facebook Context
1956.3.1.1 Based on Problem’s Complexity
430Examples
1961. Structured Decisions
431Example 1: The Flashback
1972. Semi-structured Decisions
432Example 2: I Voted
1983. Unstructured Decisions
433Example 3: Celebrate Pride
1996.3.1.2 Based on Level of Problem
434Example 4: Topic Data
2001. Strategic Planning
435The Downsides: Privacy Issues
2012. Management Control
436Two Problems with Facebook:
2023. Operational Control
43712.12: Case Study 11: ZSL – Wildlife and Big Data
2036.3.1.3 Based on Tactic Assumption
438Google Cloud Results: Taking the pulse of the planet in real-time
2041. Rational Decision Making
43912.13: Case Study 13: Royal Bank of Scotland –
2052. Nonrational Decision Making
440Banking and Big Data
2063.Irrational Decision Making
44112.14: Case Study 14: Milton Keynes – Smart
2076.3.2 Main Decision Support Techniques
442Cities and Big Data
2086.3.2.1 Mathematical Programming
44312.15: Case Study 15: Ralph Lauren – Fashion
2096.3.2.2 Multicriteria Decision Making (MCMD)
444and Big Data
2106.3.2.3 Case-Based Reasoning
445Company description
2116.3.2.4 Fuzzy Sets and Systems
446Initiatives on data analytics.
2126.3.4 Decision Support Systems
447Data analytics and applications.
2136.3.4.1 Components
448Another Ralph Lauren Insight:
2141. Data Management
449Figure Resources
2152. Model Management
450A
2163. User Interface Management
451B
2174. Knowledge Management
452C
2186.4.3.2 Categories
453D
2191. Data-driven DSS
454E
2202. Model-driven DSS
455F
2213. Knowledge-driven DSS
456G
2224. Document-driven DSS
457H
2235. Communication-driven DSS
458I
2246.5 Summary
459L
2256.6 Exercise
460M
226References
461N
227Figure Resources
462O
2287.1 Analytics
463P
2297.1.1 Business Analytics
464Q
2307.1.2 Web Analytics
465R
2317.1.3 Social Media Analytics
466S
2327.1.4 Entity Analytics
467T
2337.1.5 Business Analytics v/s Data Analytics
468V
2347.2 Big Data and Big Data Analytics
469W
2357.2.1 Advanced Analytics
