Anaïs Cai
Data Driven Management
,Anaïs Cai
Inhoudsopgave
1 What – data 5
1.1 Introductie 5
1.2 Data 5
1.3 History of data 5
1.3.1 Vroeger floppy disks 5
1.3.2 Hard disks 5
1.3.3 The cloud 5
1.4 Data producers 5
1.5 Processen 6
1.6 Technologie 7
1.7 Havens 8
1.8 Big data V's 8
1.9 Types of data 9
2 Data visualisatie 9
2.1 What? 9
2.2 Why? 9
2.2.1 Seeing is understanding 9
2.2.2 Visualization is the first step 10
2.2.3 Descriptive analytics 10
2.2.4 Diagnostic analytics 10
2.2.5 Predicitive analystics 10
2.2.6 Prescriptive analytics 10
2.2.7 Analysis complexity vs. human input 11
2.3 How to visualize 11
2.3.1 How not? 11
2.3.2 How? 11
2.4 Dashboarding 11
2.4.1 Maak je dashboard 12
3 Why - value 12
3.1 Value streams 12
3.2 Data use cases (UC) 13
3.3 Business value pyramids 15
3.3.1 Business-to-Consumer (B2C) piramide 15
3.3.2 Business-to-Business (B2B) piramide 16
4 What – Data tools 17
4.1 Analytical tools 17
4.2 Digital applications 17
4.3 AI Prompts 17
5 What – Data products 18
5.1 Data products 18
5.1.1 Use case view 18
5.2 Data product parts 19
5.2.1 Dataset 19
5.2.2 Meta data 19
5.2.3 Physical format 19
5.3 Data product integration 20
,Anaïs Cai
6 What – Data transformations 21
6.1 DIKW Framework 21
6.2 Typical data transformations 21
6.2.1 Conversion 21
6.2.2 Aggregation 22
6.2.3 Filtering 22
6.2.4 Advanced 22
6.2.5 Integration 22
7 Storytelling 23
7.1 The Story of Ignaz Semmelweis 23
7.2 Data storytelling 23
7.3 Best practices 24
7.3.1 Story structure 24
7.3.2 Providing context 24
7.4 4 D’s 25
7.5 Using text & visual clues 25
7.5.1 Algemene richtlijnen 25
7.5.2 Headlines 25
7.5.3 Graphical & textual cues 25
7.5.4 Annotations vs commentary 26
8 AI 26
8.1 History 26
8.2 AI capabilities 27
8.3 AI models 29
8.4 AI model quality 30
8.5 Generative AI 30
8.6 Trust & ethics 31
9 How – data platform (technologie) 31
9.1 Introductie 31
9.2 Process 32
9.2.1 Store (opslag) 32
9.2.2 Ingest - invoer 34
9.2.3 Transform & deliver 34
9.3 Pipelines 34
9.4 Security 35
9.5 Observability 35
10 How – data management 35
10.1 Data lifecycle 35
10.1.1 Create 36
10.1.2 Store 36
10.1.3 Use 36
10.1.4 Share 36
10.1.5 Archive 36
10.1.6 Destroy 36
10.2 Data governance 36
10.3 Data management 37
10.3.1 Data architecture 37
10.3.2 Data model & design 38
10.3.3 Data storage & operations 39
10.3.4 Data security 39
3
, Anaïs Cai
10.3.5 Data integration & interoperability 39
10.3.6 Data quality 39
10.3.7 Meta data 40
10.3.8 Data Warehouse 40
10.4 Organization 40
4
Data Driven Management
,Anaïs Cai
Inhoudsopgave
1 What – data 5
1.1 Introductie 5
1.2 Data 5
1.3 History of data 5
1.3.1 Vroeger floppy disks 5
1.3.2 Hard disks 5
1.3.3 The cloud 5
1.4 Data producers 5
1.5 Processen 6
1.6 Technologie 7
1.7 Havens 8
1.8 Big data V's 8
1.9 Types of data 9
2 Data visualisatie 9
2.1 What? 9
2.2 Why? 9
2.2.1 Seeing is understanding 9
2.2.2 Visualization is the first step 10
2.2.3 Descriptive analytics 10
2.2.4 Diagnostic analytics 10
2.2.5 Predicitive analystics 10
2.2.6 Prescriptive analytics 10
2.2.7 Analysis complexity vs. human input 11
2.3 How to visualize 11
2.3.1 How not? 11
2.3.2 How? 11
2.4 Dashboarding 11
2.4.1 Maak je dashboard 12
3 Why - value 12
3.1 Value streams 12
3.2 Data use cases (UC) 13
3.3 Business value pyramids 15
3.3.1 Business-to-Consumer (B2C) piramide 15
3.3.2 Business-to-Business (B2B) piramide 16
4 What – Data tools 17
4.1 Analytical tools 17
4.2 Digital applications 17
4.3 AI Prompts 17
5 What – Data products 18
5.1 Data products 18
5.1.1 Use case view 18
5.2 Data product parts 19
5.2.1 Dataset 19
5.2.2 Meta data 19
5.2.3 Physical format 19
5.3 Data product integration 20
,Anaïs Cai
6 What – Data transformations 21
6.1 DIKW Framework 21
6.2 Typical data transformations 21
6.2.1 Conversion 21
6.2.2 Aggregation 22
6.2.3 Filtering 22
6.2.4 Advanced 22
6.2.5 Integration 22
7 Storytelling 23
7.1 The Story of Ignaz Semmelweis 23
7.2 Data storytelling 23
7.3 Best practices 24
7.3.1 Story structure 24
7.3.2 Providing context 24
7.4 4 D’s 25
7.5 Using text & visual clues 25
7.5.1 Algemene richtlijnen 25
7.5.2 Headlines 25
7.5.3 Graphical & textual cues 25
7.5.4 Annotations vs commentary 26
8 AI 26
8.1 History 26
8.2 AI capabilities 27
8.3 AI models 29
8.4 AI model quality 30
8.5 Generative AI 30
8.6 Trust & ethics 31
9 How – data platform (technologie) 31
9.1 Introductie 31
9.2 Process 32
9.2.1 Store (opslag) 32
9.2.2 Ingest - invoer 34
9.2.3 Transform & deliver 34
9.3 Pipelines 34
9.4 Security 35
9.5 Observability 35
10 How – data management 35
10.1 Data lifecycle 35
10.1.1 Create 36
10.1.2 Store 36
10.1.3 Use 36
10.1.4 Share 36
10.1.5 Archive 36
10.1.6 Destroy 36
10.2 Data governance 36
10.3 Data management 37
10.3.1 Data architecture 37
10.3.2 Data model & design 38
10.3.3 Data storage & operations 39
10.3.4 Data security 39
3
, Anaïs Cai
10.3.5 Data integration & interoperability 39
10.3.6 Data quality 39
10.3.7 Meta data 40
10.3.8 Data Warehouse 40
10.4 Organization 40
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