Genres: Information Systems (IT), Popular Science Literature
Year of publication: 2019
Year of reading: 2021
Normal
Number of reads: 1
Total pages: 256
Summary (pages): 8
Original language of publication:
Russian
Translations to other languages: No translations to other languages found
General description
330-page book consisting of 8 chapters. Material is primarily textual, though occasionally contains images, schemas, and diagrams. Reads easily and quickly. Largely a theoretical book about large-scale technologies.
Table of contents
Chapter 1. What is Big Data?
- Martian dialects
- What it actually is and where it came from
- Post-information society
- Data-driven organizations
- 7 steps for data-driven decision culture
- Value of data-driven organization
- Data-informed organizations
- Data-informed or data-driven
- Open-source revolution and accessibility of technologies
- 4th Industrial Revolution, or why humans are no longer needed for insight discovery
Chapter 2. Data Strategy
- Where does the data strategy start?
- Data lifecycle
- Company mission and data
- Key stakeholders
- Technical infrastructure
- Why is data strategy needed?
- How does company culture affect strategy success?
- Who owns the data strategy?
- Self-service BI
- How to measure data strategy success?
- Cost of implementing data strategy?
Chapter 3. Data Storytelling
- The ideal story answers key questions
- Your dashboard is dead
- Decoding analytical content requires effort
- Impact investment – every story should have a purpose
Chapter 4. Data Regulation
- Severe European conservators
Chapter 5. Metadata
Chapter 6. Why is Data Quality Needed?
- Key data quality management methods
- How to measure data quality?
- How to choose quality measurements?
- Data quality management tools
Chapter 7. Not a Single Big Data: Platforms and Ecosystems
- PaaS and platforms
Chapter 8. And What's Next? Challenges and Trends
- Current challenges with Big Data
- We think we understand Big Data
- How to calculate financial impact?
- Big Data may not be needed at all
- Where are we heading? Trends
- Machine learning is increasingly used
Opinion
Despite being a largely theoretical book, I wouldn't dismiss it as useless. It offers something new and useful: GDPR, data-driven vs data-informed organizations, data lifecycle (not apps, but data), some frameworks and libraries for analytics and data visualization. Thus, I recommend reading it. By the way, it's one of the few books I own in printed form, though I, as usual, read it electronically.
