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Summary Introduction to Data Management
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  • Course
  • Introduction to Data Management
  • Institution
  • Avans Hogeschool (Avans)
  • Book
  • Business Intelligence

This summary will help you understanding the different aspects in your Data Management class and there are useful questions for the interviews for your DM project.

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Document information

  • No
  • Chapters in the module manual
  • September 11, 2020
  • 5
  • 2019/2020
  • Summary

Subjects

  • data management
  • business strategies
  • ib

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  • Avans Hogeschool (Avans)
  • International Business & Management
  • Introduction to Data Management
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Introduction to Data Management
Week 1
Chapter 1: Introduction to Data Science

The business environment is changing, which asks for responses, and innovative
approaches.

Data: Setting the Scene / Data Analytics / Business Impact / Data Management.

Setting the Scene:
- What is data?
- Why is it relevant?
- Where do data come from?
- What is Big Data?
- What is Machine Learning? And AI?

Data Management:
- Data creation
- Difference structured / unstructured.
- Storage issues
- DAMA – DMBOK; different aspects of data.
- How about governance?
- Privacy – GDPR; impact on business.
- How about IoT – Beacons – RFID.

Data analytics.
- Algorithms.
- Can we do prediction – association – clustering/
- What is the difference between supervised and unsupervised?
- Data & text mining.

Business Impact:
- Business intelligence

Use of data:
- New technologies make it possible: storage / preparation / processing/
- Endless use: primary, secondary.
- Few companies have all the power: ownership 80%, ability to use it 20%, ideas how
to use it 2%.

Different effects:
- New business models.
1. Brokers: collect, combine / mix, sell.
2. Ownership of large corpora has enormous value.
- Risks? Wrong people, anonymized data, interpretation.
- Data decisions needs human support.

Big Data:
- Volume: too big to share.
- Variety: different shapes (un)structured, streaming, static, clicks, sensors, posts, etc.
- Velocity: ability to produce and to process (computing power).
- Use algorithms to analyze; prediction (classification, regressions) / association /
clustering.

, Machine learning and AI:
- Based on a big data set you tell the computer what a success is.
- Based on a sample from the data set the algorithm starts to look for the variables that
are responsible for this ‘success’ (training without being programmed explicitly by
rules).
- By doing this repeatedly the machine starts to learn and becomes better in predicting
‘success’ based on the available data.

Data = all over the place; has enormous value; is used for all kinds of decisions making.
Technology makes it happen. Big data requires specific technologies.

Analytics in the retail value chain:




Week 2
Chapter 2 (79-101) Chapter 7 (399-404)

Techniques that make up AI:

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