In this tutorial, I will write posts about Big Data for Business use. These posts are for people without deep technical skills and provide an overview on various topics of data handling. The goal of this tutorial is to give you a business understanding of data handling and to introduce some core concepts.

1 Intro to Big Data Business topics

2 Dealing right with Data: pitfalls you should avoid when working with data

2.1 Why labs often don’t work on the example of a silicon valley-like lab

2.2 When Data Science goes wrong – a practical sample

2.3 Include the human in data science

2.4 How to destroy your data strategy in 5 steps

3 Building a data strategy step-by-step

3.1 What data do I need in order to be successful? An overview of the 3 data sources you need

3.2 How to make data accessible within a company

3.3 How to setup a data science team

4 Agile data science projects

4.1 Doing data science projects agile

4.2 Data DevOps and BizDevOps for agile Data Science projects

4.3 Should you do Kanban or Scrum for Data Science?

5 Architectural considerations

5.1 Hadoop is not the answer eventually

5.2 Should you use a Kappa or a Lambda architecture?

5.3 Is there a need for Data Governance?

5.4 The half life of data for data lifecycle management

5.5 The data lake as driver for digital transformation

5.6 Don’t build Hadoop platforms on your own

6 Data for Business Verticals

6.1 Why data is so important for Marketing Automation

7 Social impacts of AI

7.1 Explainable AI for greater social good

7.2 Is AI eventually destroying us?

If you need to learn more about Big Data, I can recommend you this report (German only)