Lecture 1 - Business Analytics Trends

Question 1

5V’s of Big Data

Answer 1

1. Volume
2. Variety
3. Velocity
4. Value
5. Veracity

Question 2

What is the technical challenge of data? (2)

Answer 2

1. How to deal with volume of the data

2. How to deal with real time data processing

Question 3

What is the managerial challenge with data?

Answer 3

To manage the data that comes in from everywhere and integrate these data.

Question 4

Degenerative tendency?

Answer 4

To focus inward on coasts and efforts, rather than outward on opportunities, changes and threats.

Pro of big data (comes from external sources) is that it force companies to use external data instead of only internal data.

Question 5

What is the most valuable thing you can do with big data?

Answer 5

Developing new products and services

Question 6

Caveat 1: Big data and thick data

Answer 6

Beware of quant bias or quant addiction; don’t trust too much only on quantitative data, also look at what is not measurable.

Question 7

Thick Data is?

Answer 7

Data in which emotions are visible and the context where the data is collected is taken into account.

• Big data needs to be supported with Thick data.
• Context can relativise or give meaning to the data.

Question 8

Caveat 2: Relate to business challenges

Answer 8

The challenge is not to just start exploring the data but to be aware of your business goals and focus your data analytic efforts on solving these
problems and improving these decisions.

Question 9

Why Big Data now? combination of different causes (4)

Answer 9

1. Availability of massive amounts of digital data
2. Combination of technical developments and societal needs
3. A philosophical view: Rationalism vs empiricism
4. The discovery, in science itself, of the power of data

Question 10

Why Big Data now: Technological developments (6)

Answer 10

Radical changes in: (now vs. how it was)
1. The way elementary data are captured
o Sensors (automated) vs keyboard (human)
2. The way data is stored → business intelligence (data warehouses, data analytics etc.)
o Main memory and cloud vs hard disk
3. The way data is analyzed
o Data-driven methods vs sampling
4. The way data is provided to users
o Data logistics (keeping data together) vs data integration
5. The way data is presented → visualization
o Graphical interactive visualizations vs management reports
6. The way knowledge (business rules, models) is created → by means of machine learning/
data mining
o Learning/mining vs (labor-intensive) knowledge acquisition

Question 11

Rationalism vs empiricism

Answer 11

Rationalism: theory, thinking and abstract knowledge
Empiricism: rely on data, empirical data

Now we are in the empirical time.

Question 12

Researchers view

Answer 12

More data is more important than better algorithms.

The more data you use for the training of the algorithm, the better the performance of the algorithm.

Question 13

How did we go from Big data to AI? (3)

Answer 13

1. The gap between the data and what you can do with it becomes bigger.
2. Data became the problem, but the problem empowered the solution.
3. Big data enabled AI machine learning techniques, which become a solution of solving these gaps.

Question 14

How are AI technologies driven by data and analytics? (3)

Answer 14

1. We use historical data for training
2. New data for re-training
3. With the models, make predictions

Question 15

Three types of learning:

Answer 15

1. Supervised: try to predict the variable you want to learn
2. Unsupervised: don’t have any initial variable to learn
3. Reinforcement: learn by trying out

Question 16

From action to a decision (4):

Answer 16

1. Monitor (action) > data
2. Contextualize the data > information
3. Learning from information > knowledge
4. Information + knowledge = decision

Question 17

How does Provosts (2012) position data science? (3)

Answer 17

1. Data science is somewhere between data engineering as part of computer science, the technology part and data driven decision making.
2. Automated data driven decision making is in the area of business analytics.
3. Data science uses the techniques from AI, data management to support this automated data drive decision making (DDD).

Question 18

Data science

Answer 18

A set of fundamental principles that support and guide the principled extraction of information and knowledge from data.

Question 19

Data mining

Answer 19

The actual extraction of knowledge from data, via technologies that incorporate these principles.

Question 20

Data-driven decision making (DDD)

Answer 20

The practice of basing decisions on the analysis of data, rather than purely on intuition.

Question 21

Data sciences principles (Provost, 2012): (2)

Answer 21

1. Entities that are similar with respect to known features or attributes are similar with respect to unknown features or attributes
2. Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages.

Question 22

More data science principles: (4)

Answer 22

1. If you look too hard you will find something but it might not generalise beyond the data.
2. To draw causal conclusions, look at confounding factors, possibly unseen ones.
3. Look if you found a local optimum or the right optimum.
4. decompose the problem into sub-problems

Question 23

Process mining

Answer 23

Learn about the process model from the data