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Summary exam Strategic Analytics - Master strategic management

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Strategy Analytics

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Tilburg University (UVT)

summary of the web clips for exam strategic analytics for master strategic management @UVT, which is a summary of the book for the exam

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Geupload op 30 november 2021
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Geschreven in 2021/2022
Type Samenvatting

webclips
lecture
strategic management
master
strategic analytics
summary
master strategic management
exam
tilburg university
uvt

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Lecture 1 Expect slightly fewer questions from Week 1,6 & 7
Observation in our daily life:
Marketing Finance Retail
 Online advertising  Credit scoring and  Marketing
 Recommendations for cross-selling trading  Supply chain
 Customer relationship  Fraud detection management
management  Workforce management
 Nowadays data available about everything
Opportunity for data analysis to prove decision making etc.

Fundamental concepts
Data-driven decision Making (DDD) = refers to the practice of basing decision on
the analysis of data, rather than purely on intuition.
 Useful
 Their complementary

1. Data science = involves principles, processes, and techniques for
understanding phenomena via the (automated) analysis of data
 To address specific questions (business decision)
 Helps us to take good decisions in uncovering nonobvious explanations.
 Important: (1) should be something not obvious (2) use them in all situations. To
apply insights in future for similar
situations

2. Data Mining = the extraction of
knowledge form data, via
technologies that incorporate
these principles.

3. Machine learning =

Data = fact of figures (not the information itself)
Information = context for the data (after process, interpreted, organized, structured,
presents which makes it meaningful and useful)

Big Data = simply, a very large dataset. 3 characteristics:
 Volume: the quantity of generated and stored data
 Variety: the type and nature of the data
 Velocity: the speed at which the data is generated and
processed
Also: internal and external

Data Analysis = The process of examining datasets in order to draw conclusions about the
useful information they may contain.
Types:
 Descriptive Analytics: what has happened?
o Simple descriptive statistics, dashboard, charts, diagrams

,  Predictive Analytics: What could happen?
o More useful
o Segmentation, regressions
 Prescriptive Analytics: What should we do? (not in this course)
o Complex models for product planning and stock optimalization
o Most specific on what to do with the data results

Data Science capability as strategic asset
Strategic asset = data and the capability to extract useful knowledge from data can be
strategic asset

Delta model:
Data (clean, accessible and unique) - Enterprise (focus) - Leaders - Targets - Analysts
 improve decision making and be a step ahead your competitors

From business problems to data mining tasks
A collaborative problem-solving between business stakeholders and data scientists:
 Decomposing a business problem into (solvable) subtasks
 Matching the subtasks with known tasks for which tools are available
 Solving the remaining non-matched subtasks (creativity)
 Putting the subtasks together to solve the overall problem

Typology of methods:
 Classification  Profiling  Co-occurrence Grouping
 Regression  Link prediction  Data reduction
 Similarity matching  Clustering  Causal modeling

The key question = is there a specific target variable? Target variable (DV)
 Yes – supervise learning (you are looking for something)
 No – unsupervised learning

Unsupervised learning
 Training data provides examples – no specific outcomes
 The machine tries to find specific patterns in the data
 Algorithm:
o Clusters
o Anomaly detection
o Association discovery
o Topic modeling
 Because the model has no outcome, can not be evaluated. Not predicting anything
 Independent variables  distance measure  find a pattern
Examples: Are these customer similar  customer profile
Is this transaction unusual  previous transactions
Are the product purchased together  examples of previous purchases

, Supervised learning
 Training data has one feature that is the outcome
 The goals is to build a model to predict the outcome (the machine learns to predict)
 The outcome data has a known value, model can be evaluated
o Split the data into a training and test set
o Model the training set/predict the test
o Compares the prediction to the known values
 Come with definit conclusions about the quality of the fit (good or bad)
 Algorithm
o Model/ensemble
o Logistic regression
o Time series
Examples: how much is this home worth  previous home sales
Will this customer default on a loan  previous loan that were paid or defaulted
How many customers will apply for a loan next month  previous months of
loan application

Data mining
Reuse in other circumstances. Important to make a
distinction to the mining part.
1. You use historical data
2. Use the results of data mining for predictions
3. Use the model in new data

Keep in mind that it is a process
Important: not linear process, you have to constant
collaboration of business understanding and data
understanding

Data mining focusses on the automatic search for knowledge patterns from data rather
than providing sort of technical support for manual search.  should help the company
to discover non obvious knowledge.

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