Figure 3: A sample solution of the Churn Problem on Knime

The following are examples of other areas where data analysis methods can be used. It is important to remember that the algorithms to be used in each area will vary. However, the volume, variety and accuracy of the data will be equally important for a good result in all these areas.

  • Cross Selling
  • Market Basket Analysis
  • Customer Relationship and Satisfaction Management
  • Competition Analysis
  • Fraud Protection
  • Credit and Insurance Risk Assessment

The use of technology in all aspects of life also brings competition against time. The method of collecting the data, its cleaning up for a long time and preparing detailed analysis reports began to wear out. There are some problems, such as credit card fraud, which make it necessary to analyze the data instantaneously on the flowing data rather than on the stored data. This leads to the concept of Stream Analytics. In classical methods; data gain value if it is gathered, accumulated and waited. In Stream Analytics, real-time non analyzable data is not valuable.

In recent years, there are departments of Data Analytics in universities and certificate programs by educational institutions for enthusiasts. However, you can access hundreds of educational documents free of charge with a short search on the internet. There are dozens of paid and free platforms and software languages available for data analysis work. For more successful results in this area, it will be necessary to deeper investigation of algorithms such as artificial neural networks, decision trees, support vector machines and Bayesian networks, ability to work on platforms like Knime, SAS, RapidMiner, Pentaho, Tableau and Power BI and if you want to deepen in the software field you will have to spend hours working in languages such as Python, R or Scala.


[1] Digital in 2018 by “Hootsuite” and “We Are Social”,

[2] Internet of things : Number of connected devices worldwide from 2012 to 2020, Statista,