Data Analysis

Posted by Koen Van den Bossche 15/09/2014 0 Comment(s) Data Analysis,

1.1. What is this fundamental about.

 

Data analysis, usually called Big data and Business intelligence, is of great interest. Rightly so, because the possibilities and the strategic importance of specific information for your company have increased considerably.

How can your company make use of these new possibilities. We explain in brief what the advantages of data analysis are and we explain how you analyze data. In the core is data analysis statistics, a discipline that is usually experienced as difficult.
 
Data analysis is certainly not a hype and will not blow over again. In the field of medicine and natural sciences, the results are impressive. Companies with large IT departments analyze their data intensively. So it is a proven method for better decision making. But for SMEs, the use is lagging behind. This will change quickly due to the increased possibilities. So also for the SME, getting knowledge from data with data analysis is a way to stay ahead of the tough competition..
 
Most data in the SME are raw data, unstructured and mostly incomplete and polluted. So there is already a lot of work to store your data, your future gold in the right way and ready to exploit.
This requires the active use of statistical techniques such as regression analysis, the recording of data to analyze them, the detection of possible connections, the making of predictions and the testing of these predictions against reality.

 

1.2. What are the benefits of data analysis?

 
Information, processed data, will in time be considered as important as the previously known assets such as labor and capital. Because information is necessary for coordination within a company and for making informed decisions (at all levels).
 
On the one hand, information is a tax, because collecting and structuring requires a lot of effort and resources. On the other hand, information is a source of competitive advantage and is useful for improving business processes and thus the results.
 
Data analysis is not about tracking individual facts or calculating averages, but about discovering links in data.
 
Data about things that really happened. With this insight you can make predictions based on historical data and trends. With this you can choose the best option. These choices are then examined in turn with newly collected data.
 
Guessing is missing and is therefore dependent on subjective intuition and experiences. Knowledge resulting from data analysis is not the end goal, but is a means to make money, so to reduce costs, increase sales, streamline processes and increase product and service quality.

 

1.3. Trends that stimulate data analysis

 
The strong interest in data analysis is easily explained. A number of trends promote use.

Information on paper is becoming more and more digital.

Almost all data and files are stored digitally within most organizations. The entire administration is processed by computer and mass data is stored in one or other database system.
A lot of communication, both internally and externally, is conducted digitally, but not always stored in the right way.
The Internet makes data accessible anytime and anywhere
 
Computers can exchange data with the Internet. That goes fast and there is hardly any limit to the scale. This means that an analyst can use data from any location without a threshold (if he can).
Powerful tools
Data analysis requires tools such as storage media, processors and software. These tools are becoming more powerful and at the same time becoming cheaper. Some software, such as the programming language R and OpenOffice Calc, is even free because it has been developed by universities and volunteers.
Open data
More and more governments are making their data available to the public for free. Examples are the American government (http://www.data.gov/)
Breakthrough economic psychology
In 2002, insights from economic psychology broke through when Daniel Kahnemann, a psychologist, received the Nobel Prize for Economics. He showed that decision-makers are often less rational than they themselves think and wish. In many situations that 'automatic thinking' is not bad, but with important decisions tools such as a second opinion or data analysis are desirable.
See also our article about the hidden persuaders.