Abstract
What would you like to get out of Data Analytics? Data processing, data mining, tools, data structuring, data insights and data storage are typical first responses. So, we defnitely want to analyse, manipulate, visualise and learn about tools to help us in this endeavour. We do not want to analyse the data for the sake of analysing it; the insights need to be actionable for businesses, organisations or governments. How do we achieve this? The process of discovering knowledge in databases and CRISP-DM helps us with this. Of course, we need to know about databases. There are tools such as Power BI which allow us to transform, analyse and visualise data. So we are "analysing" the data - analysing ranges from formulating a data challenge in words to writing a simple structured query and up to applying mathematical methods to extract knowledge. Of course, the "fun" part is refected in state-of-the-art methods implemented in data mining tools. But in Data Analytics, your mind is set to ensure your findings are actionable and relevant to the business. For instance, can we: find trading opportunities, figure out the most important products, identify relevant quality aspects and many more so that
the management team can devise actions that benefit the business? This motivates the following definition:
Data Analytics
is the discipline of extracting actionable insights by structuring, processing, analysing and visualising data using methods and software tools.
Where does Data Analytics "sit" in the area of Business Analytics? Often, Data Analytics is mentioned in conjunction with Business Analytics. Data Analytics can be seen as part of Business Analytics. Business Analytics also includes Operational Analytics. It has become fashionable to divide analytics into Descriptive, Predictive, and Prescriptive Analytics. Sometimes these terms are further refined by adding Diagnostic and Cognitive Analytics. What is what?