Data Analytics


The term data analytics (often synonymously labeled as big data analytics or data mining) describes a scientific method that extracts and examines data from various data sources. Thereby statistical methods are used to derive hidden patterns, unknown correlations, or other information from the data. Originally used in research to verify hypotheses and models based on data, it is now widely used in companies to make decisions on a more robust basis.

Meaning of Data Analytics

Since the amount of data that is generated around the world every day increases exponentially, dealing with the amount of data and deriving meaningful conclusions from it is becoming increasingly important. According to a study by International Data Corporation (IDC), sales of big data and business analytics will increase from US $ 130 billion in 2016 to more than $ 200 billion in 2020. McKinsey estimates that big data analytics can save the American healthcare system around US $ 300 billion.

Types and Ranges of Application

A distinction is often made between four types of Data Analytics:

  • Descriptive (What's happening in my business?)
  • Diagnostic (Why is it happending?)
  • Predictive (What's likely to happen?)
  • Prescriptive (What do I need to do?)

In the financial sector, big data analytics can help reduce the risk of default, prevent fraud, or even combat money laundering. In retail, data analytics can help to compete with online shops through more targeted offers to customers. There are also numerous advantages in companies: in finance and accounting, deviations can be better interpreted, and risks identified earlier. This applies to financial risks, but also the monitoring and compliance with the increasingly important agreement rules.

In the departments of marketing and sales, target groups can be identified better, or the benefits of marketing campaigns and sales activities can be determined, thus optimizing the cost-benefit ratio. By using big data analytics, customer relationship management can not only identify potential sales opportunities, but it can also investigate changes in customer behavior and thus avoid customer churn. The supply chain benefits from improved forecasts and the early detection of deviations in terms of requirements, procurement, production, and distribution.

For many companies, data analytics will be the key to survival in an increasingly competitive environment.

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