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As the impact area of data gets larger, the importance of it slowly but steadily found its place in decision-makers. Naming the latest phenomenon of manufacture was the initial stage since all leaps before it goes undetected in their era although their impact was impeccable. That is, the industrial revolutions from steam engines to assembly lines and automation had unique impacts to both manufacturing and other sectors of economic activity. However, after the Industry 4.0, the power of them comprehended. The magic happened in the latest Industrial Revolution was how the data is comprehended and how to use it. With the fuel of 21 st Century, data, systematical approaches got their deserved attention; thus, history became experience and planning became prediction. Therefore, having such a powerful tool in hand locates your place in today’s sectoral hierarchy. Planning in accordance with the historical realizations materialize your position; that is, using tools stemming from data and their derivatives assists you to enhance your abilities in almost every aspect.

Types of Business Analytics:

As mentioned, the derivation of the analytics suggests with every challenge it faces the data and analyzing of the data changes form in order to cope with the differences born from paradigm shifts. The newest challenge is clearly so much data so little information due to former mindset of the analysists. However, the new mindset allows us to differ past and future importance of the data. Thus, we divide analytics into four sub categories:

  • Descriptive Analytics: where past results are analyzed
  • Diagnostic Analytics: where analyzes of activators are made
  • Predictive Analytics: where forecasting analyzes are made
  • Prescriptive Analytics: where future pathways are analyzed

Why Predictive Analytics Is Important

The importance of the predictive analytics comes from the historical experience where in manufacturing the optimization became a matter of concern. That is, when sectoral development was pursued, individuals started to look for more efficient, more improved, and fraud-free operations. Therefore, the main concern was lying beyond the optimization of the resources and operations. Thus, it is only until recently, predictive analytics term is unanimously used instead of analytics since with the latest paradigms, main concern if not the main concern is optimization.

  • Fraud Detection
  • Marketing Optimization
  • Operation Enhancement
  • Risk Reducing

Predictive Analytics’ Future

The concept of the new analytics (predictive analytics) is becoming widespread in application of the analyzing tools. Thus, from manufacturing to finance and from automotive industry to health the predictive tools are used enormously thanks to new data concept brought by Industry 4.0. Therefore, it will not be surprising to see new ways to use prediction as a tool. Here is a list of some examples where predictive analytics is applied nowadays 3 :

  • Automotive
  • Aerospace
  • Energy Production
  • Financial Services
  • Industrial Automation and Machinery
  • Medical Devices

All things considered, although the prediction as a tool is in its preliminary years, the impact of it in 21 st century cannot be overstressed. Hence, the mere subcategory of future analytics became the one of the game setters of the century of automation, machine learning and IoT, thanks to its comprehensiveness.

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