(204c) Predictive Analytics and Big Data in Chemical Industry | AIChE

(204c) Predictive Analytics and Big Data in Chemical Industry

Data is more abundant than ever in just about every industry imaginable.  How data is used across the wide range of industries depends on the organization and its objectives. Predictive Analytics, written by Eric Siegel explains, “Data is being used to predict who will click, buy, lie, or die.” 

Predictive analytics is the learning, understanding and use of information (data) to predict future outcomes (Siegel, 2013).  Data is being used to predict your next move.  As an example, banks and credit card companies are tracking your behavior, and insurance companies are defining their rates based on the data they have obtained on people like you.  Internet companies such as Google, Netflix, and Amazon are using predictive analytics to market their products to you based on your recent search and purchase history.  Because of their use, these analytical organizations have gained a competitive advantage through the use of data, and have demonstrated success in their industry.  How can we apply predictive analytics in the chemical industry? 

As the amount of data continues to grow, The Dow Chemical Company is recognizing the information it contains and the potential to gain significant value for the company.  From a business perspective we can predict which products to invest or divest in; Know how much product to make in a given month or week and predict the quality of our product before it’s manufactured.  Dow is using data at all levels of the company and is continuing to invest in this information-rich data space.  As Dow recognized the potential hidden in its data, the company is looking for new ways to minimize risk and maximize profit in every investment.

Siegel, E. (2013). Predictive analytics: The power to predict who will click, buy, lie, or die. Hoboken, N.J.: Wiley.