With unfathomable data at the tip of our hands, the analysis of this data to derive critical actionable insights becomes important. Predictive Analysis is like the technological incarnation of a gypsy teller, after all, it lets industries take a sneak peek into the future.
With massive amounts of data set and advanced machine learning algorithms, industries can derive useful insights and predict outcomes to chalk future endeavors.
Since the returns of analytics are outright and evident, the adoptation of predictive analysis was not a surprise.
We discuss how industries implement predictive analysis.
So let’s begin.
Healthcare and Predictive Analysis
Piling mountains of records have become a thing of past. In the healthcare industry, advanced analytics has eased out a gamut of applications for the doctors and the patients as well.
As a patient, being able to skim through medical records and receive real-time prescriptive analysis is a huge time saver. Also, this provides an in-depth understanding of the patterns that provide patient-centered results.
With heart rate monitors and fitness bands all around, the amount of data collected has scaled up. According to Forbes, “Researchers are beginning to compile this information into incredibly useful databases that could be game changers in understanding the intersection of lifestyle and disease.”
The predictive and statistical analysis are thus enabling to turn large chunks of data into actionable, life-saving insights in the healthcare industry. This same data set can be used for detection of fraud using data mining and machine learning techniques.
The Analytical Way of Manufacturing
Analytics is changing the manufacturing and maintenance methods. Analytics can effectively be used to detect any defective components in an assembly of hundreds of parts by using real-time data and decision-making methods. Even extremely minute details like temperature fluctuations and mechanical vibrations are collected to process useful insights on the entire manufacturing process.
Predictive analysis helps to prevent errors, quality issues, and defects. Industries use it to improve the entire customer service experience and reduce returns, and repairs, warranty claims, and product recall.
Once the product is manufactured, smart devices used for data tracking help for increasing revenue and enhancing the margin for manufacturers. Thus, data science, predictive analysis, and machine learning help to provide a customer experience like never before.
Finance and Analytics
Banks and financial services have eternally been data-driven. Big data, predictive analysis combined with machine learning algos has enabled to use advanced statistics rather than plain Excel sheets. With billions of data records, it does make sense.
From optimization to real-time forecasting, statistical models help to generate accurate and precise forecasts. Banks use predictive analytics software for various applications like customer retention and acquisition, feedback services, lifetime value among many others.
By combining analysis with machine learning models, a lot of industries are changing the way they operate. And the results are convincing enough for other industries to adapt as well.