Predictive Analytics: Transforming Past Data Into Future Insights

You often come across advertisements of the products you were looking for or talking about on your phone, Netflix suggests you shows you might find interesting and amazon shows you products similar to your previous buying. How do these brands know exactly what we want? So, no more question marks. Answer to this question is one of the data analytics techniques called Predictive analytics. It helps companies to study their customers depending on their previous choices and buying behaviour and also predicts what customer might want next? And most of the time They are absolutely Right!!

What is predictive analysis?

According to Gartner, “Predictive Analytics helps to connect data to effective action by drawing reliable conclusions about current conditions and future events.”

We can explain predictive analytics as a category of data analytics that analyses current and historical data to draw insights about future or otherwise unknown events. The main role of predictive analytics in business is to extract information from available data and derive trends and patterns for future business. For example, predictive analytics can give providers a heads up when the clinic is about to get busy. Emergency units and urgent care departments can manage their optimum staffing levels as per fluctuations in patient flow to reduce waiting time and provide the highest patient satisfaction.

Steps to follow in Predictive analytics:

1.    Define Business Outcome: You should identify what you want to achieve as a result of predictive analytics. You should define the expected business outcome clearly. You should identify drivers which will impact the expected outcome. Since the outcome depends on drivers it is called a dependent variable and drivers are called independent variables.

2.    Capture the data required to train: This data can be obtained from different sources. As per data triangle methodology, there are 3 important things that need to be considered regarding data- data source, data form and data type. Traditional data sources include Excel spreadsheets, CSV files, relational databases, etc and Big data includes data from different sources like videos, audios, images, social media stats, etc. IBM scientists have proposed 4 V’s for big data- volume, variety, velocity and veracity. You can capture already stored data e.g. product sales last year. Also, you can capture data which is still generating e.g. no. of covid-19 cases registered daily. This data can be present in structured as well as non-structured form. You should make sure sufficient data is available for analysis.

3.    Determine the right technique to train your data: Different questions demand different tools for analysis. Selection of the right techniques also depends on the type and size of the available data. Data mining, text mining, statistical analytics, neural network and machine learning are some sophisticated techniques used for studying trends Useful predictions and insights are drawn by studying behaviour and pattern followed by the captured data.

4.    Validate results generated from trained data: After training your data, you will get some insights and predictions. You have to make sure that the insights drawn are aligned with your expected business outcome. Hence to ensure these results data scientists have to work along with business leaders or business analysts. Right data will flourish your business in the same way faulty predictions will destroy your business.

5.    Test predictions regularly: You have to keep track of data, study data patterns regularly, modify inputs whenever there is a chance of improvement. If the model fails then root cause behind failure should be identified and the model should be retrained.

Predictive analytics helps business leaders to make smart decisions and find an edge over competitors. Identifying right business outcomes and drivers which help to achieve those outcomes are the two critical tasks in the entire process. Predictive analytics has found infinite applications for every business domain. Some of those are mentioned below:

Applications Predictive Analytics in Healthcare:

1.    Delivering predictive care for at-risk patients:

One medical home network in the US used predictive analytics to identify individuals with a higher risk of developing severe complications from COVID-19. Rather than calling all 122,000 of their members to check in on their well-being, the home network took a more targeted, data-driven approach to focus their initial outreach on the 4.4% at-risk patients. By educating this group on when and where they should seek medical care, providers sought to proactively help at-risk patients while managing strain on healthcare organizations.

2.    Identifying equipment maintenance needs before they arise:

Certain components of medical equipment such as MRI scanners degrade over time through regular use. With the help of predictive analytics, you are able to predict when a component needs replacing, you can schedule maintenance at a time when the equipment is not in use minimizing unscheduled workflow disruptions that hinder both care providers and patients.

3.    Clinical trial design and Optimization:

The most prominent use of predictive analytics in pharmaceuticals is in the design and optimization of clinical trials. It speeds up the process of patient recruitment by segmenting patients as per their medical history. Companies can also make use of medical histories of their patients to detect the adverse effects of the drug being tested before they happen.

4.    Drug Discovery:

Using predictive analytics pharmaceutical companies can isolate specific molecules and test their effectiveness for treating disease and illnesses. In this model, the physical and chemical properties of the drugs are considered. For example, the drug’s number of H bond receptors or atoms in the molecule or compound that can form chemical bonds with hydrogen. Predictive analytics reduces the time period of the drug discovery process.

5.    Pharmaceutical Marketing:

Predictive analytics also has a place in the marketing sector and also has specific use cases for pharmaceutical marketing. Pharmaceutical companies can use predictive analytics solutions to analyse sales rates across different geographical locations. Insights include which retailers are seeing the highest sales of a product, which demographics are using the product the most, and a rise in demand for a certain product. Also, it helps to determine newer sales opportunities from existing customers.

References:

1.    Mejia, N. (2019, April 9). Business Intelligence and analytics. Retrieved from emerj.com: https://emerj.com/ai-sector-overviews/predictive-analytics-in-the-pharma-current-applications/

2.    News Center: Global. (2020, July 12). Retrieved from Philips Healthcare: https://www.philips.com/a-w/about/news/archive/features/20200604-predictive-analytics-in-healthcare-three-real-world-examples.html

3.    Singh, Y. (2018, Nov 1). Blogs. Retrieved from Edepristine: https://www.edupristine.com/blog/importance-of-predictive-analytics

4.    Vesset, D. (2018, May 14). Business Analytics Blogs. Retrieved from IBM.com: https://www.ibm.com/blogs/business-analytics/predictive-analytics-101-will-happen-next/