The amazing technology through which you can predict the future outcomes and trends by extracting information and creating patterns from the existing historical data sets is known as Predictive Analytics.
This blog post has been written as part of the assignment for Predictive Analytics studied at Victoria University. Getting the practical hands-on experience of SAP products in this university has always been the best part of learning experience. We are very grateful to our lecturer Dr Shah Miah who helped in understanding the value and scope of Predictive Analytics.
The competition is rising in the market and their are loads of data that is sitting in the database also known as Big Data. This big data comprises of the historical data sets and can be used to produce some valuable information about the company and its operations/processes. The extraction of information is done with the help of Data Mining tools and a Predictive Model is built. Predictive model act as a training set and when the new testing for prediction needs to be done with the new real-time data, then the data is run through the model and it predicts the outcomes. These outcomes about the future helps us make better informed decisions.
A 2014 report found that the top five things predictive analytics are used for is to:
- Identify trends.
- Understand customers.
- Improve business performance.
- Drive strategic decision making.
- Predict behaviour.
Predictive analytics are used in almost all fields of Fraud Detection and Security, Marketing, Operations and Risk. The most common areas of application are Credit Card, Banking and Financial Services, Governments and the Public Sector, Health Care Providers, Manufacturers, Media and Entertainment, Oil, Gas and Utility Companies, Retailers, Sports Franchises, Health Insurers and Insurance Companies.
We decided to do our predictive analytics study with the help of a healthcare example. One of the nemesis for Australia economy is considered to be obesity according to most journals. The prevalence percentage of obesity across Australia has been increasing in the recent years and it is expected to continue this trend until a proper solution is proposed. We had collected the data from Australian bureau of statistics and some health journals which gave us the percent of obesity population across Australia. The screen shot below shows the data from year 2000 to 2015 with a five year gap analysis.
We consolidated the given data to a flat file and used the data history to calculate our predictions on prevalence of obesity in the years 2020 and 2025. We had used the prediction tools to calculate mean and variance here to find the varying trend and increase of obesity in years with previous data. This data was than applied to the already present data to find the gap and fill out the predicted percentage of increase that will happen in future. The below screen shot of excel file with consolidated data shows us the final data or the percentage of increase that would probably occur if the problem is not taken care off.
The data here is the method of data representation which uses the data to be represented in desired format for study purposes. As for being a data analyst, we have to consider the importance of data collection from the past in order to study about the patterns that develop in particular fields.
In our case study here, the value of predictive analytics is used as a research purpose analytical case. With the predicted data found above, the government can take actions against various factors that cause this physical sickness among the youngsters of Australia. The government can concentrate on particular areas or suburbs to create awareness centres to curb this economical nemesis. Same type of study can be applied to various other fields for their origin of business or study. The sales industry can concentrate on individualized portfolio of a customer in order to find out the pattern buying behaviour of that person to sell him or her desirable products from their data history. Another case can be the human resource industry planned operations to meet the company requirements of work force for the next ten years in advance and keeping the idea of immediate prospects out of their company.
At the end, we would like to conclude that this formula of prediction can also be used in many other cases for predicting the future outcomes of study or business. It does not mean the values are real and perfect, but rather approximated for future. Its enough said that predictive analytics is the future of business intelligence.