With the growing amount of data available with the organization, Data analytics becomes an integral part of almost every business management. Analytics in itself has wide applicability to add more value to the business.
Data Analytics has various types such as Prescriptive analytics, Descriptive analytics applied analytics and Predictive analytics etc.in recent years predictive analytics has evolved in the business intelligence market.
Predictive analytics is the process of making predictions using data model based on historical trends and pattern. The basic aim is to know about what will happen in future based on the information what has happened.
Predictive Analytics include big data, a set of statistical techniques, predictive analytical model, data mining, algorithm and machine earning.
Using predictive analytics in business management helps in perform the function of What If Analysis and its outcome on the overall return to the business. What if analysis helps to analyze various scenario with different variables and visualize the outcome of it. It can be applied to a given data set to forecast future results such as estimated growth in sales revenue, change in the level of profitability by changing elements of expenses and incomes.
In today's scenario of big data, it is very important to embed predictive analysis as an integral part of business processing and decision making. Although traditional business analytics has its relevance in making informed decisions.
Embedding Predictive analytics is an approach to integrate intelligence into existing system such as Enterprise Resource Planning, Marketing Automation etc. to provide with valuable insights at lower cost. This enhance the decision-making ability of an organization.
3 Easy Steps to integrate Predictive analytics with existing business analytics applications:
The first and foremost thing is to identify which variable and factor has great impact on outcome and thus, crucial for decision making. Once identified, integrate those factors with what if analysis to see the impact of different scenario.
- Develop an analytical model:
An analytical model gives an idea about trends and patterns based on past data available. This model provides a direction to predict future outcome. It also helps to identify the areas that need more attention or improvement.
- Continuous monitor validity of model:
The analyst need to check the validity of analytical model with the changing factors and business scenario to ensure accuracy in predictions. It is required to validate which factors are working well and which need to be changed. If the model is not valid as per current decisions need and variables, a new model need to develop.
Predictive analytics requires good IT skills, expertise in different statistics and analytical tools, logical ability and cognitive ability to develop model and make predictions. To work with predictive analytics, data analyst must have the ability to establish cause and effect relationship among different variables and capability to visualize the impact of variables on the outcome.
Predictive analytics is gaining importance day by day as it helps to identify opportunities and threats and gives early warning signals. It is a proactive approach to manage the business in this dynamic and complex environment.