Are companies transposing their roadmap through the lens of business analytics?

The word "analytics" is no more hidden to people belonging to the business and we are witnessing its impact in day-to-day business operations as well. The path shown by analytics performed makes the decision-making easier and backed by data creates a strong sense of belief in the process. The application of analytics is touching almost all types of industries be it Oil/Gas, Retail, Financial Services, Insurance, Airlines, Software, Industrial Equipment, Manufacturing and Many more.

Business analytics is a strategy for identifying models that drive decisions and actions based on vast volumes of data. It is applied in various verticals like - Operations, Sales & Marketing, Finance, Human Resources, Strategic planning, etc. Decision modeling is an integrated mathematical method to optimize the decision-making process.

Identification of industry problems, analysis, solutions, and implementation are a few of the major steps in the process of decision making. Various types of business analytics performed are as below:

• Descriptive Analysis: It tells about happened events with mapping, reporting, and alerting
• Diagnostic Analysis: It gives reason for events happenings with data and statistical study
• Predictive Analysis: It forecast the unnecessary events by machine learning simulation
• Prescriptive Analysis: It shows the path for implementing the plan to avoid events

There are a couple of things to keep in mind which affect the results like- model misspecification, extrapolation, overfitting, and irrelevant variable selections. The learnings of machines happen in two ways supervised and unsupervised. Both contribute to the functioning and decision-making process. Some of the important milestones in machine learning usage are:

• Prediction: It forecast the results by application of linear regression and regression tree
• Classification: It segregates the data based on logistics regression, K-nearest neighbors, and classification tree
• Clustering: It makes a group of data by using K-means clustering

Sensitivity analysis and shadowing are very important ways to run business simulations keeping the constraints intact. It helps to correct the problem-solving approach in order to find feasible and optimal solutions. The insights provided by the analysis helps companies to take capital expenditure, budgeting, facilities allocation, and batch size production or service decisions.

Using data analytics companies can make important decisions about their day-to-day operations which will result in significant increases in profits. Reduced downtime, increased productivity, improved capacity utilization, more accurate forecasting, increased flexibility, streamlined production processes, and improved efficiency are some of the major benefits of performing business analytics.

The framework developed by using analytics as a tool helps companies to understand the impact of the unseen trends on business performance. It helps to grow business, improve customer satisfaction, lower service or manufacturing cost, and creates end-to-end visibility.