Predictive Analytics: The Holy Grail in Business Intelligence

In today's world, businesses have a wealth of data at their fingertips. However, data will be of no use to a business if it is not utilized to gain insights and make informed decisions to enhance business operations. Business intelligence, or BI, helps businesses achieve this goal. BI is a technology-driven way of analyzing information and delivering actionable insights that can help managers, executives and end users gain detailed insights that aid them in making decisions. It helps people to assist in making decisions on what they can do for getting insights.

While traditional BI tools primarily monitor historical data and current data, predictive analytics utilizes data, statistical algorithms, data mining methodologies, analytical techniques and machine learning algorithms to determine the likely outcomes based on historical data to provide insights into the future. In addition to being able to determine what has happened in the past and why it happened, predictive analytics also helps you understand what could happen in the future. By identifying opportunities, they allow businesses to be proactive and agile.

For businesses, predictive analysis is crucial. Digital transformations and increased competition have made companies more competitive than ever before. Using predictive analysis is like having a strategic vision of the future, mapping out the opportunities and threats. Therefore, companies should look for predictive models that:

• identify potential opportunities
• plan and optimize marketing strategies
• map consumer behavior
• enhance efficiency and improve operations
• identify and reduce risks

Any industry can use predictive analytics to forecast sales, detect risks, and improve sales operations. Predictive analytics can also be used to detect fraud, evaluate credit risk or find new investment opportunities in a financial institution. Using predictive analytics, manufacturers can identify factors that result in quality reduction, production failures, and distribution risks.

With predictive analytics, sales forecasting can create real value for businesses. Many other business decisions are influenced by accurate sales forecasts. However, sales forecasting is still a time-consuming activity for sales professionals who often rely on Excel spreadsheets and other tools that do not provide sufficient analytics and insights to make accurate sales forecasts. As a result of advanced predictive analytics, sales professionals can automate rolling forecasts and have more transparency and smarter decision support.

Predictive Analytics: How Does It Work?

Using an ensemble of machine learning algorithms, AI-based forecasting optimizes forecasts. Depending on which business metric you're forecasting, the system selects a model that's uniquely suitable. The process consists of a series of steps:

1. Determine the purpose of the forecast.
2. Identify the items for the forecast.
3. Choose a forecast model type.
4. Obtain and analyze the data needed for the model.
5. Make the forecast.
6. Analyze, verify, and implement the results.

Regardless of the business model, forecasting is extremely important for businesses as it creates some insurance for future business outcomes. In addition to detecting and mitigating potential issues in advance, it helps organizations make informed decisions and set budgets and business goals. AI helps businesses oversee all these aspects with increased accuracy in the forecasting process.