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What is the difference between Business Intelligence and Predictive Analytics?
Let us begin by learning about business intelligence.
Business Intelligence
Business intelligence is an application-driven phase allowing organizations to analyze raw information from various sources, extracting vision that lead to higher effective business results. It is a set of tools and methods that analyze and transform raw information into actionable and coherent data for use in business analysis to support decision-making.
Each business has strong transaction-oriented systems that save all information gathered from daily operations into repositories.
It can stay competitive, businesses should rediscover and uses the data they influence, and this is where BI appears into play. Business intelligence can change vision from a pool of accessible data to pass definite, actionable, and basically real-time inputs for decision making.
Advantages of Business Intelligence
The advantages of Business Intelligence are as follows −
BI tools provides executives, managers, and workers to uncover vision that apply to their act and field of authority and use them to make decisions based on fact, not speculation.
BI dashboards are created simply to oversee key performance indicators (KPIs), track progress against targets, and set alerts to understand where and when to target improvement action.
With BI, users can discover potential business issues earlier that generate financial harm including producing or distribution bottlenecks, upward trends in user churn, increasing labor costs, etc.
BI software provides intuitive interfaces, drag-and-drop reports, and role-based dashboards that group representatives can use themselves without the requirement for coding or several technical skills.
Predictive Analysis
It is the approach of using data to a model prediction about the likelihood of potential future results in your business. Predictive analytics uses historical and current data connected with methods including advanced statistics and machine learning to model unexplored future development.
It is frequently represented as learning from the previous composite knowledge of an organization to create better settlement in the future using data science and machine learning.
Predictive analytics enables organizations to forecast user behavior and business results, using historical and real-time information to model the future.
Its applications use variables that can be calculated and evaluated to forecast the likely actions of individuals, appliances, or several entities. It can be used for multiple use cases.
For instance, an insurance company is likely to take into account potential driving safety variables, including age, gender, area, type of vehicle, and driving data, when pricing and declaring auto insurance policies.
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