Analytics (or predictive analytics) uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to project what will happen next, or to suggest actions to take for optimal outcomes. 

    Analytics as we know it has deep roots in data science. Combined with the ability to view archived data in a more 3D-type analysis, analytics can provide deeper insight beyond basic Boolean search. Based on prior history and outcomes, organizations can gain deeper insight into trends and patterns regarding employees, customers, and competitors. You can also mitigate risk, and predict success and security. This is a result of capturing and analyzing current data from multiple channels, including emails, files, instant messages, CRM applications, relational databases, collaboration tools, and social media. With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways

How artificial intelligence differs?

AI has existed for a long time. But machine learning is actually being developed. Machine learning, an AI technique, is a continuation of the concepts around predictive analytics, with one key difference: The AI system can make assumptions, test, and learn autonomously. AI is a combination of technologies, and machine learning is one of the most prominent techniques utilized in information governance to yield deeper insights about data.