Data analytics is arguably one of the most vital features of a computing platform as it allows organizations to be predictive in its approach to business. Through analytics, businesses can transform raw data into actionable insights and help them determine current trends and answer critical questions about the business. An in-memory data grid pushes this to the next level through event-driven analytics, which is useful for instant notifications and alerts for vital business events like canceled payments and other transaction issues. Through event-driven analytics, a method or procedure is triggered whenever an event occurs or a set condition is met.
An in-memory data grid also provides context to streaming and transactional data by looking at an organization’s entire transaction history. Contextualizing data helps get real-time insights that lead to better—and quicker—business decisions. This is especially useful in assessing a business for potential risk that can affect regulatory compliance and customer behavior. Contextualized data also leads to real-time insights that help companies act accordingly, equipped with a better and deeper understanding of a risk’s impact and consequences. All this data is handled with high speed and low latency by an in-memory data grid, which can handle millions of events per second. Despite the lightning speed at which it processes data, an in-memory data grid effectively analyzes data to prevent undesired incidents like equipment breakdown, cyber attacks, customer churn, and more.
Data analytics is arguably one of the most vital features of a computing platform as it allows organizations to be predictive in its approach to business. Through analytics, businesses can transform raw data into actionable insights and help them determine current trends and answer critical questions about the business. An in-memory data grid pushes this to the next level through event-driven analytics, which is useful for instant notifications and alerts for vital business events like canceled payments and other transaction issues. Through event-driven analytics, a method or procedure is triggered whenever an event occurs or a set condition is met.
An in-memory data grid also provides context to streaming and transactional data by looking at an organization’s entire transaction history. Contextualizing data helps get real-time insights that lead to better—and quicker—business decisions. This is especially useful in assessing a business for potential risk that can affect regulatory compliance and customer behavior. Contextualized data also leads to real-time insights that help companies act accordingly, equipped with a better and deeper understanding of a risk’s impact and consequences. All this data is handled with high speed and low latency by an in-memory data grid, which can handle millions of events per second. Despite the lightning speed at which it processes data, an in-memory data grid effectively analyzes data to prevent undesired incidents like equipment breakdown, cyber attacks, customer churn, and more.