Data is the raw facts, while information is what happens after the data has been processed to be made meaningful. According to multiple sources, information isn't just about how accurate the data should be represented, rather it is made to be relevant, done in a certain time, and how efficient it can be used.

Since data is only useful if it can help with how things are decided, and what actions can be done. This is very important in today's digital age, since we are generating billions of data per day. Chen et al., (2012) mentioned how the rise of business and analytics shows how these different systems can change all these raw data into insights that can help with a plan of action, and innovate future ideas. So the main difference between data and information is dependent on how it is interpreted and applied.

Information systems involves the combination of technology, us humans, and the different processes or systems that help handle information efficiently to support an establishment's goals. It is not entirely machine dependent or technical tools to be left alone doing automatically doing its assigned tasks, but are frameworks formed by human needs, workflows and business strategies.

Organizations in this age often rely on these different systems to be able to strengthen communication and make rational decisions By allowing analytical power to be used with human power, these systems ensure that the different kinds of organizations are able to respond quickly to challenges, enhance performance, and sustain growth.

As Laudon and Laudan (2020) pointed out, information systems are an important factor in handling digital firms, especially since business tend to adapt to the rapid growth of technology, giving way for online businesses to grow. Thus, information systems play a critical role in allowing day to day operations while also helping adapt business into the digital world.

There are different types of support system that represent specialized information systems that are tasked to specifically meet an organization's goals. For example, Decision Support Systems (DDS) help managers analyze data and see pre-constructed decisions off of those data, which allows for human judgement to be mixed together with computer algorithms for better informed decisions.

While other types of systems like Knowledge Management Systems (KMS), focus on aligning IT resources with long term objectives said Pearlsons and Saunders (2019). All these different systems are very essential because they go beyond just handling data, rather they offer tools for problem solving, decision making and planning.