Analytics Vs Data Science

The term “information” refers to any of a wide range of things. Information can be general (such as weather and maps) or specific (such as how to play golf). It can be abstract (such as the contents of an encyclopaedia) or detailed (such as the interior design in a magazine). Information is used to make decisions, and it is vitally important to the modern economy. Decision making is central to business life.


Business intelligence is the analysis of business information and data, and therefore the management of it. Information, in a broad sense, is simply processed, structured and organised information. It gives context to previously processed data and allows decision making to be made. For instance, a single consumer’s sale in a particular restaurant is broad pathological information-it is an actualization of what they bought, where they bought it from, when they bought it and how much they paid for it. But it is up to the business to extract the meaning of this generalised, unvarying information and make it useful.

This is different from data science, which is about using the available data to make informed decisions based on the collective knowledge. Data science is very different from business analytics tools, which are about providing the framework to support decision making based on broad data, especially unvarying and universal data sets. They are therefore more directly related to business intelligence than to data science. Analytics has been around for a long time but has only recently started to be considered part of an overall business intelligence strategy. Nowadays, almost every large company has some form of analytics or information gathering function, although this has declined slightly due to the arrival of more powerful software.

So, how is information different from data? To answer that question, we need to be clear about the difference between knowing that something exists. The former relies on sensory experiences (sight, touch, smell, hearing, etc.) while the latter relies on accumulated or abstract information. Thus information exists when we can describe it in a way that our brains can understand. This is different from data analytics, which uses available information to provide enough information for a decision to be made based on the collective mass of evidence.

Business intelligence or data analysis requires the separation between these two concepts, but not necessarily between intelligence and data compression. Rather it is a question of how different their values are, which can vary depending on the circumstances. In business intelligence, the meaning of data is critical. It is the foundation of the analysis and provides the knowledge base upon which the rest of the process of decision making can be built. On the other hand, data compression is a function of budget and capability.

Today’s business environment is a complex one, and in order to get ahead it is important for managers to adopt the right techniques – ones that are often alien to those of their analysts. A good example is the use of data compression and the associated information management system. Although many analysts assume that information exists in forms such as text, images, audio, video, etc., what they fail to recognize is that people respond better to verbal cues than images do, and they prefer spoken language over written text, even when the latter is superior. The correct use of data analytics tools such as Excel and pivot tables would allow managers to leverage the collective unconscious mind, whose insights and intuitive wisdom have made businesses thrive for centuries, without needing the bloated and ineffective databases of data science.