what is the difference between data and information?

The dependency on data is not a recent or modern phenomenon. Rather the wise have always leaned towards data-dependent predictions. Before the age of computers, the process of data analytics was tedious. As the data was mostly in the form of writing or printing on paper. The storage and analysis capabilities were also very limited due to the sheer amount of human labor involved. Thus the use of data was restricted to very interested individuals and organizations. Many of those are leading and spearheading the industry.

The dependency on data increased after the emergence of the COVID19 pandemic. Due to the sudden and devastating fall, institutions of all stature decided to look forward to a more data-dependent and efficient future. And thanks to contemporary data and internet dependency on an individual level, the data required was already available in plenty. The goal of any data analysis operation is the generation of information and based on that making predictions. The predictions are then utilized for prescribing a safer path towards a better time. This article will try to go through this very interesting process of data analytics.

Modern-day commerce, public and private sectors are working hard toward a more automated and accurate future. Data is granting the necessary abilities to predict and figure out the safest path of approach. Before we understand the journey from raw data to actionable insights, we have to ask, what is the difference between data and information?

The difference between data and information

In order to understand how data is converted into necessary information for making prescriptions, we must understand the differences between data and information. Data is a set of facts. But information is something that is generated from that fact. Information is not absolute. Based on the context and analysis approach information derived from the same data set can vary drastically. In addition, a fact or a set of data must have multiple dimensionalities and characters and the information derived from it will depend on the circumstances and the need of certain characters.

How is data transformed?

Collection of data

Thanks to how much we depend on the internet and connected devices, the data we generate are plentiful. And most of it can be accessed without breaching any ethical limitations. The data we generate consists of all kinds of data from climate data to purchase data. In addition, any requirement for more data can be quenched by purchasing access to the same.

Arranging the data

The obtained from public and commercial sources are mostly unstructured. A data analyst must figure out the patterns and relations between data groups and form arrangements. Like grouping, classifying, and clustering. And after the data is arranged automation tools such as machine learning and artificial intelligence tools are deployed for the handling of huge amounts of data.

Analysis and designing an analysis model

The analysis of data involves noticing patterns and similarities between seemingly unrelated data. The analysis approach of data depends on the circumstances and origin of the data. Most analytics operations concentrate on a single or a few aspects of a data set. And in the case of huge data sets, machine learning tools are deployed on the already structured data. And after a few rounds of analysis are performed, the model of analysis is standardized for a hassle-free future experience with similar tasks.

Predictions and prescriptions

In order to make predictions and prescriptions, a data analyst must consider all the internal and external aspects. Both the determining factors and the internal conditions of an institute are considered. Proposing a prescription involves explaining the analysis to relevant people who might not understand the technicality of the same. Thus the presentations are made as graphic and easy as possible so that everyone can understand them. And depending on the success of communications prescriptions are made and plans are communicated. More the data used in deriving conclusions, the more the chance of them being accurate to its core.