What is Data??
Whatever business you work in or what interests you, you've almost likely come across how "data" is transforming our world. It may be used in a study to estimate the number of deaths due to the coronavirus, increase a company's sales, or improve the efficiency of hotels.
In general, data is just another way of saying "information." However, data refers to information that is machine-readable rather than human-readable in computers and business (especially if it's about Big Data).
Human-readable data (also known as unstructured data) is data that can only be interpreted and studied by people, such as an image or the meaning of a block of text. That information is human-readable if it requires a person to interpret it. Information that can be processed by computer programmes is referred to as machine-readable (or structured data). A programme is a set of data manipulation instructions. And we obtain software when we take data and apply a set of programmes to it. Data must have some kind of standard structure in order for a programme to perform instructions on it.
Numerous data sets are available for reference, such as personal data provided on social media platforms, transactional data through electronic commerce sites, sensor data accessed from internet-of-things technologies, and many more.
But, What is Big Data?
Technically, all of the types of data above contribute to Big Data. There’s no official size that makes data "big". The difference between Big Data and the "normal data" we were previously examining is that the instruments we use to collect, store, and analyse it have had to evolve to handle the increase in quantity and complexity. We no longer need to rely on sampling with the latest instruments on the market. Instead, we can process huge datasets to get a much more thorough picture of the world around us.
Data Analytics
The field of data analytics is vast. Data analytics can be classified into four categories: descriptive, diagnostic, predictive, and prescriptive.
- Descriptive Analytics aids in the investigation of what occurred. To describe outcomes to stakeholders, these strategies synthesise big datasets. This procedure necessitates the gathering of relevant data, data processing, data analysis, and data visualisation. This procedure provides crucial information about previous performance.
- Diagnostic Analytics answers questions regarding what happened and why it happened. These strategies are used in conjunction with more basic descriptive analytics. They take the results of descriptive analytics and delve further to discover the root of the problem. The performance indicators are looked at further to see why they have improved or deteriorated. The procedure consists of identifying anomalies in data, collecting data related to these anomalies, and finally using statistical techniques to find trend and relationships.
- Predictive Analytics aids in the answering of queries regarding what will happen in the future. These methods make use of historical data to uncover patterns and decide if they are likely to repeat again. Predictive analytical tools use a number of statistical and machine learning approaches, such as neural networks, decision trees, and regression, to provide significant insight into what might happen in the future.
- Prescriptive Analytics assists in determining what should be done. Data-driven decisions can be made by utilising predictive analytics insights. In the face of uncertainty, this enables firms to make educated judgments. Machine learning strategies are used in predictive analytics techniques to detect trends in large datasets. The likelihood of various outcomes can be determined by evaluating past decisions and events.
These forms of data analytics give firms the information they need to make informed decisions. They provide a well-rounded understanding of a company's demands and potential when used together. A data analyst's job includes dealing with data throughout the data analysis process. This entails a variety of data manipulation techniques. Data mining, data management, statistical analysis, and data presentation are the main steps in the data analytics process. The importance and balance of these procedures are determined by the data used and the analysis' purpose.
Mr. Himanshu Sharma
Assistant Professor
Jaipuria Institute of Management, Ghaziabad
