Big Data Analytics

Over the years, businesses have tried to collect information from their competitors and the market trends to improve their decisions. The gathered information was always private and only the researchers and company managers knew the details about it. Today, As the computer age has evolved to a degree that all major businesses use the world wide web, information has been much easier to access. The information has been collected by normal people, governments, or different companies, therefore there is extremely large data available for use, expanding exponentially from time to time. It is data of such large scale and complexity that it can not be easily stored or handled by any conventional data processing tools, it is really difficult to process using traditional methods. Big data is also data, but with an immense scale, which needs new and alternative programs for better analysis.

There are 3 types of Big Data: structured, unstructured, and semi-structured.

Any information that can be stored, consumed and processed is called structured data. Such files generally have structured columns and rows, so they are easy to filter and sort. Most often such data is found in Excel files. Structured data depends on a certain structural model that is already known by the particular program where the data is stored.

Unstructured data is information that is not predefined, organized or does not have a pre-defined data model. In normal words, any data which is structured in an unknown form is considered “unstructured data”. Most of the time unstructured data consists of photos, videos, and different types of texts, which makes the data not common and complex. Adding to that, mostly unstructured data is much larger than structured data. So those kinds of inconsistencies are much more difficult to analyze. At the beginning of the “big data age”, these data were mostly useless, but in the last few years, there are many innovations that tend to bring the value out of those pieces of information by making complex structural changes.

The third- “Semi-structured” data is right in the middle. Semi-structured data does not apply to the traditional structures, nonetheless, it has some distinguishing tags or tips which make it easier to separate different types of info. Examples of It are JSON and XML. Semi-structured data existence depends on the complexity of the unstructured data. It is a lot easier to sort this kind of data using modern apps.

In addition to business sector, big data began to be used in the medical industry. Several medical and healthcare programs have recently become very popular. Such programs make it easy to count calories, get water, walk and mileage, and even check your heart rate. By collecting and further processing such unstructured data, companies can have a huge impact on the medical industry if used properly. Apps can suggest how much to eat, what to eat, how much water to drink daily. These health tips will be based on your sex, age, physical activity, weight, height,previous experience, etc.

The use of data analysis in sports has become quite trendy in recent years. The exponential growth of databases has allowed people to gain more insight. Constant monitoring of players allows us to collect information. Experts can already gather information from previous matches, specific player trends, injuries and so on.

Another interesting industry is human resource management. It is already common practice in many companies to save the received CVs and share them with other partner companies. Several multi-company associations have already created huge databases, where the HR manager can easily find the data of the desired candidate on his own.

In today’s digital age, big data has emerged as a game-changer for businesses across various industries. Its immense scale and complexity present both challenges and opportunities. By understanding what big data is and how to adapt it to your business, you can unlock its potential to drive growth, gain a competitive advantage, and make informed decisions.