Big Data: Recently, data has been gaining importance in the most different markets and having a data-driven culture is already the focus of many businesses. But what does this mean? The answer lies in the use of Big Data.
This concept, which has been present for a few years in the technology area, is now also a much-debated term in other segments. Agribusiness, industries, education and even aspects of communication, such as marketing, have much to benefit from solutions based on this resource. This is because the analysis of large volumes of data, occurring more and more quickly, is used to extract valuable insights.
In this sense, productivity prediction, fraud detection and even the creation of personalized experiences are some results of this trend that makes management smarter. However, to better use this, it is necessary to know how it works and to know the technologies that, associated with it, prepare the business for present and future challenges.
After All, What Is Big Data?
Today, everything revolves around data. So much so that they are considered the ‘new oil’ of the 21st century. To exemplify, the connectivity we depend on is precisely based on this information traffic that grows every minute. Even the way of living, working and consuming content has changed recently. There are thousands of hours of streaming and video conferences and several devices connected via the Internet of Things (IoT).
However, when we put this situation inside a factory, we have intelligent devices sending data to the cloud and other connected systems for control and automation. In practice, the term has also come to describe the technology and practice of working with information. These can come from apps, audio, videos, sensors, the web, social networks, among others.
Therefore, these items are so bulky that traditional processing software can no longer manage them, and consequently, they need specific solutions to turn into insights. After all, the raw data itself is unimportant; but what organizations do with the knowledge gained through data analysis does have great value. In short, it is exactly what the term translates to a vast amount of information that, at first, will not be organized. Thus, this “tangle” of items will be input for the formation of varied knowledge.
To make it easier, it is possible to make an analogy with the human mind: although we develop structured teaching techniques and methods, nothing we learn is isolated in our mind — on the contrary, making connections and synapses is precisely what allows us to develop an idea.
The same is true of this. It is possible to go much further when working with information in a large volume and varied nature. For example, in a database, these items are cataloged and meticulously organized. Thus, from the summation and manipulation of many of them, information is obtained.
However, in the concept, the focus is on the whole. In this way, any data contributing to the final result enters the account. The objective here is not to structure information to make it more accessible, as in a database, but to generate knowledge.
And What Kind Of Knowledge Is Valid?
In principle, information from different sources can be used. One should focus not only on what is generated by the business but also on the market. Through data science, solutions are found that generate time and cost reduction, support the creation of new products and more intelligent decision making.
However, the potential goes much further. On a production line, failures can be detected and prevented in real-time. Or, even considering the customer’s purchase journey, it is possible to analyze their behavior, offer personalized offers, and detect fraud.
Structured And Unstructured Data
To understand this, it is essential to know the different types of data contained in this set. Firstly, we have structured data, which is the most accessible data to organize and classify, as they have regularity. For example, the information contained in tables is usually structured, as it follows a cataloging rule. And if they are within the same column or row, they typically refer to common property.
This first type is also known as “multi-structured data” because it contains a great diversity in types and formats. For example, they can come from web applications, where there are interactions between people and machines. An analogous case is web log data, which includes text and images and information of a different nature, such as forms and transactional records.
Secondly, there are unstructured data, that is, data that standardized models and processing bases do not easily interpret. The content of publications on social networks, videos and photos, and WhatsApp audios, for example, fit this concept. After all, it would be tough to put them on a table and find a structuring pattern that expresses some knowledge of these items.
In fact, according to IDC projections, by 2025, about 80% of the world’s existing data will be unstructured. It is worth remembering that, before it’s technological solutions, this type of information could usually only be analyzed by humans. In conclusion, It is a collection of data from different contents, formats and sources, which interact in non-linear ways and with almost infinite variability. As a result, the joint analysis of all these items allows for continuous discovery and research.
Also Read: Big Data: A New Era Of Digital Communication