Today the challenge of organizations is to create information, innovation and value starting from Big Data. Data, in fact, are the vital energy that powers all business processes, projects and strategies.
The importance of Big Data
Today’s market is characterized by its destabilization, on the one hand due to the rapidity with which new technologies are developed and, on the other, due to the ever increasing competition that leads the markets to saturation. It follows that to be able to emerge, companies must continuously innovate and keep up with the times.
With the development of new devices, ways of interacting between people and machines, the amount of data available to organizations has grown dramatically. Analyzing this huge amount of data would lead to new knowledge that can guide companies towards more informed decisions. It is no coincidence, in fact, that market forces are rewarding organizations that use Big Data and are guided by them. However, to achieve success, organizations need to be able to collect and use data strategically.
The Vs of Big Data
In 2021 Douglas Laney created the “3V Model” in which he described the three main characteristics of Big Data. The first stands for volume and indicates the enormous size of data generated and collected. The second concerns the variety of data collected which can have different forms and be structured, semi-structured or unstructured such as texts, web pages, video and audio. The latter refers to the velocity with which the data is generated.
To these 3 Vs others have been added over the years. The fourth V is the veracity, that is the quality and significance of data collected and / or processed. Then there are value and visualization. The first indicates the degree of connection of data with other data and the visualization, while the second indicates the need to summarize the most relevant data and the knowledge extracted from them in a visual and easily interpretable way.
The great challenge of Big Data
Most Big Data is in an unstructured form because it is written in human (or natural) language, which is difficult for machines to understand. Therefore, we begin to accumulate unstructured data that the software will not be able to classify and consequently, it will not be able to return data that becomes useful information to build our business strategies. To overcome this limitation, Natural Language Processing (NLP) algorithms have been created and perfected in recent years.
Natural Language Processing and Semantic Clustering
NLP is a branch between Artificial Intelligence and Linguistics. It’s objective is to making the machine interpret and understand natural language, with the purpose of transforming written texts into well-structured and classified data, on which to carry out subsequent analyzes aimed at creating value for the organization. We can intuit the complexity of this process given the continuous mutability of human language. The data must also be analyzed from a semantic point of view, understanding the meaning of the individual words and the relationship between them.
Semantic Clustering is the process that allows the machine to divide words into clusters according to their semantic content. In other words, it takes unstructured data and structures and classifies it so that the machine is able to return useful information. Thanks to it, for example, we can take advantage of Sentiment Analysis services for web content, or we can speed up the recruiting and people management processes in the field of Human Resources.
In conclusion, it is not enough to collect data by ignoring the method of collection and thinking that accumulating large quantities is sufficient to create value. In order for them to bring value to the organization, the precautions described above are necessary.
The solutions of Pragma Etimos
We develop structured and classified data models (Intelligence Data Table) resulting from years of innovation in the semantic field and we use them as a basis for the construction of neuronal models, territorial links and semantic analyzes. We thus obtain information from unstructured data, creating relationships and classifying them so that they can be reused in an analytical and strategic way.
Advantages:
– Quality usable data
– Enriched database
– Strategies based on consistent and comprehensive data analysis
– Reduce time and costs
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It is a short sentence. Data is vital energy. I hope to create a great news.
In conclusion Semantic Clustering is cool. I talk about it t. As a result, it fell over.
Data Intelligence is very important. Today we talk about Semantic Clustering.
I enjoy his company because he always tells interesting stories. For example about Data Cleansing.
Data Cleansing is Data Quality. Infact, they clean data and transform them in quality data.
This article is usefull? Great! In this paragraph, I’m going to discuss a few reasons why practice is important to ICT skills.
Fantastic!
Whats the name of V of Big Data?
Velocity, Value, Vericity, etc. For example, yuppy. Moreover, that number rises to as much as 90% when you put theory to practice. In conclusion, following up explanation with practice is key to mastering a skill.
The passive voice is a monter, moreover. Firstly, the only way to truly learn a skill is by actually doing what you’ll have to do in the real world. Secondly, I think practice can be a fun way of putting in the necessary hours.
Data intelligent is on the table. Are you sure? Yes, I, am. It is fantastic! I’m tired. Therefore, I’m going to bed.
It is a branch of LNP.