What is Big Data?
At a time when data is ever-present in companies, Big Data is gradually establishing itself in all sectors of activity. In fact, according to a study by IDC, the Big Data and data analytics markets are expected to grow by 12.8% by 2025. Considered a strategic asset for companies to remain competitive, today it is impossible to ignore the new trends in Big Data.
Definition of Big Data
Big Data refers to a set of varied and voluminous data that arrives at high speed. Using this definition, we can identify the 3 big V proprieties of Big Data.
In fact, since new technologies (internet, social networks) are present in the daily life of many companies, companies have to deal with a set of data from a multitude of sources (emails, social networks, traffic on website, etc). Impossible for a classic tool to collect, store and process all this data flow. Therefore, it is essential for organizations to move towards a Big Data architecture.
The Big V Properties of Big Data
Quality is a guarantee of competitiveness, the more data we have the more information we are able to provide. It is at this precise moment that organizations are confronted with the problem of the 3 V proprieties of Big Data.
Volume: the most spectacular element. Companies have to process a large amount of data. Logically, the amount of data varies according to the size of the companies (the data comes from the web, smartphones, sensors, etc.).
Velocity: Data must be collected, stored and analyzed. The challenge of Big Data is to be able to process this flow of data in real time.
Variety: the data collected by a company is very varied. Indeed, we can identify 3 types of data (unstructured data, semi-structured data, structured data).
With the evolution of the number of data to be collected, we can identify two new major Vs to deepen our analysis:
Veracity: with the multitude of data collected on the website or social networks, the accuracy of the data must be verified. Only exact data can be used for decision making.
Value: you need to know the value of the data since not all of it is qualitative. In a Big Data project, it is recommended to set up a tool that cleans the data to ensure its relevance.
Operation of Big Data
Big Data has created new tools that now cover all kinds of data sets. In this sense, the latter will allow a more in-depth analysis of this data and will offer organizations the possibility of collecting valuable information when making decisions.
To successfully implement Big Data environment in a company, it is important to adopt the following 3 actions:
Integration → organizations must confront a first challenge: collecting and processing a large amount of data from different sources and applications. To successfully process terabytes or even petabytes of data, it is essential to identify new strategies and adopt new technologies.
When integrating data, you must import, process and ensure that the data is formatted and accessible to the people in charge of analyzing and using them to make strategic decisions.
Management → with an impressive volume of data, Big Data requires a storage location. You can choose from different options:
- Choose a cloud infrastructure
- Choose to store data on company local computers (on premise)
- Both of them
Data storage is done, as you consider best for your company. It is at your choice to impose your requirements about collecting, analysis and processing.
The analysis → the profitability of your Big Data will be achievedthanks to the analysis and actions that you have implemented on the data that companies collect. Data Visualization allows you to have a different visual analysis and potentially make new discoveries. It is advisable to create data models using Artificial Intelligence and Machine Learning.
Some Big Data use cases
The use of Big Data techniques can allow a company to understand the state of the market, identify the causes of certain malfunctions or even set up commercial actions. We present some use cases:
-> Anticipate future needs: Big Data offers organizations the possibility of making predictions using structured data for their short, medium and long-term strategy.
-> Avoid fraud: with the explosion in the volume of data, fraud is more and more frequent within companies. Embedded in data, fraud is difficult to identify and increasingly sophisticated. With its ability to absorb a large volume of information, Big Data will identify data patterns potentially affected by fraud.
-> Boost innovation: Big Data technologies can offer the opportunity to a company to innovate using analysis on the interactions between organizations, processes and human beings. Subsequently, it is essential to determine new ways of using the information from this study.
-> Customer experience: companies can now have a more precise vision of the customer experience thanks to data collected from social networks and the website. Customer experience can be analyzed and optimized to deliver personalized experiences.
To conclude, a Big Data strategy should be put in place to allow the company to automate the collection, storage, analysis and processing of these data. How to do it? Use new tools and competencies of new professions (Data Analyst, Data Scientist, Big Data Engineer, etc.).
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