Big Data, in a literal definition, means mass of data, etc. Indeed, companies today face the problem of managing data efficiently. The use of Big Data technology allows for rational analysis of this data.
This concept meets the needs of companies to find relevant solutions in order to offer close to perfect services to customers.
The different types of Big Data
Big Data is a technology which is an integral part of Data science which it subdivided into two types.
Operational Big Data
This brings together all the daily data. These are social media interactions, online transactions, or even the day-to-day organization of work. It’s a bit of a jumble of data, but it’s essential to getting meaningful results.
There are several elements that define your daily life and which are part of operational Big Data technologies. These include online ticket booking, online shopping, employee profiles or social media data.
Big Data analytics
This type of Big Data uses the data of the previous one in a more objective way. With its complex nature, it’s based on operational data to come up with relevant solutions. These solutions can be as part of a new product, a business solution, or a new sales strategy.
Big Data analytics brings together technologies like weather forecasting, forecasting customer behavior, stock marketing, and more.
The 3V rule
There is a so-called 3V rule which consists of three rules that concretely define Big Data. It's about volume, variety, and speed.
Volume
This first principle corresponds to the capacity of information produced per second. Indeed, it’s estimated at 2.5 trillion bytes of data consumed every day.
This information comes from daily messages, shared media, in short, any data available on the internet.
Information is everything these days, and many companies like Google and Facebook benefit greatly from it. The challenge is to be able to derive from this large pile of data a result capable of pushing your company into high spheres.
Variety
The variety of Big Data means types of data: structured data and unstructured data.
Structured data is nothing more than information that can be stored, accessed and processed in the same way. They’re of the same nature, the same format. In terms of structured data, there’s for example a table containing employee information in a database.
As for unstructured data, it’s data whose format is unknown or different. They can be compared to a heterogeneous mixture. It’s, therefore, more difficult to treat. They represent 80% of all mass data.
However, Big Data technology makes it possible to analyze this data in order to be able to produce a relevant result.
Velocity
This rule that big data must obey is the impressive way of processing information and acquiring it. It’s also the viral nature of the dissemination of data.
Apart from the 3V rule, there are other obvious factors that characterize big data. Like veracity, which refers to the reliability and credibility of information. Given the large amount of data taken into account in the analysis, it's hard to be able to pull the wheat from the chaff. However, scientists are considering how to develop techniques to meet this need.
The other factor when it comes to big data is the value delivered. Indeed, Data Science is not being used to its full potential. On the other hand, some companies use it to be competitive in the market and to stay ahead of their competitors.
Technologies associated with Big Data
Behind Big Data lie four main technologies which are defined as follows.
Data storage
Data storage is the set of methods or techniques for the preservation of digital data. The common means used are hard disks, USB keys, or CD disks. These means cannot be used by companies because they need a large storage capacity. The available systems that meet existing needs are equipped with artificial intelligence.
In addition, some of them use systems grouping servers equipped with a set of hard drives. The possibility of opting for online storage is not ruled out.
The different methods used for information storage consist of the transfer of digital information to a medium. The unit of measurement used is the bit. Depending on the size, we talk about kilobit, megabit, gigabit, terabit, petabit and exabit.
Data mining
Data mining calls for the analytical character of Big Data. It allows a complex analysis to be carried out in order to transform data into useful information. Its function is to find relationships between data by targeting patterns.
There are pre-designed solutions on the market that can accomplish the function of data mining. These solutions are generally software with sophisticated and complex algorithms. These allow the segmentation of data and assess future probabilities. Using this kind of software will allow you to make meaningful decisions based on the behavior of your customers.
Data analysis
Briefly, it consists of finding links in all the data in order to find a statistical result. For example, analyzing the key performance indicators of an advertising campaign in order to improve future campaigns. You can choose to analyze the age, gender, and occupation of your customers to determine which products each category of people prefers.
To achieve this, certain mathematical notions must come into play. However, softwares are available on the market that can help you synthesize information.
Data visualization
Big data visualization is the graphical representation of information. As the name suggests, it makes use of visual elements such as graphics to observe future results. Also, visualization makes it possible to simulate the impact of decisions.
How to profit from big data as a business ?
The world of Data Science is in an increasing evolution that one must think of using for commercial purposes. Analyzing company data will enable efficient market research, customer-oriented product development.
In addition, the use of Big Data allows you to benchmark the behavior of your customers to come up with good offers. This technology can be used to limit fraud, and even integrate machine learning into your habits. It’s also used to also achieve operational efficiency and boost innovation.
Big Data can enable better data management by centralizing it.
Ultimately, Big Data, in its operational and analytical character, helps companies to make strategic decisions. However, its use is not yet widespread and its adoption by all would make companies real competitors. The growth of this sector only benefits data scientists, as few people are qualified for this job. The future of this science remains an enigma, but as long as there is information it will see bright days and astonishing improvements.