, which has become part of our IT jargon, refers to a large amount of data and information acquired){kind=link}
Big Data (big data), which has become part of our IT jargon, refers to a large amount of data and information acquired and managed daily by companies or entities. More than the size of these data, however, what attracts attention is their use or how they can be analyzed to extrapolate important information for all those companies that deal with statistics and market analysis.
Therefore, it is understandable how indispensable it is to manage this data or to possess technological tools capable of making a constant flow of information commercially and socially valuable and usable. We have almost gotten used to a revolution in the market now but have revolutionized how advertising campaigns are carried out.
Over the years, the greater diffusion of smartphones and tablets as companions in the daily life of each individual has made the arrival of data and information that are commonly requested to carry out even the most straightforward online operations even more pressing.
The aggregation of insignificant elements is therefore of enormous value. Just think that a simple “like” placed on a comment or a product multiplied by thousands or millions of users can influence the performance of a company and its future development. The concept of Big Data, however, is far from recent since it has been talked about since the end of the 90s to reach the scholar Doug Laney who at the beginning of 2000 formulated the theory of the three Vs., namely:
- Variety: the data arrives unevenly, being photos, documents, alphanumeric values, video, audio, and so on;
- Volume; a large amount of data from different sources (social media, financial transactions, online purchases);
- Speed: refers to the rate with which the data flow in real-time and the consequent need to use them promptly.
To understand the enormous amount of data circulating online every day (with values in the order of zettabytes), think of the fact that there are almost eight billion people on the planet, of which about two-thirds are active online and, therefore, perform periodically actions that generate the direct or indirect production of information that must be managed adequately.
Big Data, therefore, also has a life cycle or a set of processes that, from their collection, leads, through subsequent modifications, to their use, also passing through the reclamation phase since very often the set of data contains information that they are no longer helpful for processing purposes.
The two macro-types of actions that Big Data undergoes are the management or the series of processes concerning the acquisition and storage of information and Analytics, the analysis of this data that must occur as quickly as possible.
All The Necessary Steps In Big Data Management
Each user generates data by interacting with various devices and through different types of platforms. A large amount of information must be collected and stored to be used immediately or later.
The acquisition takes place through various channels: via API(i.e application programming interface) specifically used to collect data when accessing a site, with special software for managing documents, importing data from pre-existing databases, or interpreting and extrapolating the flow of data passing through the network or simple cookies through web browsing.
An enormous amount of information derives from all these operations, much of which is not helpful for subsequent analysis. It follows that they must be reclaimed or cleaned of all those that do not fall within the format required for processing. At this point, the Big Data must be stored and archived, but an enormous amount of information comes into play.
For this reason, in recent years, systems capable of storing large datasets have been studied and built. We then move on to analysis and modeling through the development of targeted algorithms and subsequent interpretation so that the information can be helpful for corporate performance.
Data Management And Apache Hadoop
Nowadays, data analysis cannot be tackled with the same methodologies used in the past as scenarios have changed, users have changed, and Data Management cannot ignore some primary considerations. First of all, the sources from which Big Data originates and are extensively discussed are constantly evolving. Therefore an increasing number of information arrives and must be analyzed quickly and precisely, identifying new sources to include them in the Management platforms.
Once the data has been identified, these must be taken in their entirety and archived since what may seem useless at the moment in a second analysis could be extremely important for an evaluation. The amount of data to be managed is genuinely immense, and until a few years ago, it was unthinkable to collect it quickly and at low costs. This is no longer a utopia thanks to new technologies such as Apache Hadoop, the key to working with Big Data by storing information regardless of when it will be used.
It is open-source software that allows you to manage large datasets enabling the archiving of a large amount of data over time. Apache Hadoop was designed to write applications that process data-parallel on clusters of thousands of nodes without losing reliability. Hadoop was created, unlike other systems, to store large amounts of data but optimize archiving activities to achieve these goals. It has libraries that allow the subdivision of information to be processed directly on the calculation nodes.
In this way, it significantly reduces access times because no network transfers are necessary. It is a very versatile and reliable software since the criticalities are managed directly on the calculation nodes. In this scenario, the use of Big Data appears more viable even in companies that cannot count on a high budget, as was the case until a few years ago. Once the data has been stored, their analysis is not aimed only at creating a report or a graph.
Still, it is necessary to bring the information obtained into the reality of that particular company of that unique individual to help it in the decision-making processes and succeed in this goal. New professionalism and new skills are needed, as in the case of the so-called “data scientists,” Or experts able to develop specific algorithms that do not provide trivial analyses but that can naturally support a company’s competitiveness.
Big Data storage platforms must equip themselves with tools and features capable of using the information at the right time. Even if it may seem obvious, the use of data is not as immediate as it looks. Most of the time, they are confined to databases. “Standing alone” hardly communicates with each other and therefore makes it impossible to interface and share information.
The Use Of Big Data In Small And Medium-Sized Enterprises
More than 80% of small and medium-sized enterprises have developed tools to use big data, having understood its importance to help them improve product quality, expand their business opportunities and accelerate decision-making power. . However, the main obstacle is represented by the limited economic resources to be used for this analysis and the identification and exploitation of the most relevant information for the business.
The tight budget represents a limit that sometimes seems impossible, even if there are simple rules to follow that allow even medium-sized companies to use data to improve their business without using too high sums. First of all, it is necessary to build a “business case” or focus on what objective is essential to improve the activity, thus optimizing the choice and analysis of valuable data without unproductively losing critical resources.
In sales, for example, it is necessary to study the previous behavior of the possible customer to propose targeted articles that that customer will most likely buy in a relatively short period. Another aspect to consider is the “collaboration” within a company. Many studies have shown that the most excellent results have been achieved in situations where a close relationship has been established between the different areas.
The manager cannot ignore the figure of the IT manager, just as he must convince company executives of the goodness of their choices in the context of data analysis. Most companies outsource extensive data analysis to external experts who can process and perform sophisticated research. This is undoubtedly useful, but it implies a high investment that not all companies can support.
However, it is essential to understand how to use the information with internal working groups without intermediaries since all the necessary data is already available to the company. It will be sufficient to identify them to make the most of them. This process is helped by technology that offers a wide range of IT solutions that non-professionals can also use.
Create reports and cards that can be easily shared, graphs of various kinds to cross data, and provide satisfactory solutions that go far beyond ordinary spreadsheets but for which no specific training is required. Speed is the critical word in this type of business.
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