If there’s one type of technology applied in the business world whose effectiveness is unquestionable, it’s Big Data, which has significantly revolutionized the way that companies approach markets by establishing the practical use of information and its efficient analysis as a base.
The Worldwide Big Data and Analytics Spending Guide report, by IDC, predicts that the global value of the Big Data market will reach 202 billion dollars in 2020; while, at IDC Research Spain, it is estimated that, in 2021, 50% of organizations’ income will come from the monetization of data and that, up until 2020, over 50% of business will be generated from data. This obviously tends towards data centric.
This gives us an idea of why it has always been said that information is one of a company’s main assets, although, with digital transformation, it has become the most important capital for a company. If managed correctly, it may make the difference when gaining competitive advantages. However, the rate at which companies’ requirements when it comes to information and data processing are growing is so fast that conventional IT solutions are unable to keep up with them. Also, nowadays, organizations are inundated with data and struggle to identify which to prioritize, which actions to take and which to avoid.
Let’s consider how to sufficiently process the ocean of data implied by the 175 Zb that IDC predicts will be generated by 2025, as reflected in its Global DataSphere study. This is where Big Data and analytical tools come into play and reveal their full potential, while we continue to instill the idea that processing large volumes of information in real time to make business decisions based on reliable data is critical in order to manage companies in a better, and more cost-effective, manner.
Equally, today’s organizations need an integral understanding of their business ecosystem to give them a complete overview of all of the parts that intervene therein, from markets to clients, including products, competition, workers, partners, suppliers, legal framework... Subsequently, to maintain their competitiveness, companies need to create more value from the structured and unstructured data stored in their systems.
The efficient use of Big Data will favor immediate decision-making and will improve the relationship with the client by getting to know them better and enforcing a personalized service. It will also optimize the procurement of materials by identifying sales trends and will be of help in maintenance tasks thanks to the analysis of data generated by the machines. Ultimately, the company will have a holistic view of its ecosystem and will be prepared to react to any possible scenario.
But, to make the most of this technology, it requires optimal implementation. Big Data should therefore be implemented as a structured system that allows data from any source to be entered and stored, be it from the sales system, the client base, the social networks or the sensors in the devices, just to name a few. This data is saved in special file systems and categorized in databases to be interpreted.
On a second level, where the analytical tools come into play, the data is studied and ordered to obtain the results. This analysis allows us to identify patterns to determine trends and display the results in the form of reports, graphs or even concrete proposals. The aim of these results is to define how specific actions can help to achieve a business objective. To attain this objective, companies must face challenges such as the lack of experts qualified in data science, the quality of the data itself, aspects related with cybersecurity and the protection of confidential information, the increasingly stringent legal framework or take into account how the rapid evolution of technology can bring about changes that result in the obsolescence of IT in the short term.
But we must be sure of one thing: the binomial Big Data analysis is the convergence point for other tools and technologies such as wireless connectivity, AI or the Cloud, as they converge on this data analysis.