The 5 challenges Big Data faces

The 5 challenges Big Data faces

The range of Big Data application ranges from decision making to drastic increase in productivity, through customer segmentation. The need for talent, the quality of the data, its correct regulation and protection, together with the dynamic change are the great challenges of this technology.

Big Data is one of the most important technological trends for the digital transformation of companies, since it has represented one of the biggest impacts in different economic and business sectors. The application of this type of technologies is varied and goes from decision making, through customer segmentation, to drastic increase in productivity. For example, 53% of companies worldwide have substantially improved their Customer Service services thanks to this technology.

However, despite the fact that the investment in this type of technology does not stop growing, the reality is that 65% of the Big Data-based projects launched in Spain end up failing. The reasons are due to the scarce technological and formative preparation of the companies, or for not having external experts that help to design and monitor this type of innovations. In addition to that the digital transformation in Spain advances, but at a medium pace, companies must analyze the challenges that Big Data poses and determine how it will impact each of their processes when implementing it.

We have identified the following challenges:

– Need for talent: The progress of Big Data in recent years has been so rapid and has impacted in such a way that professionals in this field have become the most coveted by companies. Data scientists are among the highest paid experts in the industry. This is due to one of its main functions, which is to analyze large volumes of data with different technological solutions to determine if the information is valuable, with the aim of improving different types of processes in the company.

– Data quality: One of the most important challenges in implementing Big Data solutions is to ensure that the information analyzed is accurate and relevant to the business. Although this process slows down the submission of reports, it will be essential to ensure that the ideas to be implemented are based on relevant information.

– Cybersecurity: The storage of data, especially those that are classified as confidential, is usually one of the most attractive objectives for cyber attacks. Companies must ensure that their data repositories, whether on physical or virtual servers, have the necessary security measures to prevent theft, kidnapping or even the destruction of information.

– Regulation: Most of the information stored by companies is confidential or private, so it is important to comply with certain legal frameworks specific to each industry when storing and analyzing data. As a result of the rapid advancement of technologies in Big Data, companies must guarantee the protection and circulation of their own and customer data with the latest regulatory codes such as the General Data Protection Regulation.

– Dynamic change: The constant and rapid development of technology forces organizations to rethink the possibility of investing in Big Data tools. Without a correct analysis of the processes that you want to expedite or the costs that you want to save, it can result in poor implementation, causing a loss of money, time and obsolescence in the short term.

Big Data offers companies the possibility of obtaining detailed, reliable and thorough knowledge to make smarter and less risky decisions. The crucial importance of this technology lies not in storing a large volume of data, but in knowing how to analyze them to extract the maximum value from them. Companies must first consider for what type of processes they need to implement data analysis solutions and what challenges their implementation presents, in order to reduce costs and increase business profitability.

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