In this blog post, we discuss Big Data Challenges and how to implement them.
It is fundamental to understand that Big Data challenges do not rely on processing large amounts of data.
It is about using all the available data more effectively in an agile way in order to produce actionable insights. In this way, Big Data systems must consider the following:
Volume: The information produced in the world grows exponentially, referring now to Terabytes or Petabytes. It is not uncommon for a company's data to double every few years or so.
Variety: The available data can be structured or unstructured, internal or external to each company. Now they can count on non-traditional sources such as social networks, electronic devices, or sensors that allow knowing movements and daily habits, among others.
Velocity: Based on how quickly data is received, processed, and decisions can be made. As information grows so fast in volume and variety, it forces process updating.
Veracity: It refers to the reliability of the data. By extracting those that have the required quality, it is possible to obtain accurate information, which optimizes decision-making.
Value: The idea is to always extract really useful information for decision-making purposes.
Companies that have access to valuable customer data can establish trends and predictions, as well as behavior patterns that optimize the decision-making process with respect to their business or make more accurate and timely proposals in front of their clients.
Big Data Challenges: Implementation Process
The implementation of Big Data is not an easy task and keeping up with technological change requires constant work. Here are some Big Data challenges to consider:
Development of technological capabilities: Companies will need to overcome budgetary restrictions and the difficulty of estimating the profitability of developing and adopting the technologies necessary to store, process, and adequately analyze high volumes of information with the required speed.
Human resources: Currently, most corporate IT departments lack the human capital required to exploit the potential of such technological development. Finding and employing "data scientists" is critical.
Identification of useful data: With the volume and variety of information produced growing exponentially, a difficult task will be to separate the really useful data from those that are not.
Definition of objectives: It is not only about deciding what information is relevant, but what is the objective that you want to achieve using large volumes of data. This will allow you to make decisions in the right way and at the right time for your application.
Data protection: Data contains sensitive information and there are no clear answers on the best way to take care of it. The exchange of information between different jurisdictions goes beyond the merely local and national sphere, making analysis and controversies the order of the day at a global level. The debate continues, who is the "owner" of the data and how much can it be used by those who have access to it.
Cybersecurity: Thanks to the value that information has been acquiring, large-scale cyber breaches are becoming more and more frequent, generating a great financial loss for many organizations. It is essential that organizations implement information security schemes with the capacity to respond to incidents (especially financial institutions).