Big data is both a concept and a field. Hence, as a concept, it is a term that pertains to datasets that are too large or complex for traditional data processing methodologies and tools to analyze. Furthermore, as a field, it corresponds to all techniques for analyzing these massive datasets for the purpose of extracting value or revealing and understanding patterns and associations.
Developments in digital communication, including wireless communication technologies, have underscored the importance of big data. The digital information age has resulted in an explosion of data of varied forms as people and societies become more dependent on the use of the internet, mobile communication devices, social media, and online services, among others.
The Pros: Advantages of Big Data and Applications
The primary advantage of Big Data centers on the need to process, analyze, and extract valuable information from large datasets for informed decision-making. This advantage comes with more specific advantages and applications for organizations, including businesses and governments, and individual decision-makers.
Below are the specific benefits of big data and its applications:
1. Optimizes and Improves Business Processes
For business organizations, one advantage of big data is that it enables them to understand their customers or target market, particularly their behaviors and preferences. Doing so allows them to provide better products or services, develop products based on market trends, predict patterns in consumption to provide an appropriate marketing response, and improve customer experience via the inclusion of value-added services and after-sales services.
It can also be a source of competitive advantage for businesses. Analyzing massive datasets can lead to the optimization and improvement in different aspects of operations. Manufacturers can mine data to reveal patterns in supply and demand to improve their supply chain, promote better inventory management, optimize distribution channels, improve order fulfillment processes, and make production processes more effective and cost-efficient.
2. Supports Progress in Artificial Intelligence
Another advantage of big data is its role in advancing artificial intelligence. The subfield of machine learning and its subset called deep learning depends on training large datasets. The quality of an AI application is dependent on the quality and quantity of data. It is important to note that the ongoing modern AI revolution has been made possible due to the explosion of big data and its continuous accumulation from the digital information age.
OpenAI is at the forefront of the modern AI revolution. It is responsible for both developing and commercializing one of the largest large language models in the world which has resulted in the realization of the practical applications of natural language processing. It was able to develop its Generative Pre-Trained Transformer model using massive data obtained from the internet. The same is true for other models from other technology companies.
3. Empowers Online-Based or Digital Economies
It is safe to say that digital communication and dig data have now become intertwined. Google depends on the analysis of large chunks of web and user data to power its Google Search and Google Ads products. The same is true for Meta Platforms that power its social media platforms such as Facebook and Instagram. Amazon, Netflix, and Apple crunch large amounts of user data to improve their products and improve different facets of user experience.
Nevertheless, based on the aforementioned, the relevance of big data to online-based businesses stems from the fact that more people have become more dependent on digital communications and online services. These business organizations are utilizing the data obtained from these users to improve their products and maintain their competitive advantage. The existence of the digital economy and its continuous persistence hinged on access to big data.
4. Equips Organizations with Better Capabilities
Remember that the application of big data does not rest alone on businesses. Several government agencies have been using tools and methodologies to process large datasets in their operations or programs. One example is predictive policing which uses data analytics to determine the potential for criminal activity. Another example is in maintaining national security via various intelligence-gathering methodologies such as signals and open-source intelligence.
Furthermore, in scientific research, big data expedites the process of data analytics, particularly for continuous experiments, such as in the case of particle experiments or simulations. Note that some medical researchers are using patient data and genetic information to discover and develop new therapies and understand diseases better. These activities require the use of supercomputers that are capable of processing enormous data at a much faster rate.
The Cons: Disadvantages of Big Data and Challenges
It is also important to note that big data comes with disadvantages and challenges. Some find its applications difficult to implement. The collection of massive datasets also has ethical and legal risks become several jurisdictions have policies against unauthorized collection and use of user data. These issues limit the promises of big data.
Below are the disadvantages of big data and its applications:
1. Main Privacy Concerns and Security Issues
One of the notable disadvantages of big data centers on emerging concerns over user privacy and security. Even large business organizations such as Meta Platforms have figured in several cases of data breaches. Companies such as Google and OpenAI have been criticized for collecting and using data from the internet to build their products. Several critics have alleged that some data are protected by intellectual property rights and privacy rights.
Furthermore, with policies or laws becoming more stringent, such as the General Data Protection Regulation of the European Union, organizations seeking to develop and maintain capabilities or advantages for collecting and using massive datasets need to invest in protocols, processes, and technological infrastructures aimed at protecting data and mitigating security risks. This makes big data inaccessible to organizations with limited resources.
2. Technical Challenges and Requirements
It is also important to reiterate the fact that big data requires both processing capabilities from a capable technological resource and technical proficiency from qualified professionals. In other words, for an organization to have the capacity to mine and process large volumes of data, they need to invest in acquiring capabilities composed of large databases, capable processors, and information technology and data analytics professionals.
Furthermore, these organizations need to have a certain degree of competencies or proficiencies that would enable them to address more specific requirements. These include data storage and transportation, database management, data access, and data quality and validity assurance. Some organizations also need to have infrastructures that are scalable for future-proofing. Take note that these requirements necessitate substantial investments.
3. Need for Ensuring Data Quality and Reliability
One of the main characteristics of big data is that the entire dataset can be composed of different types of data. Another characteristic is that these data are often unstructured. These are not the main problems. The problem is that the dataset can suffer from issues such as inconsistencies and inaccuracies. Both translate to poor data quality and reliability which can further result in flawed analysis and misleading insights or predictions and conclusions.
The aforementioned problems come from the fact that the data come from diverse sources. This can be resolved through quality assurance measures in place. However, because the dataset is either massive or complex, human-led assurance quality practices can be a painstaking task to accomplish. The workaround is to use a particular AI algorithm or AI model that can help in automating big data analytics. But this is an expensive solution.
4. Implications of the Disadvantages of Big Data
Another one of the main disadvantages of big data is the concerns over its value and implications for organizations. Remember that resolving the challenges and responding to the requirements of its implementations involve substantial investments. This is also true for on-premise capabilities that involve in-house IT infrastructure and IT personnel. Not all organizations have the means to afford these costs. These costs are also not valuable to all organizations.
Nevertheless, because of the costs associated with its implementation, smaller companies are at a disadvantage when compared to larger organizations with bigger access to resources. It appears that due to the associated costs, as well as the other disadvantages of big data, its advantageous applications only benefit large organizations, especially established businesses, expanding the competitive gap between them and smaller and less-capable businesses.
Takeaway: Advantages and Disadvantages of Big Data
The advantages and disadvantages of big data make it both a source of competitive advantage for certain organizations and even individuals and a cause of problems for other organizations and other individuals. It can empower organizations while also fueling the current digital economies. It is also one of the main drivers of the modern AI revolution. However, because of the costs needed for its implementation, its advantages or benefits are not accessible to all. Another one of the notable disadvantages of big data is the ethical and legal issues associated with its collection and use. Acquiring a large dataset both has financial cost and legal implications. This further affects how organizations and individuals can benefit from its use.
