Introduction
There has been a massive window of opportunity created by the proliferation of big data for any business ready to take advantage of it. The proliferation of connected devices is mainly responsible for the rise of “big data.” Smartphones, laptops, and sensors in the field add to the already available mountain of data. There must be a method to store all this information and tools to extract valuable insights. For one thing, the proliferation of smartphones has fueled the expansion of social media, which has increased the volume of unstructured data available to businesses. However, decisions based on unstructured data can only be made once the data is cleaned and readied for analysis. Despite these obstacles, large amounts of data are still arriving from many sources, making it more challenging to use the data effectively. For businesses to reap the benefits of big data, its many features must be harmonized to produce the intended effects. Therefore, this study aims to investigate the relationships and interactions between the various aspects of big data so that their combined effects may optimize big data’s potential.
Analytics for large amounts of data
In 2011, it became clear that big data and competition were crucial to boosting productivity and creativity. The number of individuals using the Internet has increased dramatically, resulting in a wealth of new data for analysts and other consumers of big data in 2018. The data created by internet users have four main qualities that helped define “big data.” Velocity, which quantifies the pace and pattern of data creation, is one feature (Géczy, 2014). The term “variety” was used to describe the numerous forms the gathered data took. In this context, “volume” meant the vast quantity of data produced, which was expressed in bytes.
Big data analytics is a technology that evolved from big data. Big data- pushed for the requisite technologies to help significant data users make sense of the data in their stores. Big data analytics contains the various processes used to analyze the different types and large volumes of datasets to discover patterns, new relationships, market shifts, and consumer behaviour. Significant data analytics results are critical to decision-makers since it offers them an upper hand in improving the quality of their decisions. Big data analytics differs from traditional business analytics, where some data types, such as unstructured data, cannot be analyzed. Though the earlier three characteristics of big sufficed initially, the evolution and complexity of data being generated by the growing technologies forced the inclusion of other techniques, thus resulting in five major characteristics of big data. The use of big data has created new opportunities for different economies, allowing tremendous growth in the modern world.
Characteristics of significant data Volume
Hariri et al. (2019) define a volume of big data as the massive amount of data generated each second and is applied to the size besides the scale of the dataset (Géczy, 2014). The term dataset is deliberately used because big data cannot meet the volume threshold since time besides data type may influence the definition. The types of data which reside in exabytes and zettabytes
Are the only ones considered big data? However, this definition still faces a challenge where not all organizations that use big data can collect data that meets the definition. On the same note, some organizations collect more significant volumes of data that exceed the definitions’ specifications. These new volumes collected in small portions lead to new aspects and scalability besides uncertainty in big data. The other challenge resulting from significant data volume is that most of the current techniques are designed for small-scale datasets and are also disadvantaged when evaluating and understanding the data’s scale.
Variety
This is defined as the various form of data within a dataset. These forms may include unstructured, semi-structured, and structured data. The structured data includes data stored within a company database. On the other hand, unstructured data is found in the text besides multimedia content, which is present in random and more challenging to analyze. The semi-structured data include that data found in the NoSQL databases and has tags that help separate the different data elements. Uncertainty in this characteristic comes when the time for conversion of the different data types comes. This is because there is a need t convert unstructured data into structured data to help during the analysis process. research h shows that traditional data analysis techniques face challenges in dealing with multi-modal, incomplete, besides noisy data (Acharjya & Ahmed, 2016). this is because the techniques are only designed for well-formatted input data where the unstructured besides semi-structured data do not meet the criteria.
Velocity
Velocity is the speed of processing data, emphasizing the speed at which data is processed and produced. A good example is where the Internet of things devices consistently produce large volumes of sensor data, and delays in processing the data besides sending the outcomes of the analysis process to the decision-makers must be on time.
Uncertainty
Uncertainty presents itself in every aspect of big data learning and may come from various sources, starting from data collection complexity and data type. The complexity of multimodality, besides data variance, impacts data timing and contributes to its incompleteness. The different forms of uncertainty within big data and big data analytics can negatively impact how effective the outcomes will be. Data augmentation of the analytics techniques must help deal with uncertainty (Acharjya & Ahmed, 2016). Dealing with uncertainty in data analytics is critical, where probability theory plays a significant role in ensuring that randomness is incorporated to help deal with statistical characteristics concerning input data.
Conclusion
The growth in big data has opened new opportunities for organizations to utilize the benefits of big data. Through the growth in mobile computing and the Internet of Things, big data has contributed to the wealth of information. However, the use of big data faces numerous challenges concerning uncertainty. Uncertainty arises from the various characteristics of big data. Velocity is the speed of production and processing. Volume, the rate of big data generation, and variety, the different forms of data generated must all work together. This is because the uncertainties within big data arising from the lack of required speed, unclear size, and the different forms of data generated, thus making it harder to generate accurate and more efficient outcomes from big data.
References
Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1–16.
Acharya, D. P., & Ahmed, K. (2016). A survey on big data analytics: challenges, open research issues, and tools. International Journal of Advanced Computer Science and Applications, 7(2), 511–518.
Géczy, P. (2014). Big data characteristics. The Macro theme Review, 3(6), 94–104.
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