Big data analytics
Big data, as the name suggests, is voluminous data that is so hard and complex to process by the use of traditional methods. The origin of big data is from various sources within an organization or entity. Some of the notable sources of big data include videos, social media homes, along with smart internet of things devices. Moreover, it is also important to note that big data comes in various forms to include structured and unstructured forms.
Conversely, there are various importance of big data. One of the importance of big data is that it can be used by firms in providing valuable information that can help in refining their various strategies to include their marketing strategy (Hassanien, 2015). This can subsequently help the organization in subject in increasing its conversion rates along with customer engagement. Moreover, the other importance of big data is that it helps an organization to meet the specific needs of its clients as the organization in subject now adopts a holistic view. In addition, big data can also be useful in trend analysis as it captures organizational data overtime.
Given the increased rate of technological innovations and inventions, big data is known to experience some changes in the future. What is expected is that big data will go the virtual way. In this case, the expectation is that all the transactions and records of an organization will be stored in the cloud (Marr, 2015). As a result, anyone who has permission to access the organizational database can access the system from anywhere without having to visit the organization physically as is the current case in most entities that have not advanced in their technological systems. An analysis of this case at hand shows that big data will lose its popularity and may end up adopting another name.
On the other hand, there is a difference between big data analytics and regular data analytics. When it comes to big data analytics, this refers to the process of looking at large sets of data that comprise various data types with the aim of discussing knowledge, identification of patterns, and establishment of relationships. On the other hand, regular data analysis is that which aims at converting raw data into a usable and meaningful format.
Conversely, big data analytics is known to have various critical success factors. One of these is the relevancy of the data sources with the aim of avoiding duplication and unimportant results. Apart from that, the other critical success factor for big data analytics is that of completeness. This aspect ensures that all the essential and important information is captured. Last but not least, the other success factor is clear business goal that the firm aims at achieving.
On the other hand, there are various challenges that one should be mindful of when dealing with big data analytics. One of these is that of data security. It is usually very difficult and challenges to enhance the security of big data. Apart from that, the other critical challenge is that of recruiting and maintaining big data analytics staff. Such kind of persons may be too expensive to maintain. In addition, the big data analytics staff members usually have high turnover rates.
References
Hassanien, A. E. (2015). Big data in complex systems: Challenges and opportunities.
Marr, B. (2015). Big data: Using smart big data, analytics and metrics to make better
decisions and improve performance. Chichester: Wiley.