There are two types of analytics employed in businesses: operational and discovery analytics. Operational analytics, involve statistical analysis, data-driven techniques, and data mining methods employed in facilitating day-to-day decision-making processes and operations (Böhm et al., 2016). It comprises the usage of data analysis and business intelligence tools to streamline everyday business or industry processes in real-time. The approach is centered on establishing a trusted system that promotes sharing of highly accessible data between teams in the business. Operational analytics functionality is prevalent in areas that involve diagnostics, manufacturing, billing automation, and supply chain tasks. For operational analytics to be successful, the organization requires the employment of a robust team of experts in data and business analysis.
The finance industry has undergone a tremendous transformation with regard to technological advancements through the use of cloud computing, artificial intelligence, and robotics (Cornwell et al., 2022). Due to the changes, a lot of implications continue to rise in the industry, particularly due to the rising use of data in the provision of financial services and products to customers.
The financial sector is one of the industries that have access to multiple consumer data to collect, analyze and employ for organizational objectives. However, the processes for collecting, integrating, analyzing, and solving various problems in the sector, are challenging (Cornwell et al., 2022). In a single day, a financial institution processes millions of transactions. Thus extracting data from these transactions and keeping track of them can be overwhelming, and time-consuming. Despite this data being so insightful, inadequate sorting and improper structuring of the big data can be a substantial threat. Often giving competitors an advantage. To attend to the rising requirement of managing big data in financial institutions, and survive in the competitive market, where quality data analytics are key, many are adopting operational analytics technologies.
On the other hand, discovery analytics facilitate the creation of new creative solutions or products and services that, for instance, can influence robotics or customer purchase decisions. Discovery analytics comprise the utilization of rapid analytical techniques that aid in robust explorations of multiple data sets. Overall, the analytics aid in homogenous systems integration increased access to data, sophisticated analytics implementation, reduction in manual coding, and employment of superior computing systems.
Remember! This is just a sample
You can get your custom paper by oneof our expert writers
In the financial industry, cyber-security is a rising concern for the big data collected and processed in these institutions. Loss of information, even for the transactions from a minute can lead to the closure of an institution. The value of financial institutions is well-known and the risk of loss is constantly an imminent threat to their data and the institution’s overall organizational objectives. Note, that with discovery analytics, the big data can offer significant insights into weaknesses that may be exploited by malicious data miners. It is important to note that installation of complex hardware peripheral devices and software are not assurances of data security. That is why discovery analytics may facilitate the analysis of data and using the outputs in identifying highly valuable data sets, and adopting measures that protect such. Preventing access to every bit of data, as noted before, can be time-consuming and overwhelming. Thus the use of discovery analytics to create new strategies for protecting valuable data first is an important milestone in mitigating cybersecurity in its entirety.
Quality Work
Unlimited Revisions
Affordable Pricing
24/7 Support
Fast Delivery