Analytics as a Service and Cloud Computing

Analytics as a Service and Cloud Computing

Analytics as a service, AaaS, is the provision of analytics software and its operation using web-delivered technologies. It is a solution to a business alternative for developing internal hardware setups to perform business analytics. Cloud computing is the practice of delivering most services through the internet without directly involving the user. The services in cloud computing include data storage, software, servers, among others. Cloud computing is an easy way of safely keeping files and any information and retrieving it anytime, anywhere on demand.

AaaS uses predictive analytics, artificial intelligence, and data mining to examine and reveal the insights and trends from existing data sets. Previously, running large processes such as analytics on data warehouses required a bigger team of data scientists and engineers (Drury, 2019). AaaS has solved the past problem that involved those teams that always caused the business large amounts of money to sustain and reduce the cost of production and prediction in business.

Companies with different capacities at their IT departments look for AaaS for simpler descriptive analytics, which is then unpacked by their data scientists. In contrast, companies with unstable information technology abilities use Analytics as a Service for considerably more complex prescriptive and predictive analytics in business (Harris, 2018). AaaS helps businesses reduce the cost of analysis while offering better insights, quicker comprehension, and more agile business processes.

AaaS informs future processes and product decisions. It simplifies analytics accessibility to the users such that team members in a business set up require minimal knowledge or flash understanding of analytics to access. Ireland (2020) reported that AaaS combined different data types ranging from structured to unstructured, into a single data narrative through Artificial intelligence and machine learning to extract customers purchasing power and patterns, preferences, and expectations. This makes it easy for a business to make adjustments on their production and meet the market demand and standards.

Cloud computing has taken over other technologies for data management in many companies today. Companies resorted to using cloud-based data management and business intelligence solutions to effectively and quickly manage and analyze data (Camargo-Perez, Puentes-Velasquez, and Sanchez-Perilla, (2019). The challenges that businesses face today are big data and ineffective analysis related to the need to protect the customers’ privacy and the businesses’ important information from being accessed by unauthorized persons.

Perfect data management techniques and analytics models are important requirements for implementing an integrated business intelligence solution. Businesses using cloud computing techniques ensure that their big data is stored safely in the clouds without any unauthorized persons (Chang, Kuo, and Ramachandran, 2016). The unlimited space in the cloud storage is advantageous to the businesses and a characteristic they have taken advantage of by storing huge amounts of data. Cloud computing is a solution to small businesses that have no obligation to buy a server to get the dominant software, framework, and platforms on the planet.

AaaS and cloud computing performs to the advantage of the business and provides the best business intelligence system. It saves a lot for the business to incorporate the two technology-based systems in their operations. The businesses now have an obligation of keeping the data safer from attackers who are the continuous threats to their well-being.

References

Drury, R. L. (2019). HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide Assessment, Intervention, and Performance Optimization. In Autonomic Nervous System Monitoring. IntechOpen.

Harris, R. J. (2018). The Evaluation of Constraints Within Information Technology Departments Concerning the Statistical and Analytical Problems of Big Data (Doctoral dissertation, Northcentral University).

Ireland, E. (2020). Strategies Big Data Analytics Specialists Need to Improve the Analytical Methods Used for Classifying Structured and Unstructured Data (Doctoral dissertation, Colorado Technical University).

Camargo-Perez, J. A., Puentes-Velasquez, A. M., & Sanchez-Perilla, A. L. (2019, November). Integration of big data in small and medium organizations: Business intelligence and cloud computing. In Journal of Physics: Conference Series (Vol. 1388, No. 1, p. 012029). IOP Publishing.

Chang, V., Kuo, Y. H., & Ramachandran, M. (2016). Cloud computing adoption framework: A security framework for business clouds. Future Generation Computer Systems, 57, 24-41.