Cloud Analytics
Data analysis is the collection, transformation, and organization of data to have more meaningful insight into the data and make better-informed decision-making. Data is being used and created every day by individuals and organizations alike. It's almost impossible to run a successful organization without data analysis to make data-driven decision-making. The quantity/volume of data being created daily keeps increasing exponentially in line with its value.
What is Cloud Analytics?
To put it simply, cloud analytics, also known as Software-as-a-service (SaSS), is any data analysis done in the cloud (private or public) with the help of a service provider. Cloud analytics involves using analytical software and scalable cloud computing of data to give organizations new insights and enable them to make data-driven decisions.
As earlier stated, data is generated at a higher rate than ever before. With innovations like the internet of things creating data at high speed, storing, managing, and processing data will be difficult and expensive to do on physical databases. The cloud acts as a repository of data with different data sources. Service providers like AWS and Microsoft support cloud-based analytic tools that give organizations the ability to store, process, consolidate and access data from the cloud anytime and anywhere in the world. Cloud-based analytics tool includes; Power BI, Tableau, AppOptics APM, etc.,
Cloud analytics is the embodiment of the elements of analytics that include; data sources, data models, processing applications, computing power, analytical models, and sharing and storage of data. Cloud analytics often use AI, Machine learning, and Deep learning to give predictive models for businesses to implement and make data-driven decisions. These various elements of data analytics are implemented to provide companies with new informed insights on the data and improve business performance.
Types of Cloud Analytics
There are three types of cloud analytics stated below;
- Private Cloud
Private clouds are clouds used by a single organization or entity, usually extending the organization's IT facilities. Organizations or bodies use private clouds that place their data privacy and security as a top priority.
- Public Cloud
Public clouds are where data storage, data processing, and data accessibility is open to organizations that share IT systems but not data. This is cheaper compared to the private clouds.
- Hybrid Cloud
A hybrid cloud is a merge of both the private and public clouds. They are used by organizations or bodies with sensitive data that needs to be kept in a private cloud and have not so sensitive data that can be held in the public clouds.
Benefits of Cloud Analytics
Big organizations often deal with a large dataset from different sources, and it can be challenging to keep track of them all. Cloud analytics can store all the data received in a data warehouse and run data analytics to give insights into the data and create prediction models for the organization to make data-driven decision-making.
Data can be easily accessed by members of the organization and its stakeholders anywhere and anytime. Also, a restriction can be placed so only the right people can access the data.
Due to the enormous amount of data some organizations interact with (sends and receive), it is cheaper to subscribe to a cloud service than purchase new hardware. Also, processed data can be accessed in real-time.
Global organizations depend on cloud analytics for easy access and sharing of data with their branches or partners across the globe. Collaboration among employees can be done in real-time when they view analytics done in the cloud.
Drawbacks of Cloud Analytics
An organization that stores and accesses its data solely from cloud services are at risk of being affected if the cloud service provider experiences downtime. A solution to this is the hybrid approach.
Data security is an issue in cloud analytics as data loss can occur either from data migration to the cloud or unskilled cloud architects and developers. Though this is a decreasing worry as many operations of cloud analytics are becoming more automated, requiring less expertise.
Cost can be a factor, especially when an organization underestimates its data consumption requiring unexpected costs to cover for data usage overlap. Also, the cost of employing a cloud expert is on the high side as there is a shortage in that field.
Conclusion
Organizations and businesses realize that they need data analytics to make better decisions. The data being created increases exponentially with innovations like IoT, and there is more demand for data storage, processing, analysis, and accessibility by organizations, both big and small. Cloud analytics is the service software that performs all aspects of data analysis leveraging technologies like AI and machine learning to give insights on data to organizations and also produce predictive models. Data can be accessed in real-time by members of an organization, and cloud analytics gives organizations the ability to be more data-efficient and improve on products and services