In today’s world, data is the new gold that helps frame winning strategies and wield customer success stories.
MicroStrategy’s insight shows that 60% of companies use data analytics to improve processes and cost-efficiency.
However, when leveraging the power of this gold, organizations must be ready to take on a two-fold challenge.
Firstly, how to store, manage, and handle constantly increasing data volumes, and secondly, how to analyze this data in its varied forms.
Interestingly, these challenges are intertwined and can be tackled as a combined problem.
By combining the power of the cloud and analytics for next-level decision-making, enterprises can kill two birds with one stone. Indeed, cloud analytics can uncover smart insights from enormous chunks of data in real time.
This article covers some critical facts about cloud analytics and encourages you to adopt it for your business.
Understanding Cloud Analytics
Cloud analytics serves as a service model that combines the capabilities of cloud computing and data analytics to deliver business intelligence solutions via the cloud.
So, if your business continuously generates data that can be leveraged for real-time decision-making, cloud analytics can prove to be a game-changer.
With cloud analytics, you can wrangle large datasets, apply appropriate analysis procedures, and glean insights to drive your business strategy and improve business results.
You can thus infer and understand patterns from humungous data volumes and pinpoint improvement areas in your business using real-time insights.
Real-world Implementation of Cloud Analytics
Cloud analytics has evolved into a foundational element of smart technologies. Machine learning algorithms, which need massive datasets, must be continuously fed with data to achieve the desired smartness.
For instance, an Indium client providing sleep technology solutions wanted to build a robust sleep monitoring system. The constant influx of data posed challenges in establishing a real-time data processing and modeling pipeline.
The key was automating the complexities associated with model training and deployment into production. Multiple systems had to be updated in real-time, and the best version of the model had to be selected.
Identifying end-to-end automation of the entire machine learning cycle as the solution, our cloud and analytics experts leveraged the power of the cloud machine learning platform, AWS Sagemaker.
The MLOps (machine learning operations) pipeline now operationalized the ML models with real-time data, improving the overall efficiency of the models.
Facilitating a two-fold reduction in model training time, AWS Sagemaker accelerated the turnaround time. The efficiency provided by the cloud became visible as the new features helped to enhance the existing features further.
The above is only one of the cloud analytics solutions we deal with. In general, there is no dearth of examples highlighting how organizations are reaping tangible benefits from cloud analytics.
For instance, the Oslo University Hospital in Norway built an integrated business intelligence system.
Using cloud-based analytics capabilities provided by Microsoft Power BI, the hospital management could monitor departmental performance using recent data.
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Virginia Tech is another splendid example of how cloud analytics can benefit essential sectors like healthcare.
To create a tailored medical treatment regime and provide personalized medication, Virginia Tech leveraged Azure. It built a customized cloud computing model using Microsoft Azure HDInsight Service.
Besides being cost-effective, the cloud-driven solution also offered supercomputing power for DNA sequencing analysis – the most crucial part of the project.
Benefits of Cloud Analytics
While we delved into some specific benefits above, in general, cloud analytics offers the following benefits.
The cloud offers an on-demand model in which resources can be scaled up or down per project requirements.
You can maintain enough servers at any instance and buttress the machine learning model to onboard any number of users without affecting the user experience and downtime.
Cost consumption is relatively less in the cloud, and the ROI is higher. You are not required to invest upfront in hardware and software, significantly reducing the cloud infrastructure cost.
Moreover, the cloud solution removes the burden of managing ongoing upgrades, leading to lower maintenance costs.
Ease of access is one of the most significant advantages offered by cloud analytics. Internal and external stakeholders can use analytics capabilities irrespective of their geographical presence.
So, cloud analytics is vital to building a robust remote and hybrid work environment.
By leveraging the power of the cloud, you eliminate data silos that various systems like DBMS – CMS, ERPs, and automation systems can cause.
A cloud data analytics solution streamlines inter-functional coordination and collaboration, and different teams can quickly get real-time insights into the desired process.
Cloud data analytics solutions automatically perform upgrades and updates at required instances.
The system remains constantly updated and can handle unexpected spikes in data traffic. As a result, the performance of the machine learning model never suffers.
Faster Decision Making
Real-time data processing and decision support set cloud analytics apart from traditional on-premise driven analytics.
Predictive models are based on real-time data, and users can build customized dashboards for accurate analytics. You can monitor the process performance and track critical activities at the click of a button.
Realize Your Data’s Worth with Cloud Analytics
There is an increasing interest in cloud solutions, and cloud analytics is the next frontier for many organizations.
Indeed, there’s a tremendous scope that cloud analytics offers. However, it would help if you considered certain factors to accomplish your goals with cloud analytics.
These could be selecting a cloud service provider (CSP) and identifying the problems you want to address with cloud analytics.
When the preliminary requirements analysis is done, you can cash in on the multiple benefits of cloud analytics.