Best Cloud Analytics Solutions in 2024

What are Cloud Analytics Solutions?

As companies collect big data from multiple digital applications, their analysis and visualization become easy with the best cloud-based analytics tools. These applications have contributed to making business intelligence an accessible solution for enterprises of all types. After all, it is not just data visualization that you can perform using cloud analytics tools but also data synchronisation, data augmentation as well as interactive analytics.

All such data is easily shareable and you can filter it always for custom viewing.

Types of Cloud Analytics Solutions

Cloud analytics helps businesses scale their processes thereby reducing the costs of operating on-premise hardware solutions. This is one of the major reasons why cloud-based analytics are so important for organizations. Cloud analytics software as a service model is of the following types.

1. Hybrid cloud- Hybrid cloud combines private as well as public cloud together. The model works most effectively when an only a small quantity of data is available in the private cloud.
2. Public cloud- Cloud analytics as a service model stores and processes public data. Using a multi-tenant architecture, this data is available to those sharing IT systems.
3. Private cloud- Price cloud functions as the extension of an organisation's IT infrastructure. It is most convenient when the privacy of the data is of utmost importance.

Features of Cloud Analytics Solutions

Keep your business running fast and smooth with analytics cloud core support features. The best part about using these solutions is they adapt easily to a fast-growing business while at the same time providing reliable infrastructure for data security. Let us go through similar such features of cloud analytics solutions to understand their functionality better.

• Conversational analytics

With capabilities like natural language generation and processing, cloud analytics give immediate answers to queries in the form of data explanations/visualizations. Built-in AI-analytics makes these applications easily accessible for all as with little training, even those with not much experience can use cloud-based analytics software with ease.

• Automated algorithms

Automated algorithms for machine learning help generate outliers and patterns in a data thus providing automated services for machine learning. Inbuilt predictive analytics are further helpful for forecasting and predicting upcoming outcomes at the snap of a finger.

• Data modelling & visualization

You can conveniently use cloud analytics for drilling and filtering data for accurate data visualizations. The size of the data doesn't matter as the application is capable of processing large data sets. This equally works for efficient data modeling and cleansing too besides generating deep business insights.

• Collaborative predictive planning

The ability of data analytics to produce key business insights are mostly used for collaborative purposes. This feature along with the platform's machine learning functionality helps companies do predictive forecasting, strategic alignment and undertake informed decision making.

• Financial planning

It is because of the solution's advanced analytics that combining transactions is easy for effective financial planning. Just from a single cloud interface, it is possible to undertake data-based budgeting, analysis and planning.

• Rapidot protyping

Cloud analytics offer business intelligence and analytical planning for more customized interactions. You can also customize templates, content, etc. with the available data models using the rapid prototyping feature.

• Custom widgets

In cloud analytics business software are available tailored widgets with data science abilities and composites. These widgets can be always adjusted hassle free with support from the application’s reusable and continuous user interface.

• BW connectivity

Hierarchies in data help with easy data management. The custom grouping module functions well for developing custom hierarchies for effective data management. You can later on use this information for other models, charts and stories. Custom ordering is critical for understanding data the right way.

• Embedded analytics

Use cloud analytics for leveraging APIs for authenticating and adding analytics to the daily work. Once embedded, you can either publish it through a webpage or create interactions using chatbots. You can even use this feature for embedding analytics into another application.

• Robust NLP

Powerful natural language processing (NLP) is what you can definitely expect from the available best cloud analytics tools. NLP helps with powering text and speeches for conversational analysis and search. You can also expect some graph recommendations for enriching your data.

Industries that Primarily use Cloud Analytics Solutions

Based on the type of cloud analytics, the software is deployed in multiple ways as a service model. These are important for not just saving company costs but also improving a business’s performance. Some of the prominent industries that deploy these solutions are:

• Healthcare
• Life sciences
• Consumer goods
• Education/ research
• IT industry
• Telecom
• Energy sector
• Entertainment industry
• Manufacturing units

In short, cloud analytics tools are used by such industry verticals planning to undertake any of the following activities.

• Companies looking for solutions that may help serve the functioning of platform as a service.
• Organizations planning on scaling their operations the fast way.
• Businesses that opt for storing some of their data in cloud storage while keeping the rest on premise. Usually the hybrid model gets deployed in such a scenario.

Why Cloud Analytics Architecture is Important for Business Growth & Scalability

Cloud analytics are best used to enhance the capability and accessibility of doing complex analysis for large sets of data. These are important to businesses of all types for many reasons. Let us go through some of those.

• Cloud based analytics services can be easily deployed for these are available in automated form. Plus, they don't require any special maintenance.
• Services so offered by cloud analytics can be turned on/off as required. Thus, end users only need to pay for what they have used so far. Further, the space needed for data centres is also minimal.
• For any kind of project involving data analysis, you are free to scale storage and compute as per the business requirements. There is no need to procure any particular amount of IT equipment in physical form in cloud analytics as a service model.
• Hybrid analytics software as a service model is best for leveraging the cloud for testing new analytics.
• Identifying patterns within videos, speech and images for improving both customer service and satisfaction.
• Analyzing buying behaviour for improving product delivery services.
• In scientific field, cloud analytics tools are best for testing genomic data and disease reporting patterns for finding new cures as well as medicines.
• Analyzing cloud infrastructure for optimizing IT costs and boosting the performance of an application.

How do Cloud Analytics Solutions Work?

Cloud analytics operates using a software-based system hosted on an internet platform. The entire system therefore works on a server within the data center. The larger a company is, the more powerful its server is for supporting cloud-based data analytics platform. Such data can be stored or retrieved as and when required. Enlisted here are the sources through which cloud-based analytics tools work.

• Data source: Data source may cover either of the two- usage data of website or social media platform’s analytics.
• Data model: Structured data types are being used here for relating standardized data points.
• Processing applications: Using a data warehouse for processing large sets of data.
• Computing power: Computing power for analyzing and structuring data available at scale.
• Analytic model: Closed function based mathematical model for prediction of outcomes.
• Data storage/sharing: Data warehouses for quick scaling of the data.

Benefits of Using Cloud Analytics Solutions

Cloud analytics tools offer multiple features like data storage, analysis and ingestion. Along with such features, they also offer numerous benefits in the form of data visualization and business intelligence. Some other key benefits offered by the platform are:

• Flexibility for easy scaling

A fast-growing business needs to be scalable enough to stay ahead of the competition. Cloud analytics tools are important here because they offer businesses the intelligence required for getting access to data in real-time. All these processes are highly flexible and cost saving.

• Warehouse for data consolidation

Dara warehouse of cloud-based analytics solutions help with the consolidation of data. Such data can be consolidated from any type of source and processing this is also easy. Consolidated data is important for not just data mining but also developing prediction models in real-time.

• Easily scalable & accessible

The data processed by cloud analytics solutions help greatly the companies planning to scale their operations. Also, end users can use these as and when required so there is no hassle of compulsorily procuring it just for the sake of it. Such a data is collaborative in nature too as it can be shared with people across borders.

• Better data security

Cloud based analytics platform offer secured data access points for improving the data governance mechanism. Access is also available for undertaking granular controls for both auditing as well as identifying sources that have access to the data.

• Agile resources for computing

Servers operating on-premise quite often suffer from delivery and speed related issues. But this isn't so with analytics-based software. All of the platform's high-powered functions help with timely delivery of reports and queries.

• Reduced data latency

Cloud services in data analytics are best for not just handling well response related issues but also reducing data latency. These are further helpful for managing emergency and BI queries in real-time. Companies also use this feature for scaling up their operations as well as efficiently managing the data warehouse.

• Effortless matching & cleaning of data

Cloud analytics help organize the data in such a way that there is little to no possibility of data mismatch or delay. Such data is always updated so that there are predictable outcomes as well as quick decision making.

• Fast adaption

The interface of cloud analytics is consumer friendly as a result of which they have fast and flexible adaption capabilities. It is for this reason that organizations are able to scale their operations using these applications and adjust their data storage capacity as per the business's requirements.

Some Of The Best Cloud Analytics Tools

Microsoft PowerPoint BI

Key features:

• Prebuilt reports/dashboards
• Custom visualization and with NLP (natural language processing)
• Secured data governance
• Collaboration with embedded analytics

Qlik

Key features of Qlik:

• Inbuilt tools for data integration and preparation
• Real-time reporting and analytics
• Smart feature for quick searching
• Functionality for data storytelling

Oracle Analytics Cloud

Key features of Oracle Analytics Cloud:

• Data recovery
• Analytical dashboard
• Integrated tools for data preparation
• Embedded and enterprise analytics
• Intuitive dashboard

IBM Cognos Analytics

Key features of IBM Cognos Analytics:

• Automatic data modelling
• Data governance and management
• NLP based AI
• Inbuilt management features for data handling