Cloud analytics – The future of anywhere analytics

What is cloud analytics

Cloud data analytics is ingesting, processing, and analyzing large data volumes at scale using cloud computing. By leveraging public or private cloud resources and cloud data analytics technologies, companies can identify patterns from data and extract insights to enable better business decisions, improve customer experiences, drive competitive advantage, or identify the need to innovate better products and solutions.

Benefits of Cloud Analytics

Cloud data analytics is more efficient than on-prem analytics. Where on-prem analytics would need you to maintain large data centers at high costs, cloud analytics gives you multiple benefits. The benefits include more agile processes, a centralized dashboard view of data, cloud-based workflows, easy sharing of data, better data auditing, more data control, improved data recovery, and reduced costs due to subscription-based usage models.

Cloud based analytics – the future of Analytics

Azure Cloud analytics is a modern cloud analytics tool that exemplifies the power of data analytics in the cloud. It offers massive scalability when compared to on-prem platforms. Computing resources can be added or removed based on business demand; using services such as Azure Synapse Analytics and Azure Stream Analytics, faster ingestion and analysis of large data volumes can be achieved. One of the more important aspects which cannot be ignored is Azure’s cost-effectiveness; with tools such as Azure Advisor and Azure Cost Management, IT staff can quickly reduce costs by optimally using Azure resources. With its anytime, anywhere analytics capabilities, Azure powers up team collaboration for data analysis and decision-making. It has tools for data visualization, reporting, dashboards, and integration options to connect with many data sources and data lakes. Azure can identify patterns in data and extract insights using its AI-ML capabilities; with tools such as Power BI and Azure Databricks, businesses can interpret and visualize data to drive faster decision making. Azure has identity and access management features to authenticate and authorize user access to data. The encryption options ensure confidentiality for data in motion or at rest. Security staff can leverage Azure Security Center for its automated threat detection and remediation capabilities and meet data compliance standards such as ISO 27001, SOC 2, and HIPAA.

How does Data Analysis on Cloud works

Azure’s architecture is comprehensive, end-to-end, flexible, and scalable for data processing and analysis. Here at some of the key components of its architecture.

Azure has very powerful features, here are some of them. The processing of large data volumes is taken care by Azure Data Lake. This acts like a central repository. For real-time data ingestion and to derive insights from data, Azure Event Hubs and Azure Stream Analytics can be used. Creation of data pipelines can be done using Azure Data Factory. Data Analysis and Visualization is handled by Azure Databricks and Power BI. Azure Data Factory is meant for creating data pipelines. These data pipelines can be leveraged for moving data, processing, and transforming it. Azure Databricks & Power BI helps in data analysis and visualization. For processing big data workload, Azure HDInsight can be used. Azure Synapse Analytics can be used for data integration and big data analytics capabilities. Azure Analysis is helpful for business intelligence in creating data models and reports.

Azure Machine Learning is another key tool that enables the creation and deployment of ML models through a drag-and-drop interface. More importantly, Azure offers a unified data governance solution, Microsoft Purview, to govern on-prem, multi-cloud, and SaaS data.

Reasons why businesses needs to adopt Cloud data analytics

As seen, the power of Azure analytics offers solutions to store and analyze large data sets with top-notch accuracy and performance to offer critical insights for business faster and at lower costs. It provides automated data orchestration from disparate sources and extensive drill-down options for deeper analysis. It also requires consideration of several factors during implementation. By partnering with an Azure Gold Partner, companies can overcome several challenges.

Here are a few factors to be considered while implementing Azure cloud analytics:

Data Sources: Identify the data sources to populate the analytics environment, where it will be stored, and how the data will be accessed and transformed.

Data Quality: completeness, accuracy and consistency of data is a must. Ideally, it must be cleansed of any inaccuracies or discrepancies.

Data Storage: architecture must meet performance and scalability requirements by selecting the right storage type, including blob, file, or table storage and data structure optimization.

Data Processing: the right data processing technology based on the analytics infrastructure being built; this can be Azure Data Factory, Azure Databricks, Azure Stream Analytics to process and transform data.

Analytics Tools: the right cloud data analytics tools based on their ability to handle the volume, velocity, and variety of data; the choice can be made from Azure Synapse Analytics, Azure Analysis Services, or Power BI.

Security: security is very critical while implementing Azure cloud analytics. There must be data access controls to ensure sensitive data is stored and transmitted securely.

Governance: Governance is a critical criterion for compliance. This includes institutionalizing security policies and procedures to manage data, establish role-based access, and meet regulatory needs.

Grow and scale your business with Cloud Analytics Services

With its immense power and flexibility, an interface that is sensitive and intuitive, Azure analytics cloud platform is broadly capable and depends on how its deployed; here are a few capabilities and some of its core uses across a few industry segments.

Financial services: analytics is highly potent in detecting fraud and prevention. It can also be used for credit risk assessment, anti-money laundering, and compliance. By processing real-time data of financial transactions, Azure stream analytics can detect malicious patterns indicating counterfeit activities.

Manufacturing and transportation: it predictive maintenance, optimizing supply chains, or asset tracking, Azure’s machine learning capabilities can predict machine failure, schedule maintenance before breakdowns, and increase productivity.

Media & Entertainment: segmentation, content recommendation, and optimizing ads are some areas where Azure can be used. Audience behaviors and preferences can be analyzed using Azure Cognitive Services to offer personalized content and targeted ads.

Networking & Security: traffic analysis and threat detection capabilities can be enhanced using Azure Network Watcher to monitor & diagnose network issues and Azure Sentinel for detecting and responding to complex threats.

Education: Machine Learning personalizes learning experiences by assessing student data on learning styles and performance. Data from multiple sources such as LMS, student information systems, and social media can be integrated using Azure Data Factory to gain insight into their behaviors and preferences. Azure Stream Analytics can help in student activity monitoring and analysis, and the insights can be used to identify improvement areas.

Top tools for anywhere data analytics

Here are a few choices of cloud business analytics tools, other than Azure, which can be chosen based on the business need and requirement.

Tableau: Available both as a on-prem and cloud versions, this tool has good data visualization and business intelligence features which can empower users to create interactive dashboards and reports.

Google Data Studio: cloud based tool which can integrate wide range of data sources such as google analytics, google ads and google sheets and is widely used to create reports and dashboards.

Amazon QuickSight: popular tool for business intelligence and analytics it allows users to integrate data sources to develop reports and dashboards. It can also be easily integrated with AWS products and services.

Looker: A cloud based tool where users get a centralized view of data and enables them to create dashboards, explore data, create reports and collaborate with other teams.

SAP Analytics Cloud: A cloud-based analytics platform which has a plethora of data visualization and business intelligence capabilities. This platform integrates well with other SAP data sources to enable users to create interactive dashboards and reports.

Domo: cloud based intelligence platform has features such as data integration, data visualization and data collaboration features. It is can create create interactive dashboards and reports.

Why Choose Ness as you Cloud Analytics Solutions Provider

Ness is an obvious choice for an organization’s cloud analytics needs. It is one of the few cloud analytics companies having proven expertise, technology partnerships, experienced resources, and a agile delivery approach. Be it data strategy, Data reorchestration, Data platform modernization, in the development of customized AI-ML data analytics solutions, Ness can unlock the the value of your data ecosystem to open up new opportunities of growth to sustain your competitive edge. Here are a few facts why Ness should be in the top of the list while considering a cloud analytics service provider.

Expertise: has a global talent pool of cloud analytics experts with wide experience in designing and developing cloud analytics solutions. The diversity of resources include data engineers, data scientists and data analysts who can develop and implement complex data pipelines, and implement advanced analytics to derive insights.

Technology partnerships: has top tier partnerships with Microsoft and Amazon Web Services, which enables in the leverage of high end cloud analytics solutions to help client migrate to cloud services such as AWS Redshift and Azure Synapse Analytics.

Agile delivery approach: adopts an agile delivery approach to enable clients iterate through their cloud analytics solutions. The teams are very responsive and can provide you with a POC quickly and then incrementally add new features and functionalities based on your feedback.

Customer-centricity: a customer-centric approach, Ness will work closely with you to understand your custom needs and goals. This will help us to create tailored solutions to maximize value, our services can range from data strategy and architecture to data governance and data visualization.


What is the difference between cloud analytics and data analytics?

There are distinct but are related. Data analytics can be termed as a practice of analyzing data to extract insights for decision-making. Cloud analytics can be referred to as using cloud technologies to perform data analytics.

What is modern cloud analytics?

Modern cloud analytics uses cloud technologies to perform data analytics tasks that are impossible using traditional on-premise solutions.

What are the three most common types of cloud service models?

The three common cloud service models are infrastructure-as-a-service, platform-as-a-service, and software-as-a-service.

What are examples of cloud analytics?

Cloud analytics provide various tools and services to get insights. Examples include cloud analytics, BI tools to create interactive dashboards, data warehouses to store data, big data analytics to analyze large data volumes, machine learning services to automate tasks, and real-time analytics to monitor data in real time and make better decisions. Some of the top platforms widely used include Qlik Sense Enterprise, Board, Tableau, IBM Cognos, & TIBCO Spotfire X.