In this virtual expanse, data is no longer a static entity confined to silos. Data is a dynamic force that is brimming with insights waiting to be discovered. Cloud analytics plays a pivotal role in driving business innovation as it transcends traditional data management by providing scalable, efficient and accessible solutions for data analysis.
What is cloud analytics?
Cloud Analytics is the process of performing data analysis, aggregation, storage, integration and reporting on remote computing resources called the cloud. These remote servers are accessible over the internet, publicly or privately. With cloud analytics, enterprises can use advanced analytical tools and Business Intelligence tools for analyzing data on-demand, on the cloud and predicting important trends, patterns while understanding performance.
The integration of online and offline customer journeys has led to a surge in the volume and complexity of data that businesses deal with every day. Data increasing at such an enormous scale is required to reach out to the target market, manage the supply chain and generate qualified leads. It becomes difficult and costly to manage such massive volumes of data using traditional infrastructure within the on-premise data lakes. Thus, the cloud has become the new normal for data enthusiasts to cater to their storage and analysis needs.
Cloud Analytics Benefits
Cloud Analytics is preferred by enterprises for the many benefits that it has to offer:
Scalability: Cloud provides massive scalability in terms of both data and users. Businesses can increase their cloud data storage and enhance their cloud analytics capabilities as per requirement. Thus, enterprises can easily and efficiently scale the data storage and processing to reflect the ever-changing market conditions.
Agility: Cloud data analytics lets enterprises approach their data in a holistic and unified manner. Companies can access several types of data from diverse sources. This helps decision makers to access cloud resources quickly and easily to capture insights.
Cost-effectiveness: A fee is charged based on subscription or data consumption volume and queries. Thus, the overall infrastructure costs are reduced by paying only for the resources being used.
Cross-organizational Integration: Cloud analytics makes it possible for businesses to access data across the organization. This can help data users to generate and share insights on-demand. It also aids collaboration and organizes communication.
Faster Insights: Cloud analytics makes it possible to generate insights faster than traditional data lakes, making the decision process faster.
Security and governance: With cloud analytics platform Businesses can achieve single and secure access to data. It also provides granular access controls for knowing who has access to what kind of data. Thus, making governance easier.
Remote access: Cloud analytics platforms make it convenient for teams to access their data remotely. This helps in reducing any needs for relocating data to different analytical environments.
All these benefits make cloud analytics a more viable option for the new age enterprises.
What is a cloud analytics platform?
Cloud analytics leverages cloud computing infrastructure to offer scalable, efficient and flexible data analysis capabilities. This dynamic tool has transformed the way organizations manage, process and research vast volumes of data. Cloud analytics aggregates data from sources such as cloud data warehouses and lakes to organize a single-source-of-truth. Special applications are employed to clean organize, process and analyze the data using proprietary algorithms developed with artificial intelligence (AI) and machine learning.
Types of cloud analytics:
Public cloud: Available for everyone over the public internet, public cloud services are operated by third parties.
Private cloud: Computing services are accessible only within a private network for greater control and security.
Hybrid cloud: Combining private and public clouds, hybrid clouds use proprietary software for communication between services.
Public clouds offer cost efficiency and scalability while private clouds provide improved security and control sensitive business data. Hybrid clouds provide the best of both worlds, catering to the needs of businesses that require flexibility without compromising on security. At the intersection of technology and business, cloud analytics platforms stand as pillars of modern decision-making. They are catalysts for innovation for businesses to unlock the potential of their data.
How does a Cloud Analytics Platform Works?
Cloud analytics platforms leverage the power of cloud computing to offer comprehensive and accelerated insights. These platforms are designed to handle vast amounts of data, sourced from various points and process it in a cloud-based environment. When the aggregated data is then stored securely in the cloud, the platform offers robust and scalable solutions for data management.
Advanced algorithms and machine learning techniques work in unison to extract valuable insights and patterns from the available dataset. However, these insights and data patterns need to be presented in easily understandable formats such as graphs, charts and dashboards for simplified pattern recognition. Location-agnostic access facilitates remote work and data management across separate locations. This is especially beneficial for businesses operating in multiple regions or workforces with flexible work hours.
The scalability of cloud analytics platforms cannot be overstated. They can handle increasing volumes and more complex processes without substantial infrastructure investments. Cloud analytics platforms are a comprehensive solution for data aggregation, management, processing and visualization for deriving data-driven conclusions and strategic planning.
How to choose the best cloud analytics platform for your business?
The ideal cloud analytics platform transcends a mere checklist of features. It stitches together various elements that cater to unique business needs.
Need assessment: Discussion around the core business requirements should involve every stakeholder. It is important to refrain from implementing features that look futuristic but are not needed. Deductive analysis should be conducted to arrive at the most prominent features.
Advanced analytics capabilities: Advanced analytics, including prescriptive and cognitive analytics capabilities are important considerations. Evolving technologies like machine learning and advanced algorithms should be the center of the discussion.
Integration with existing systems: Most companies already have a data ecosystem in place. The platform should integrate seamlessly with existing tools and systems for minimal disruption to current workflow. Scalability: The flexibility to scale up or down based on data needs is crucial for managing varying data volumes and maintaining cost efficiency. Some cloud analytics platforms like Kyvos offer a scale-out architecture for efficient handling of large data volumes and supporting concurrent user access without performance loss. This architecture allows for interactive big data analytics across various enterprise sectors.
User experience: An intuitive interface that chalks up a clear path towards actionable insights. The availability of clear documentation and support through advanced features is a huge plus.
Remote collaboration support: The world is moving towards a hybrid work environment. Hence, the platform should offer location-agnostic collaboration capabilities with remote access to analytics.
Cost and ROI: Cost should not be given undue precedence. It should be weighed with a broader lens to include implementation costs, operational costs and long-term ROI. Many businesses opt for short-term contracts (12 to 24 months) with cloud analytics platforms. This allows for better negotiation terms at renewal and alignment with rapid technological advancements.
Trial options: Before investing in a paid solution, free trials can be a viable option to compare features and usability. Some platforms also offer a demo to highlight their capabilities.
Vendor reputation and support: Consider the vendor’s reputation regarding customer support and their track record for innovation.
How Cloud Analytics Can Help Drive Growth and Scalability
The global cloud analytics market size is projected to grow $86.15 billion by 2028 at a CAGR of 20.9%. Cloud analytics is experiencing this growth as industries such as BFSI, IT & Telecommunications, Retail and Consumer Goods, Healthcare and Life Sciences, Manufacturing, Research & Education, etc. adopt the technology. More than 94% of organizations with more than 1,000 employees have a lot of their workloads in the cloud and this trend is expected to continue growing.
This growth is attributed to cloud analytics’ ability to drive business growth and scalability. The technology enables organizations to process, analyze and interpret large volumes of data quickly and efficiently. It allows businesses to scale their computing resources up or down based on their needs to accommodate sudden increases in data processing requirements. Cloud analytics can quickly adapt to workload changes without significant upfront investments.
Providers offer high-performance cloud computing solutions, which are crucial for complex analytical tasks. These solutions enable businesses to make real-time decisions based on the most current information available. Cloud analytics can lead to cost savings as businesses can allocate their capital where it’s most needed while still having the ability to grow. This can help businesses optimize their resources and reduce overall expenses. Cloud-based analytics platforms offer data protection, scalability and flexibility, which can help businesses maintain the security and integrity of their data.
Use Case examples of cloud analytics platform
Industry-wide applications: Cloud analytics has been successfully implemented across various industries, demonstrating its transformative potential. In certain industries, the technology has been adopted on broad levels.
Financial services: Financial institutions use cloud analytics platforms for applications like customer analytics, risk assessment, fraud detection and security threat detection. Financial institutions and service providers are increasingly adopting predictive analytics and AI/machine learning. These new-age algorithms increase efficiency by identifying complex patterns in financial data. They also automate routine tasks, freeing up analysts to focus on complex analyses.
Healthcare: The healthcare industry uses cloud analytics platforms to provide personalized treatment, patient admissions prediction and practice process optimization for better management. The industry is also using big data and analytics usage for patient segmentation and clinical research optimization.
Telecommunications: Cloud analytics platforms are capable of analyzing multi-source IoT device data to ensure regulatory compliance and maintain high service quality standards. They also optimize network performance, detect and prevent fraud, predict customer churn and facilitate targeted marketing through customer segmentation.
Supply chain management: Real-time and accurate demand forecasting in the dynamic business environment requires big data analysis for supply chain planning and management.
Digital marketing: Cloud analytics enables data migration and visualization for digital marketing performance for development and analysis of digital campaigns.
Real-world case studies
Along with these industry-wide applications, various companies have leveraged cloud analytics platforms across different sectors for operational efficiency and strategic planning.
Music streaming service: A globally popular music streaming platform utilized cloud analytics to monitor user preferences and deliver personalized music recommendations. This led to improved user engagement and retention rates.
Online grocery retailer: This multinational grocery chain employed machine learning tools to optimize operations and customer service. Real-time data analysis allowed effective demand prediction, inventory management and delivery efficiency.
Social media platform: A leading social media platform leveraged cloud analytics for real-time monitoring of trending topics, detecting spam or abusive content and recommending relevant accounts to users.
Consumer goods company: The consumer goods conglomerate harnessed cloud analytics platforms to gain insights into market trends and consumer behaviors. Analyzing data from social media conversations, product reviews and sales data helped them make informed decisions regarding product launches and marketing strategies.
Manufacturing: A European manufacturing company used a cloud analytics platform for fraud detection in employee expense claims and expenses to ensure the transparency and integrity of its financial processes.
Upcoming trends in cloud analytics
The fusion of Artificial Intelligence (AI) and cloud computing provides necessary resources for AI and machine learning applications. IoT is the new face of the internet for communication and cloud security protocols. The evolution in cloud analytics for efficiency, security and the integration of advanced technologies is very promising.
The shift towards hybrid work models is driving demand for cloud-based tools for location-agnostic collaboration and productivity. Multi-cloud and hybrid cloud solutions in data-driven industries have increased the focus on cloud security and resilience along with the use of managed security services and AI for threat detection. Cloud computing also plays a significant role in disaster recovery with system restoration during emergencies.
The maturity of Everything-as-a-Service (XaaS) is driving cloud investments towards flexible and scalable internet-based services. The development of sovereign and industry-specific clouds helps with data protection for industry-specific needs.
Cloud gaming is making its way into the mainstream with high-speed internet technologies like 5G. Kubernetes and serverless computing are gaining traction by offering scalable and efficient cloud application management. Blockchain’s integration with cloud solutions improves security and transparency for various applications.
Know Kyvos as a Cloud Analytics Platform
Kyvos is a future-ready cloud analytics platform known for its ability to manage multidimensional analysis and large data sets. The right cloud analytics platform, like Kyvos, is capable of adapting to future technological advancements and changes in business requirements. Kyvos’ analytical data warehouse is built for fast, secure and cost-effective data discovery while controlling querying costs with a pay-as-you-go model. The semantic layer empowers users to perform multidimensional analytics on billions of data points.
With its revolutionary AI-powered smart integration technology, Kyvos leverages machine learning and advanced algorithms to create massively scalable data models for unmatched performance on modern data platforms. Built-in data science and machine learning capabilities accelerate the company’s BI experience to analyze historical data or live streaming data for insightful decisions. While accessing Kyvos on data lakes and warehouses, Kyvos democratizes data querying with GenAI features to foster natural language conversations. With such a comprehensive bouquet of features, Kyvos can fit into any company’s cloud analytics needs.Ā« Back to Glossary