Cloud Analytics

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 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-premises 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: The holistic and unified approach to data offered by cloud analytics allows organizations to 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. Convenient access to remote data helps in reducing any needs for relocating data to different analytical environments.

Cross-organizational insights: The ability to access data across the organization can help data users to generate and share insights on-demand. It also aids collaboration and organizes communication and 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.

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 Computing Models

There are three different types of cloud computing models: public, private and hybrid clouds, each with its unique characteristics, benefits and drawbacks.

Public cloud: In this model, cloud service providers own and maintain all computing resources, such as servers, applications and storage and make them available to the public over the Internet. Users can access their cloud accounts through a web browser without worrying about storage, maintenance and server issues or keeping the hardware cost in check. Public clouds are scalable, flexible and cost-efficient. Users only have to pay for what resources they consume and can host their web applications or develop test applications without the need for on-premises infrastructure.

Private cloud: Organizations with sensitive or confidential data usually use this deployment model as it provides higher security than public clouds due to their isolated nature. It is a secure cloud environment that offers privacy and is owned and operated by a single organization. If organizations choose this deployment model they have control over hardware, software and network configuration to meet specific industry regulations and compliance standards. Unlike public clouds, it is not as easily scalable, may require ongoing infrastructure management and can be expensive in terms of upfront costs.

Hybrid cloud: Hybrid clouds combine the elements of public and private clouds and offer a unified and flexible computing environment for organizations to deploy workloads and data according to their needs. Hybrid clouds provide the best of both worlds, catering to the needs of businesses that require flexibility without compromising on security. Organizations have a choice to migrate and integrate data and applications between public and private cloud environments. These environments ensure data security and compliance, improve agility and optimize cloud spending.

These three cloud platform deployment options stand as pillars of modern infrastructure. Organizations can choose the most suitable option among the three 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. 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 crippling 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. Before choosing any cloud analytics platform, organizations should have a discussion with respective stakeholders about the core business requirements. It is important to refrain from implementing features that look futuristic but are not needed. Some of the most important features that organizations should seek in a cloud analytics platform are:

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.

User experience: The platform should be user-friendly and intuitive to facilitate non-technical users. 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.

Vendor reputation and support: Consider the vendor’s reputation and experience in the cloud analytics market and research regarding customer support services 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.

Example Use Cases of Cloud Analytics Platform

Cloud analytics provides actionable insights from vast datasets across a broad spectrum of industries. From finance to healthcare and beyond, organizations are harnessing the capabilities of analytics on cloud-based platforms to unlock new opportunities, drive innovation, optimize operations, improve decision-making, enhance overall performance and achieve significant competitive advantages. Here are some use cases of cloud analytics platform:

Financial services: Financial institutions use cloud analytics platforms for applications like customer analytics, risk assessment, fraud detection and security threat detection. These 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 analysis.

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: Telecom industry uses cloud analytics to optimize network performance in real-time for identifying anomalies that impacts service quality, detecting and preventing fraud, predicting customer churn and facilitating targeted marketing through customer segmentation.

Supply chain management: Real-time and accurate demand forecasting in dynamic business environments requires big data analysis for supply chain planning and management. Cloud analytics can help businesses examine factors such as historical data, sales trends, economic indicators, weather patterns and much more to help them optimize inventory levels, reduce supply chain disruptions and improve planning.

Digital marketing: Cloud analytics enables visualization for digital marketing performance for development and analysis of digital campaigns. Using cloud analytics, marketers can track key metrics such as website traffic, conversion rates, customer acquisition costs and return of investments (ROI) to target the right audience and allocate resources efficiently.

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.

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 a cloud-based analytics platform 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 cloud analytics for fraud detection in employee expense claims and expenses to ensure the transparency and integrity of its financial processes.

Cloud Analytics Challenges and How Kyvos help Organizations to Overcome Them

Cloud analytics offers several benefits but also presents numerous challenges like ballooning costs and performance degradation as volume and concurrency increases and compromised data security and governance. Cloud costs escalate due to the varying workloads and resource requirements. Performance degradation in cloud can be the result of increased data volumes and user concurrency that can strain the performance of cloud analytics platforms, leading to slower response times. Additionally, without a robust security framework in place, organizations face challenges in protecting sensitive data from unauthorized access, breaches and other security threats.

To solve these challenges, organizations need an innovative solution like Kyvos, a Gen AI-powered semantic layer platform that can handle any scale or complexity of data to meet the growing analytical needs of an enterprise. With its revolutionary AI-powered smart aggregation technology, Kyvos leverages machine learning and advanced algorithms to create massively scalable data models for unmatched performance on modern data platforms. Let’s see how Kyvos help organizations to overcome cloud analytics challenges:

Optimizing cloud costs: Enterprises often need to maintain separate cloud data warehouses to store raw and processed data. Storing the same data in 2 different locations increases storage costs. Also, as data volumes increase, analyzing this massive amount of data requires complex resource-intensive queries. If analytical tools do all the heavy processing at runtime, it can substantially increase cloud resource consumption and analytics costs.

Kyvos helps enterprises reduce costs as it eliminates the need to maintain separate data warehouses and instead offers built-in analytical warehouse that can store both raw and aggregated data, while improving analytical speed. Kyvos further reduces costs with its price-performant querying that processes all possible calculations in advance and creates data models so that minimal resources are consumed per query. This enables users to fire unlimited queries without incurring additional costs.

Enhance performance: With organizations accumulating larger and complex data sets, cloud-based data analysis becomes resource-intensive and impact performance. The challenge becomes more pronounced when multiple users attempt to access and analyze this data simultaneously using complex queries. These queries may involve multiple joins, aggregations, filters and require significant processing resources that can strain resources and lead to longer execution times.

Kyvos addresses this challenge using its ML-powered smart recommendation engine that brings in intelligence needed to create smart aggregates and multi-level caching with AI-driven self-tuning that can manage large data volumes and query loads. It profiles data and studies query patterns to provide intelligent recommendations that simplify the data modeling process and enable users to optimize their designs without needing technical expertise. Together with load-balancing, Kyvos enables sub-second query responses on large datasets for thousands of concurrent users with no performance degradation.

Strengthening data security and governance: Different business units of the same enterprise sometimes use multiple BI tools to analyze data. This can cause data to become scattered across various data sources and get externally cached in BI tools, increasing data movement and creating silos. As a result, enterprises face security and governance challenges as they fail to prevent unauthorized data access in such situations from the users inside the firewall, nor can they ensure data quality and integrity. This challenge can be overcome by introducing semantic layers to their data architecture.

Kyvos’ semantic layer offers a centralized point for data management, security and analysis. It provides a three-tiered security architecture with row and column-level security at the group and user levels. It allows organizations to set up role-based access control to assign user privileges as per their authorizations. The platform has achieved globally recognized certifications such as SOC 2 Type I certification, SOC 2 Type II certification and ISO 27001 for a well-documented security program to meet the highest industry standards for data security, confidentiality, availability and privacy. Additionally, Kyvos’ AuthKey helps unlock data securely with data auditing, monitoring, versioning and lineage tracking.

Final Thoughts

Cloud analytics has revolutionized how businesses interact with and analyze data, offering scalable, agile and cost-effective solutions that empower modern decision-making. As the need for cloud analytics continues to grow, organizations that embrace it will be better positioned to scale, innovate and maintain a competitive edge.