Data Analytics and AI Solutions and Services
Collect, manage, and analyze data flows from multiple sources and ecosystems. Deploy advanced data ingestion, processing, and security engines. Embrace cutting-edge Artificial Intelligence (AI) to generate business-relevant insights for informed decision-making
The Data Intelligent Enterprise to Survive and Thrive
Does this require any more persuasion post pandemic?
Cloud, Big Data, AI, and IoT: Often touted as the fundamental pillars of the Fourth Industrial Revolution, a multi-impact phase described as more revolutionary and larger than the last three. However, if we lean closer, what seems like four different technologies on the surface form part of a larger whole.
Modern enterprises are fast expanding into diversified endpoint ecosystems: mobiles, user PCs and laptops, smartwatches and wearable devices, biometric devices, sensors, and more. Gargantuan amounts of data flow from these endpoint ecosystems to the centralized Cloud IT ecosystems where the same is stored and processed with cutting-edge analytical solutions. AI solutions run on top to generate smart business insights, automate key processes, and lay the groundwork for further innovations, affecting the firm’s endpoint footprint across and the market. The cycle repeats with information and intelligence at the core.
Such competency is fundamental for 21st-century businesses. But, what’s the reality?
CloudEngin: Beyond the Obvious
Over a hundred million businesses today still lack basic awareness and skill sets on data management, let alone deploying AI for intelligent transformation. Major reasons include insufficient storage, inertia with age-old legacy infrastructure, crippling cybersecurity strategies, and traditional, non-smart business models. While Facebook and other digital giants have already voiced for the Metaverse (an embedded digital reality for starters), many major corporations are still struggling to replace their paper documentation. Workflow management is painfully offline-centric even to this date.
CloudEngin, the world’s leading automation-driven, application-focused managed cloud services provider, ensures the perfect transformation journey to being a data-empowered, intelligent enterprise. Migrate to the cloud at zero disruption to business as usual, optimize processes with leading hyper automation-RPA solutions for maximum ROI, modernize core assets (virtualization of legacy systems, infrastructure, computing resources, networks, servers, data centers, storage, platforms, third-party systems), and onboard advanced applications to digitize operations and workflows across all departments.
Embrace a full-stack data management suite including data collection, cleansing, monitoring, dataflow administration, data modernization, and deep information analysis. Augment data operations with advanced business intelligence (Deep Data Analytics + AI), proprietary platforms to generate smart insights for informed decision-making. Secure all databases, dataflows, data centers, and assets with CloudEngin advanced cybersecurity offerings and threat intelligence. Gain 24/7 data consulting and support to address any requirement, anytime.
With CloudEngin, gain an end-to-end partner to streamline your data intelligent enterprise vision. Tomorrow is today!
5 Common challenges in implementing Enterprise AI
Determining the Right Datasets
Identification and aggregation of accurate training datasets are critical to improving an AI solution’s learning and decision-making. To achieve the same, businesses may have to connect with Data and AI experts to realize the right datasets, train the deployed algorithms for top-notch accuracy, and enable transformative experiences
Data Security and Storage
Larger the training data set, the more accurate is the AI’s prediction capability. However, storage issues plague businesses in the utilization of large volumes of data. Moreover, there’s always a gnawing concern of data security while driving automated, data-intelligent operations. This is why it is integral for businesses to embrace proper data management environments to implement the right AI.
Infrastructure Silos
Development, testing, and running of Artificial Intelligence solutions demand high computational speed and power, often requiring advanced GPU-based systems rather than the traditional CPU-powered infra. AI-based systems will deliver truly agile outcomes if the enterprise has advanced computing technologies and infrastructure.
AI Integration with Existing Systems
Integrating AI solutions and applications into existing business systems and mission-critical infra is a challenge for most businesses. Non-synchronicity with existing libraries, APIs, middleware, architectures may pose a serious problem.
Complex Algorithms and Continual Training of AI Models
Preparing your organization for Data Analytics and AI
Building an artificial intelligence infrastructure involves deliberate and strategic planning around storage, networking and AI data needs among others.
As the volume of data grows the storage needs to scale up too. Ensuring proper storage capacity, IOPS (input/output operations per second), and reliability to deal with the massive data amounts are required for effective application of AI.
Figuring out the storage needs of an organization depends on many factors. For instance, advanced, high-value neural network ecosystems might have scaling issues related to I/O and latency. Similarly, BFSI firms that depend on real-time trading decisions may need fast all-flash storage technology.
Another key factor would be the nature of the source data – Will applications be analyzing sensor data in real-time, or will they use post-processing? How much AI data applications will generate. As databases grow over time, companies need to constantly monitor capacity to plan for expansion.
Networking is another key component of deploying AI, requiring periodic upgrades. Deep learning algorithms are highly dependent on communications and enterprise networks must be highly scalable with high bandwidth and low latency. Companies should automate wherever possible. Software-defined networks (SDNs) powered by machine learning create intent-based networks that can anticipate network demands or security threats and react in real-time.
Having powerful compute resources, including CPUs and GPUs is critical for AI infrastructure. While a CPU-based environment can handle basic AI workloads, deep learning involves multiple large data sets and scalable neural network algorithms. To support that, companies must turn to GPUs to enable organizations optimize their data center infrastructure and gain power efficiency.
Organizations have much to consider here. This includes data storage, processing, and management of the information utilized or generated by the AI. One of the critical steps is data cleansing or scrubbing. This includes removing data from a database that is inaccurate, incomplete, improperly formatted or duplicated.
Data quality is especially critical with AI. Deploying automated data cleansing tools to assess data for errors using rules or algorithms is of paramount importance as the output is only as good as the input.
Data access management is highly critical, requiring efficient processes to share information with only those who need it. Data management strategies ensure that users, machines, and various endpoints have easy and fast access to data without compromising security. This necessitates proper data access controls such as IAM, data encryption solutions, and more.
Offerings: Optimize or Start your Data Analytics and AI adoption with CloudEngin
Detailed, domain-specific assessment, consulting, and support to help integrate cutting-edge analytical processes with data modelling and designing
Streamline data collection, processing, and analysis from multiple sources and IT ecosystems to ensure a universal data architecture
Deploy advanced automation, RPA tools to optimize critical business processes and outcomes. Generate maximum benefits and least costs. Filter out redundancies or hyper-effective and optimized business processes in real-time.
Monitor and manage infrastructure health in real-time to prevent sudden outages and disasters
Single SLA services up to application login layer, DevOps-based development, and testing frameworks
Advanced static and dynamic dataflow monitoring, security with SIEM-SOAR, MDR, EDR, SOC, threat intelligence solutions
Seamlessly address all infrastructure-networks, platform, data storage, and management concerns to deploy advanced AI solutions and services. Embrace ready-made models and libraries to deploy AI on-prem, across remote ecosystems, and edge environments
Data Archival as per industry regulations for automated, efficient data profiling and data cleansing
End-to-end data ingestion and management across all cloud landscapes. Deploy cloud-native data analytics and AI tools to modernize workflows of cloud and all connected landscapes
Utilize Big Data solutions to identify resource and cost-hungry processes, approaches, systems. Fix inefficiencies and improve productivity to reduce overall enterprise expenses
Gain universal visibility to business functions, systems, processes, workflows, applications, and performances in real-time, via intuitive analytical dashboards and smart reporting. Smart insights served via a single pane of glass for informed decision-making
Defined data engineering, data modernization, data ops project management and tool integrations with flexible choices of ETL tools and services
Seamless AI and data governance – compliance with local, national regulations and industry standards, up to date methodologies
Automated management of your workloads with proprietary, novel intelligent solutions such as Self Healing Operations Platform, Universal Cloud Platform to accelerate data intelligent enterprise goals and strategies
What can you expect?
CloudEngin End-to-end Data Management, Analytics, and AI Solutions and Services
Gain competitive advantage, improve process efficiencies, innovate via data. Our data and analytics consulting services are design-led and framework-based to help you with an optimized roadmap to make your enterprise and decisions data-enabled. Your big data and AI projects can function the same way they did as pilots even when they scale up exponentially.
- Data Maturity Assessment
- Data Strategy Blueprinting and Roadmap
- Data Strategy aligned to Business Objectives and growth
- TCO optimization for Data Analytics & AI Adoption
- Industry-specific data consulting services
Traditional Data Warehouse (DWH) systems and processes are unable to render the growth that enterprises are really capable of. The presence of siloed data, high time to insights, inefficiency across multiple systems, limited analysis of data, challenges in security and compliances ask enterprises to consolidate siloed data sources and migrate legacy systems onto the cloud.
- Data migration from legacy to cloud platforms
- SAP, on-prem data to Data Lake migrations
- Traditional Data warehouse (DWH) to data on cloud
- Vertical-based use cases
- Adaptive application and asset modernization
DataOps refers to continuous support and management of the Data pipelines post the implementation and setup of a Data-related use case on the cloud. DataOps supports the underlying Infrastructure, Application (ETL and transformation code), and the Database in a Data Analytics solution.
- Data Pipeline setup, support, and management
- AIOps-powered managed services
- Incident, problem, and change management
- Operational tuning and operational automations
- Data Platform Security Management
- Performance Measurement and Reporting
- Support Resolution and defined SLA for managed analytics
Focuses on practical applications of data collection, processing, and analysis. Data scientists process large-scale organizational information to generate insights and solve essential use-cases for immediate impact.
- Data discovery and ingestion
- Data integration
- Data lakes
- Data Warehouses
- Master Data Management
- Visualization
- Reporting
- Dashboards
Integrate deep, smart analytics across business processes with AI, ML, and Deep Learning Capabilities. Modernize, and smarten up processes related to enterprise strategies, service delivery, operations, customer management, supply chain management, and monitoring with cutting-edge intelligent analytics.
- Data Science Solutions with AI and ML
- Use case-driven Data Modelling
- Recommendation Engines
- Sentiment Analysis
- Image, Speech/Text, Video Analytics
Secure static and dynamic dataflows across the entire organization. Monitor, analyze, and protect databases, data centers, and dataflows across the firm’s entire IT stack. Embrace deep threat hunting, remediation capabilities paired with advanced threat intelligence and smart cybersecurity solutions. Implement a stringent data governance framework and ensure seamless compliance to local-national regulations and international standards.
- Data shielding with data masking, data encryption, etc
- Application and API security management
- Databases and data center security management
- SIEM-SOAR, and Risk Analytics
- Vulnerability Management and Penetration Testing
- Threat Intelligence
- Identity and Access Management
- Data Obfuscation
- Role-based Access Control
- Network Security
- Logging and Monitoring
- Data Reconciliation and Reporting
- IT Risk Advisory and Maturity Modelling
- Regulatory and Compliance Support
Intelligent Process Optimization and end-to-end Automation with CloudEngin Hyper Automation, RPA Solutions for Maximum ROI
CloudEngin deploys advanced machine learning and deep learning algorithms, solutions, and platforms to continually optimize complex processes and IT ecosystems in real-time. Avail full-stack automation and modernization of workflows and operations to grant enterprises the freedom to focus on core offerings and business growth. Take IT hassles out of the equation, once for all.
As the volume of data grows the storage needs to scale up too. Ensuring proper storage capacity, IOPS (input/output operations per second), and reliability to deal with the massive data amounts are required for effective application of AI.
Figuring out the storage needs of an organization depends on many factors. For instance, advanced, high-value neural network ecosystems might have scaling issues related to I/O and latency. Similarly, BFSI firms that depend on real-time trading decisions may need fast all-flash storage technology.
Another key factor would be the nature of the source data – Will applications be analyzing sensor data in real-time, or will they use post-processing? How much AI data applications will generate. As databases grow over time, companies need to constantly monitor capacity to plan for expansion.
- sualize the entire process map and paths
- Discover processes, trends, patterns, and deviations
- Identify good candidates for automation
- Define & Configure Key Performance Indicators
- Identify process deviations /inefficiencies impacting the metrics
- Gain actionable insights to improve business outcomes
- Find new automation opportunities
- After automation, monitor and see improvements in KPIs
- Automate routine/labor-intensive as well as cognitive tasks
- Leverage reusable objects, optimize robots, and improve efficiency
- Integrate with existing business systems
- Test and deploy custom-built bots
- Enable faster and accurate end-to-end process automations with in-house RPA Center of Excellence
Why avail CloudEngin Data Analytics and AI Solutions and Services?
One of most trusted Managed Cloud Service providers with expertise in Data Analytics, and AI solutions in APAC, MEA, and the Americas for 12+ years
World’s largest Application-focused, high-end managed services provider with AI-driven, automated migration operations
24/7 Support backed by 1600+ cloud and analytics experts, 25 dedicated Centres of Excellence
Comprehensive experience with public cloud platforms such as Azure, GCP, OCI, AWS, IBM Cloud, Ali Cloud etc. Data Migration involving heterogeneous data and complex enterprise applications.
Zero Friction Data Modernization Model with industry-leading Cloud Adoption Factory approach at 99.95% availability, zero outages, near-zero delays
25000+ Apps and Databases migrated, 3000+ databases handled, 10,000 TB+ Managed Databases with no-fail 0.5 million transactions per hour.
Large scale database modernization experiences for 1000+ clients including 10 of Top 25 Global Clients.
Proven Expertise in Data Ingestion, Aggregation, Cleansing, Analysis, and Insights generation with cutting-edge AI solutions: Data and IT Modernization, Data Ops, Data Engineering, Cloud Data Analytics, and Management offerings
Advanced Managed Data Analytics, Business Intelligence with effective management for structured, semi-structured, and unstructured data.
Proven Expertise with Hyper Automation and intelligent RPA solutions for end-to-end automation – 1.2 million man-hours saved and 1.5 billion payments processed, 35X faster reporting, 100% efficiency.
Dedicated DR on Cloud offerings with automated recovery-backup, failback-failover mechanisms, zero data losses.
200+ Compliance and DR drills and audits done annually with stringent compliance to industry specific and geography specific regulatory standards.
Dedicated Cloud Managed Security and Data Security Services Expertise, 40+ Security Controls, dedicated SOCs, end-to-end data encryption, and verification.
CloudEngin proprietary solutions including Self-healing Operations, Universal Cloud Platform, and more.
Cost-effective as-a-service engagement models offering single SLA up to App layer
Realise The New Potential With Our Data Analytics & AI Expertise
Schedule a consultation with our Cloud experts and get answers for any specific queries you may have. You can also schedule a visit to our Datacenters, or share feedback on our website and services.