Data Analytics and AI Solutions & Services
Collect, manage, and analyse data flows from many ecosystems and sources. Deploy sophisticated engines for data input, processing, and security. Adopt cutting-edge Artificial Intelligence (AI) to create business-relevant insights for judicious decision making.
The Data Intelligent Organization to Survive and Prosper
Does this take more persuading after the pandemic?
Cloud, Big Data, AI, and IoT are often cited as the essential pillars of the Fourth Industrial Revolution, a multi-impact period that is characterised as more transformative and expansive than the previous three. However, upon closer inspection, what seems to be four distinct technologies constitute a bigger whole.
Modern businesses are rapidly diversifying their endpoint ecosystems, including mobiles, user PCs and laptops, smartwatches and wearable devices, biometric devices, and sensors. Massive quantities of data move from these endpoint ecosystems to the centralised Cloud IT ecosystems, where it is stored and analysed using cutting-edge analytical methods. AI solutions operate on top to create intelligent business insights, automate important processes, and pave the way for future developments, influencing the firm’s endpoint presence across and in the market. At the centre of the cycle are information and intelligence.
This skill is essential for firms in the twenty-first century. But what is the truth?
CDWT: Beyond the Obvious
Over a hundred million enterprises lack even the most fundamental data management knowledge and skills, much alone AI deployment for intelligent transformation. Major causes include inadequate storage, lethargy with ageing legacy infrastructure, ineffective cybersecurity tactics, and non-intelligent business models. While Facebook and other digital giants have previously advocated for the Metaverse (for starters, an integrated digital reality), many large firms are still attempting to replace paper documents. Even today, workflow management remains frustratingly offline-centric.
CDWT, the world's leading automation-driven, application-focused supplier of managed cloud services, provides a seamless migration to a data-enabled, intelligent organisation. Migrate to the cloud with zero business disruption, optimise processes with leading hyper automation-RPA solutions for maximum ROI, modernise core assets (virtualization of legacy systems, infrastructure, computing resources, networks, servers, data centres, storage, platforms, and third-party systems), and onboard advanced applications to digitise operations and workflows across all departments.
Adopt a comprehensive data management package that encompasses data collecting, cleaning, monitoring, dataflow administration, data modernisation, and in-depth data analysis. Enhance data operations with superior business intelligence (Deep Data Analytics + AI) and unique platforms to provide intelligent insights for intelligent decision-making. Protect all databases, dataflows, data centres, and assets with the sophisticated cybersecurity solutions and threat intelligence provided by CDWT. Gain data consultation and assistance around-the-clock to solve any need, at any time.
5 Common Challenges in Enterprise AI Implementation
Determining the Right Datasets
Identification and aggregation of correct training datasets are crucial for enhancing the learning and decision-making capabilities of an AI system. To accomplish the same, organisations may need to collaborate with Data and AI specialists to collect the appropriate information, train the deployed algorithms to attain the highest levels of precision, and provide revolutionary experiences.
Data Security and Storage
The larger the training data collection, the more accurate the AI's ability to make predictions. However, storage constraints hinder the use of high data quantities by enterprises. In addition, there is always a nagging fear over data security while driving automated, data-savvy activities. For this reason, it is crucial for enterprises to adopt effective data management infrastructures in order to apply the appropriate AI.
AI Integration with Existing Systems
Development, testing, and operation of Artificial Intelligence solutions need significant computational speed and power, often necessitating powerful GPU-based systems as opposed to conventional CPU-powered infrastructure. If an organisation has sophisticated computing technologies and infrastructure, AI-based solutions will provide really agile results.
Root Cause Analysis
Identifies potential underlying causes for each problem and assists teams in resolving it fast by offering real-time coaching.
Complex Algorithms and Continual Training of AI Models
After the installation of AI solutions is complete, businesses must continue to devote significant labour and resources to training the AI systems for optimal accuracy.
Organizational preparation for Data Analytics and AI
Building an infrastructure for artificial intelligence requires thoughtful and strategic design about storage, networking, and AI data requirements, among others.
- Big Data Storage: Infrastructure needs for AI
- Network Infrastructure for AI
- Responsibilities Associated With Artificial Intelligence
- Preparing AI data
- AI data governance
As the amount of data increases, storage must also expand. Effective use of AI requires enough storage capacity, IOPS (input/output operations per second), and dependability to manage huge data volumes.
Numerous variables determine how a company determines its storage requirements. For instance, sophisticated, high-value neural network ecosystems may have I/O and latency scalability concerns. Similarly, BFSI organisations that rely on trading choices made in real-time may need rapid all-flash storage technology.
Another important consideration is the nature of the source data: Will applications analyse sensor data in real-time, or will they use post-processing? How much data will AI applications generate? As databases expand over time, businesses must continuously assess capacity to prepare for growth.
Networking is another essential aspect of AI deployment that requires continuous updates. Deep learning algorithms rely heavily on communications, and corporate networks must be extremely scalable with high bandwidth and low latency. Wherever feasible, businesses should automate their processes. Software-defined networks (SDNs) fueled by machine learning provide intent-based networks that can anticipate network needs or security concerns and respond in real time.
Compute resources, such as CPUs and GPUs, are crucial for AI infrastructure. Deep learning requires numerous big data sets and scalable neural network algorithms. While a CPU-based environment may handle basic AI tasks, deep learning requires multiple large data sets and scalable neural network techniques. To achieve this, businesses must use GPUs to improve data centre infrastructure and increase power efficiency.
There is plenty for organisations to consider here. This involves the storage, processing, and administration of data consumed or produced by the AI. A crucial stage is the cleaning or scrubbing of data. This involves eliminating erroneous, incomplete, badly structured, or duplicate data from a database.
In AI, data quality is very crucial. It is of the utmost necessity to deploy automated data cleaning technologies to evaluate data for flaws using rules or algorithms, since the result is only as good as the input.
Management of data access is of the utmost importance, necessitating effective mechanisms to share data with just those who need it. Data management techniques guarantee that people, computers, and multiple endpoints can quickly and securely access data. This requires appropriate data access controls, including IAM, data encryption technologies, and more.
Optimize or Initiate Your Data Analytics and AI Adoption With CDWT
We can assist you in addressing each of the aforementioned obstacles. Further reading…
Detailed domain-specific evaluation, consultation, and assistance to combine cutting-edge analytical procedures with data modelling and designing.
Archiving of data in accordance with industry standards for automated, effective data profiling and data cleaning
Ensure a universal data architecture by streamlining data collection, processing, and analysis from diverse sources and IT ecosystems.
End-to-end data intake and administration across all cloud environments. Implement cloud-native data analytics and AI solutions to modernise cloud and all linked landscapes' operations.
Deploy sophisticated automation and RPA solutions to improve vital business processes and results. Maximize advantages and minimise expenses. Real-time elimination of redundancies or hyper-effective and improved business operations.
Utilize Big Data solutions to discover processes, techniques, and systems that are resource- and cost-intensive. Reduce total corporate costs by eliminating inefficiencies and boosting output
Real-time monitoring and management of infrastructure health to avoid unforeseen outages and catastrophes.
Gain real-time insight into all company operations, systems, processes, workflows, apps, and performance through analytical dashboards and intelligent reporting. For educated decision-making, a single pane of glass containing intelligent insights.
Single SLA services up to the login layer of the application, DevOps-based development, and testing frameworks.
Defined data engineering, data modernisation, data operations project management, and tool interfaces using adaptable ETL tools and services.
Security using SIEM-SOAR, MDR, EDR, SOC, and threat intelligence systems for advanced monitoring of static and dynamic dataflows.
Effortless AI and data governance - compliance with local, national, and industry standards, and the use of the most recent techniques
What can you anticipate?
With correct segmentation and targeted marketing and products, optimize buyer journeys. Boost conversions, customer acquisitions, and retentions.
Enhance strategic and data-driven decision-making across operations, supply, people management, and administrations, among other areas, to facilitate more intelligent and efficient business practises.
Incorporate contextual market and consumer input to improve product quality. Deploy sophisticated sentiment analysis and digital listening techniques, for example, to better evaluate market attitudes and requirements.
Data Management, Analytics, and AI End-to-End Solutions & Services from CDWT
- Data Consulting
- Data Modernization
- Data Ops
- Data Engineering
- Advanced Analytics & AI
- Data Security, Governance & Compliance
Gain competitive edge, enhance process efficiency, innovate using data. Our data and analytics consulting services are design-driven and framework-based in order to provide you with an efficient road map for data-enabling your business and choices. Even when scaled massively, your big data and AI initiatives may operate identically to how they did as pilots.
- Data Maturity Evaluation
- Data Strategy Roadmapping and Planning
- Aligned Data Strategy with Business Objectives and Development
- Optimization of TCO for Adoption of Data Analytics and AI
- Industry-specific consultancy services for data
Traditional Data Warehouse (DWH) technology and procedures are incapable of delivering the expansion that businesses are really capable of. Enterprises are compelled to combine fragmented data sources and transfer older systems to the cloud due to the existence of segregated data, high time to insights, inefficiencies across many systems, restricted analysis of data, and security and regulatory concerns.
- Migration of data from old to cloud-based systems
- SAP, on-prem data to Data Lake migrations
- Traditional Data Warehouse (DWH) vs cloud-based data
- vertically-specific usage cases
- Application adaptability and asset modernisation
DataOps refers to the ongoing administration and maintenance of the Data pipelines after the deployment and configuration of a cloud-based Data-related use case. DataOps supports the Data Analytics solution's underlying Infrastructure, Application (ETL and transformation code), and Database.
- Data Pipeline setup, support, and management
- Powered by AIOps, managed services
- Incident, issue, and change management
- Tuning operations and automating operations
- Data Platform Security Administration
- Evaluation of Performance and Reporting
- Support Resolution and Specified Service Level Agreements for Managed Analytics
This course focuses on the practical applications of data gathering, processing, and analysis. Data scientists examine massive amounts of corporate data to produce insights and solve critical use cases with quick effect.
- Discovery and ingestion of data
- Data integration
- Data lakes
- Data Archives
- Master Data Management
- Visualization
- Reporting
- Dashboards
Using AI, ML, and Deep Learning Capabilities, integrate smart, in-depth insights throughout business processes. Using cutting-edge intelligent analytics, modernise and enhance corporate strategy, service delivery, operations, customer management, supply chain management, and monitoring.
- Data Science Services that Include AI and ML
- Use case-driven Data Modelling
- Recommender Systems
- Emotional Analysis
- Image, Text and Speech, and Video Analytics
Secure static and dynamic dataflows across the enterprise. Monitoring, analysing, and protecting the firm's complete IT stack's databases, data centres, and dataflows. Utilize comprehensive threat hunting, remediation capabilities, and sophisticated threat intelligence with intelligent cybersecurity solutions. Implement a rigorous data governance structure and guarantee compliance with local, national, and international legislation and standards.
- Data protection through means of data masking, data encryption, etc.
- Management of application and API security
- Management of databases and data centre security
- SIEM-SOAR, in addition to Risk Analytics
- Management of Vulnerabilities and Penetration Testing
- Intelligence Regarding Threats
- Identity and Access Administration
- Data Obfuscation
- Role-based Access Management
- Network Protection
- Logging and Monitoring
- Reconciliation and Reporting of Data
- IT Risk Consultation and Maturity Modeling
- Compliance and Regulatory Support
CDWT Expertise: An overview of Data Analytics and Artificial Intelligence (AI) solutions on the top Cloud Platforms
Ingest
AWS
- CDWT Expertise:
- Amazon Kinesis Firehose
- Amazon Schema Conversion DOC
- AWS DMS
- Amazon Glue
- Amazon Redshift Spectrum
- Amazon EC2
- Amazon Managed Streaming for Kafka
AZURE
- Azure Event Hub
- Azure DMS
- Azure Kafka
- Azure VM
-
GCP
- BigQuery
- GCP DMS
- Pub/Sub
- BigTable
- Compute Engine
- Data Fusion
-
Transform
AWS
- Amazon Glue
- EMR
AZURE
- Azure Data Factory
- Azure HDInsight
- Azure Databricks
GCP
- DataProc
- BigQuery
- Dataflow
Process
AWS
- Amazon Glue
- EMR
- AWS Lambda
AZURE
- Azure Data Factory
- Azure Databricks
-
GCP
- Data prep
- Data Fusion
- Cloud Functions
Analyze
AWS
- EMR
- Amazon Redshift
- Amazon Athena
- Amazon Elasticsearch Service
- Amazon SageMaker
AZURE
- Azure Databricks
- Azure SQL DW
- Azure Data Lake Analytics
- Azure Functions
GCP
- Azure Databricks
- Azure SQL DW
- Azure Data Lake Analytics
- Azure Functions
Insights
AWS
- Amazon Glue
- EMR
- Amazon SageMaker
AZURE
- Azure Databricks
- Azure Data Factory
- Azure Synapse
GCP
- DataProc
- DataProc
- Cloud Machine Learning
Reporting
AWS
- AWS QuickSight
- Tableau
- Power BI
AZURE
- QlikSense
- Tableau
- Power BI
GCP
- QlikSense
- Tableau
- Power BI
Governance
AWS
- IAM
- Amazon Macie
- Amazon CloudWatch
- Amazon CloudTrail
- AWS Config
AZURE
- Azure Log Analytics
- Application Insights
-
GCP
- Cloud IAM
- Error Reporting
- Cloud Monitor
- StackDriver
Self Healing Operations Platform (SHOP)
CDWT SHOP is a low-code AI-powered platform that unifies the many tools and solutions required to offer enterprise-level managed cloud services. The intelligent platform integrates hundreds of operational platforms and applications, such as auto-remediation and self-healing, into a single system. This allows the whole infrastructure and application landscape to be automatically controlled through a single pane of glass, while giving clients with a comprehensive picture of their IT infrastructures. The platform increases the productivity of engineers and enables engineers with less expertise to do more difficult jobs.
SHOP changes your enterprise's cloud management operations beyond understanding. Integrate current platforms, such as third-party systems, and connect smoothly to your cloud architecture using robust APIs. Easily automate workflow management, IT infrastructure administration, security management, and project delivery in the cloud, from project inception through reporting to the end client. With SHOP by CDWT, you can prevent outages, identify risks and avert threats in advance, automate risk responses (self-healing), modernise cloud operations and asset administration, and increase overall engineering efficiency by up to 50 percent.
SHOP makes CDWT the biggest Application-centric Managed Services provider in the world.
- Intelligent, Automated Operations Management
- Predictive & Preventive
- Collective Knoweledge
- Situational Awareness
- Remedial & Autonomous
Integrate your cloud architecture with all of your current apps, tools, and systems, as well as third-party systems, on a single intelligent platform. Gain unprecedented control and security over business processes, automate IT operations to save infrastructure expenses, and increase organisational output.
By using clustering and regression models, SHOP is able to identify any abnormalities that might lead to system failures, ensuring that they are promptly addressed even before they occur (Self Healing).
SHOP is also a full-stack infrastructure and Business Activity Monitoring solution that provides a 360-degree view of all pertinent data for identifying potential faults and early warnings.
SHOP captures all contextual data at the moment of the anomaly in order to give appropriate root cause possibilities that enable comprehensive and coherent replies. Utilize a study of essential service interruption reports and the eradication of recurrent problems across operating systems, databases, applications, platforms, etc. Proactive monitoring and preventative maintenance, as well as service enhancement across all infrastructure and application layers.
Our in-house ML engine assures the optimal corrective action for the issue and the system.
Intelligent Process Optimization and End-to-End Automation with RPA Solutions from CDWT Hyper Automation for the Highest ROI
CDWT employs sophisticated machine learning and deep learning algorithms, platforms, and solutions to continuously improve complex processes and IT ecosystems in real time. Utilize full-stack automation and modernization of processes and operations to liberate businesses to concentrate on core services and company expansion. Put an end to IT complications once and for all.
Convert unstructured data to structured data
Data validation utilising intelligent document processing engine
Eliminate the potential of manual mistakes
Integrate extracted data with current business processes and upload/update extracted data
Determine processes, patterns, trends, and deviations
Determine suitable candidates for automation
Define and configure performance metrics
Determine process irregularities and inefficiencies that affect the metrics.
Gain actionable understanding to enhance company results
Find fresh automation possibilities
After automation, monitor KPIs and look for improvements.
Utilize reusable items, optimise robotics, and enhance productivity.
Integrate current enterprise systems
Test and deploy bespoke bots.
Enable quicker and more precise end-to-end process automations with an internal RPA Center of Excellence.