Data Analytics and AI Solutions and Services

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.

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