Frequently Asked Questions

Enterprise architecture provides a strategic framework, aiding organizations in aligning business goals with IT strategy. It facilitates efficient decision-making, resource optimization, and technology roadmap creation, ensuring IT investments align with overall organizational objectives.

Capital Planning and Investment Control (CPIC) practices involve strategic planning, selection, budgeting, oversight, and evaluation of investments in information technology and capital assets. CPIC ensures efficient resource allocation, project alignment with organizational goals, and ongoing monitoring for successful IT initiatives.

The relationship between Enterprise Architecture (EA) and CPIC is synergistic. EA provides a holistic view, guiding strategic decisions, while CPIC ensures effective planning and management of investments. Together, they enhance decision support, risk management, and lifecycle management of IT investments.

Data management is crucial for effective decision-making, risk mitigation, and regulatory compliance, ensuring data accuracy, accessibility, and security for overall business success.

Data ingestion is the process of collecting and importing raw data into a storage or processing system for analysis, involving acquiring data from various sources for use in databases, data warehouses, or other processing platforms.

Data integration is the process of unifying data from different sources into a cohesive view. It ensures a consistent understanding of data across an organization, enabling effective analysis, reporting, and decision-making.

Ensure consistency by implementing data quality checks, standardizing formats, mapping to a common schema, deduplicating records, managing metadata, handling errors effectively, enforcing data governance, tracking data lineage, using version control, and employing data profiling tools.

Big data refers to large, complex datasets challenging to manage, process, and analyze using traditional tools. It involves massive volumes of structured and unstructured data, enabling storage, processing, and insights extraction.

Data science involves extracting insights from data using statistical, mathematical, and programming methods. It analyzes large datasets to uncover patterns, trends, and valuable information for decision-making.

Yes, big data environments provide a context for data science work, allowing organizations to derive insights from massive datasets for better decision-making.

Data analytics and business intelligence empower organizations to derive insights, enabling informed decision-making, trend identification, and gaining a competitive edge.

Mainframe support maintains stability and reliability for critical business applications, involving troubleshooting, performance optimization, and ensuring mainframe system security.

Legacy modernization updates outdated systems to align with current needs, adopting newer technologies for improved efficiency, scalability, and integration while preserving essential functionalities.

Technical debt is the cost of suboptimal decisions in software development. Modernization addresses technical debt by adopting updates and improvements.

Legacy modernization involves addressing accumulated technical debt by implementing updates and adopting current technologies to enhance performance, maintainability, and alignment with current business requirements.

Microservices are independent software components. In legacy modernization, microservices offer flexibility, agility, and technology adoption by transitioning from monolithic to modular architectures.

Systems engineering integrates disciplines to design, analyze, and manage complex systems, ensuring components work together for efficient and reliable system development.

A cloud is a network of remote servers on the internet for data storage, management, and processing, providing on-demand access to computing resources.

Cloud computing is a model offering on-demand access to shared computing resources over the internet, promoting flexibility, efficiency, and cost-effectiveness.

Cloud scaling should emphasize horizontal scaling for flexibility, fault tolerance, and cost-effectiveness. A combination of both (elastic scaling) is often optimal for varying workloads.

Cloud computing offers scalability, cost-efficiency, flexibility, and access to resources on-demand, fostering collaboration and rapid innovation.

Cybersecurity safeguards against unauthorized access, protecting businesses and individuals from data breaches, financial loss, and reputational damage.

Zero Trust is a security framework assuming no trust, requiring continuous verification, minimal access, network segmentation, constant monitoring, and the assumption that a security breach is always possible.

The global pandemic and remote work trends have accelerated the adoption of Zero Trust. Its decentralized security measures are well-suited for modern work environments.

Organizations can integrate AI by identifying suitable use cases, investing in talent and technology, addressing ethical considerations, and implementing pilot programs with continuous monitoring.

AI stands for Artificial Intelligence, involving the development of computer systems capable of tasks requiring human intelligence.

Correlative AI focuses on identifying patterns and correlations within data for predictions or recommendations, using statistical and machine learning techniques.

Causal AI involves understanding cause-and-effect relationships, going beyond correlations to infer relationships between variables.

The majority of AI applications today primarily use correlational approaches, emphasizing pattern recognition and prediction.

Network infrastructure forms the backbone of communication, comprising hardware, software, and protocols enabling seamless data exchange.

Agile emphasizes flexibility, collaboration, and iterative development, allowing quick adaptation to changing requirements for a dynamic and customer-centric approach.