Data Architecture Singapore schedule

In this 10-minute networking session, the goal is to connect with three new people. Let the questions on the screen spark your conversation. Enjoy the opportunity to expand your network!

• Automating data processes to streamline and scale data management.
• Creating real-time architectures that enable responsive decision-making.
• Enhancing governance and compliance in complex data ecosystems through AI.

• Leveraging AI to detect and resolve data issues automatically.
• Enhancing data quality and reliability through proactive monitoring.
• Minimizing downtime and improving operational efficiency with self-healing capabilities

• Exploring how AI can enhance compliance through automated governance frameworks.
• Examining the balance between machine-driven oversight and human judgment.
• Discussing the role of AI in ensuring data privacy and ethical use.

Moderator

Ville Kulmala President DAMA Singapore

Panellist

Amit Saxena Head - Data and Regulatory IT Section Credit Agricole

• Identifying common sources of latency in data pipelines and architectures.
• Implementing strategies and technologies to optimize real-time data flow.
• Enhancing user experience by reducing latency in data access and processing.

• Exploring the potential for seamless integration between unified and distributed data systems.
• Identifying strategies for balancing centralized control with decentralized autonomy.
• Discussing the role of automation and orchestration in achieving harmony in hybrid clouds.

• Examining the intersection of data privacy and ethical AI practices.
• Implementing governance strategies that prioritize user privacy without stifling innovation.
• Assessing the evolving regulatory landscape and its impact on data governance.

• Identifying the key challenges associated with managing multi-cloud environments.
• Exploring strategies for optimizing performance and security across hybrid architectures.
• Discussing best practices for data integration and management in cloud ecosystems

• Discussing the principles of cloud-native architecture and its role in scalable data solutions.
• Exploring strategies to enhance data security in cloud-native environments.
• Identifying best practices for designing cloud-native systems that support rapid growth.

• Discussing the shift from centralized to decentralized data management through the data mesh approach.
• Examining the benefits of empowering business units with autonomous data ownership.
• Identifying challenges and solutions in transitioning to a decentralized architecture.

• Exploring how data mesh promotes autonomy and agility within business units.
• Discussing the impact of decentralized data ownership on decision-making and innovation.
• Identifying the best practices for implementing data mesh to drive operational efficiency.

• Discussing the critical components of building real-time data pipelines for agile business operations.
• Identifying strategies to ensure data accuracy and consistency in real-time environments.
• Exploring the role of automation in enhancing the resilience of data pipelines.

• Examining the potential impact of quantum computing on data architecture design and implementation.
• Discussing the challenges and opportunities of integrating quantum technologies into existing systems.
• Identifying use cases where quantum computing can enhance data processing capabilities.

• Discussing the principles of cloud-native architecture and its role in scalable data solutions.
• Exploring strategies to enhance data security in cloud-native environments.
• Identifying best practices for designing cloud-native systems that support rapid growth.

• Exploring the integration of AI technologies in automating data management processes.
• Discussing the benefits and challenges of deploying AI-powered architectures at scale.
• Identifying use cases that demonstrate the value of AI in enhancing data operations.