CDAO UK schedule

• Addressing data scarcity issues and enhancing overall model performance, sparking innovation   
• Increased interdisciplinary collaboration to obtain well rounded solutions 
• Producing data that simulates real world-scenarios, reducing the need for extensive manual data collection 
• Leveraging predictive analytics to foresee potential outcomes, trends and anomalies, aiding in strategic decision making  
• Strategies on how to eradicate ethical biases and ensure adherence to compliance when dealing with generative AI 

• Integrating data from different departments and systems to create a centralized data repository 
• Establishing data governance practices to maintain data quality over time 
• Utilize advanced analytics, including machine learning and predictive modeling, to gain insights from your financial data 
• Streamlining financial operations by identifying areas where automation and optimization can reduce costs and improve efficiency 
• Maximizing revenue and profit margins using data-driven pricing decisions  

Joanne Biggadike, Deputy Head of Data UK, DUAL Group  

Mike Seville, Chief Data Officer, Dojo 

Pankaj Manek, Global Head of Data Excellence, Apex Group 

Harleen Thethy, Vice President Digital Analytics, Barclays Corporate & Investment Bank 

• Emphasizing how data mesh corresponds with overarching strategic objectives, gaining the business a competitive edge and an assured ROI  
• Providing examples of successful data mesh implementations in similar industries, showcasing quantifiable improvements in efficiency, agility, and innovation 
• Presenting data mesh as a risk mitigator and security enhancer  
• Demonstrating data mesh’s is capabilities of adjusting to emerging technologies and shifts in market conditions, ensuring its resilience 
• Highlighting your team’s skills and expertise, emphasizing how their efforts align with the organization’s vision  

Presented by Fivetran  

• Pioneering innovative business models that reshape the significance of connectivity 
• Analysing the correlation between 5G-powered digital ecosystems, service satisfaction, and automated networking 
• Embracing agile methodologies to quickly respond to market changes and user feedback, ensuring your ecosystem remains relevant and competitive 
• Delivering a flawless end-to-end service journey by harmonizing efficiency and user experience 
• Tailoring your ecosystem to accommodate different languages, cultures, and regulatory environments, enabling growth across diverse market 

• Exploring collaborative dynamics between your business and cloud provider to amplify synergies 
• Forming a competent and diverse task force to facilitate smooth and swift cloud migration 
• Cultivating cultural alignment to maximize ROI  
• Identifying potential risks and developing contingency plans for minima disruptions during migration  
• Embracing transparent examination of errors to drive an effective strategy 

Kassim Hussein, Head of Data and Analytics, Cleveland Clinic London  

Mike Leverington, Director of Data Experimentation, ITV  

Luke Vilain, Generative AI Risk Assessment Lead, UBS   

From Data and AI algorithms into production-ready web applications. 

Discover Taipy an open-source Python library for easy, end-to-end application development, featuring what-if analyses, smart pipeline execution, built-in scheduling, and deployment tools. 

In this talk, participants will discover how to: 

• Model dataflows, orchestrate pipelines, and manage scenarios (which are pipeline executions) through the development of an application backend 
• Develop an interactive and customizable multiple-page dashboard without knowing HTML, JS, and CSS by using a simple syntax and make it in a few lines of code 
• Create and manage scenarios through a robust pipeline graphical editor 

With Taipy, transform simple pilots into production-ready end-user applications in no time with the most powerful Python front-end and back-end stack. 

• Maximizing your data's potential through advanced analytics to comprehend and provide customers with precisely what they desire 
• Creating customized, integrated products to seize opportunities in the evolving distribution landscape 
• Emphasizing customer experience and allocate resources to enhance journey optimization, boosting your reputation and bolstering customer retention rates 
• Assessing risk factors in real-time, helping financial institutions better manage their portfolios and reduce exposure to risky assets 
• Identifying fraudulent activities and transactions swiftly by analyzing transaction data and customer behavior 

• Utilizing automation to clean, transform, and organize unstructured data into a usable format, saving time and reducing errors 
• Deploying machine learning techniques to identify relevant features within unstructured data, enhancing insights and predictive capabilities 
• Employing NLP algorithms to extract meaning from text data, enabling sentiment analysis, topic modeling, and entity recognition 
• Leveraging ML models to interpret images and videos, unveiling patterns and insights otherwise hidden in visual data 
• Utilizing ML-powered pattern recognition to uncover relationships and anomalies in unstructured data, revealing valuable insights  

• Exploring the untapped possibilities that the Metaverse could unlock in the coming years, spanning enhancements to customer interactions, product innovation, distribution strategies, internal processes, staff engagement, and talent retention 
• Gauging the Metaverse's potential influence on digital asset management, and get ready to safeguard crypto assets and NFTs within this emerging digital realm 
• Leveraging the Metaverse as a fresh avenue for distribution, elevating brand visibility, while simultaneously captivating and educating customers, providing them with a profound insight into the worth of your offerings 
• Collaborating with technology providers, Metaverse platforms, and industry experts to gain insights and access to emerging technologies and trends 
• Encouraging your team to experiment with new approaches and technologies within the Metaverse space 

Ramakrishnan Subramanian, Director- Head of Data and Analytics, SquareTrade  

Janthana Kaenprakhamroy, CEO, Taploy 

Tom Clay, Chief Data Scientist, Covéa Insurance 

• Mastering talent, skills, and culture by navigating challenges and solutions 
• Instilling an enduring data culture for impactful data-driven decisions 
• Responding to big tech challenges, securing top tech talent, and nurturing skills 
• Evolving skill sets in the data and AI era and sourcing them effectively 
• Gauging metrics and ROI of talent, skills, and culture in digital transformation 

Presented by Multiverse  

• Exploring and guiding the ethical optimization of data and technology advantages 
• Delving into diverse ethical considerations across various use scenarios 
•  Analyzing public and consumer perspectives on intelligent technologies and AI 
• Restoring confidence in data technologies to enhance data acquisition and insights 
• Probing how ethically obtained insights drive impactful strategies and technologies 

• Engaging customers through transparency to spark fresh dialogues 
• Obtaining explicit consent from consumers before collecting their data, respecting their choices and preferences 
• Providing consumers with control over their data, allowing them to access, correct, or delete their information 
• Keeping consumers informed about how their data is being used and any changes in data practices 
•  Regularly assessing your data practices to ensure alignment with evolving ethical standards and consumer expectations 

• Spearheading an effective data literacy initiative to drive tangible and rapid transformation 
• Painting a vivid picture and conveying data's worth to non-data personnel, enhancing their involvement 
• Establishing a comprehensive framework capable of conveying data to diverse audiences with varying levels of data proficiency 
• Triumphing over obstacles and setbacks in the journey toward fostering data literacy 
• Introducing user-friendly data analytics tools that don't require advanced technical skills 

Gishan Nissanka, Group Head of People Intelligence, Worldline  

Murtz Daud, Chief Data Officer, St. Andrews Healthcare  

Andy Wrigley, Former Head of Communications- Data and Analytics, HSBC  

• Exploring initial steps in crafting a center of excellence 
• Disseminating knowledge throughout the organization to enhance insight optimization  
• The significance of gaining support across departments for a unified approach 
• Consistently observe and analyze shifts in customer behavior, preferences, and expectations, offering the business insights to adjust and enhance the customer experience 
• Identify common data pain points across the business and focus your effort solving issues so that many people within your organization can reap the benefits 

• Championing data literacy awareness by crafting a clear case for change 
• Leveraging partnerships and collaborating with stakeholders to attain a data driven culture 
• Modernizing legacy systems and progressing the journey of cloud migration and analytics 
• Addressing resistance to change by highlighting how data-driven decisions can enhance efficiency and outcomes 
• Making data accessible to relevant teams, ensuring they have the resources to base their decisions on insights 

• Analyzing optimal approaches to data strategy to achieve excellence 
• Exploring factors that hinder swift decision-making to foster innovation 
• Advancing cooperation between data leaders and stakeholders to forge a cohesive strategy 
• Embracing risk and learning from errors to drive rapid adaptation and expansion 
• Engaging in quick iterations and prototypes to test novel ideas, allowing space for calculated risks  

Mike Leverington, Director of Data Experimentation, ITV  

Marijke Majoor, CDO |Head of Data & Analytics, NIBC Bank  

Shweta Gupta, VP - Strategic Planning| Transformation | Business Analytics, Deutsche Bank 

• Broadening role expectations to prevent overlooking emerging trends 
• Cultivating a culture of innovative thinking and continuous learning 
• Fostering cohesive comprehension of data expectations through enhanced collaboration among data roles 
• Allocating time for employees to work on innovative projects that align with emerging trends, fostering creativity and exploration. 
• Conducting periodic trend analyses to identify upcoming technologies and methodologies, allowing you to prepare in advance 

• Demonstrate your commitment to learning with active participation in training and development opportunities 
• Offer a variety of learning resources such as online courses, workshops, webinars, and reading materials that cater to different learning preferences. 
• Communicate that continuous learning is valued and expected within the team 
• Allocate a budget for individual or team-wide learning initiatives, enabling access to paid courses, conferences, and certifications 
• Encourage learning from different backgrounds and perspectives to foster an inclusive learning culture 

• Appointing data champions or ambassadors within different teams who can provide guidance, share best practices, and encourage data-driven approaches 
• Providing visibility into data sources, collection methods, and processing steps 
• Sharing the importance of data accuracy and integrity 
• Equipping your team with user-friendly analytics tools that allow them to explore data, create reports, and extract insights without requiring technical expertise 
• Tailoring training programs to individual needs   

Ramakrishnan Subramanian, Director- Head of Data and Analytics, SquareTrade  

Pardeep Bassi, Global Proposition Leader-Data Science, WTW 

• Crafting compelling narratives that illustrate the impact of data governance on real-world scenarios 
• Utilizing visually appealing graphics, infographics, and animations to convey complex concepts in an easy-to-understand and captivating manner 
• Injecting humor into your messaging to create a lighter tone 
• Involving influencers or industry experts who can speak positively about the importance of data governance 
• Painting a futuristic picture of how data governance will shape the future, making it an exciting and essential part of the journey 

• Encouraging data ownership within business units via awareness and education 
• Ensuring data falls under suitable technical supervision 
• Establishing a feedback mechanism linking program operations and the risk register for up-to-date risk data capture and reflection 
• Classifying data based on its sensitivity and importance, aiding in targeted risk management 
• Promoting collaboration between risk management and data governance teams to align efforts effectively  

• Executing a data governance plan that harmonizes with your organization's objectives, focal points, and principles, guaranteeing the proficient and productive utilization of data 
• Embracing contemporary methodologies for data excellence, such as employing data observability methods to identify and address data challenges before they escalate into substantial predicaments 
• Enhancing data governance endeavors with progressive techniques for data quality and data observability, fostering unwavering data integrity and bolstering decision-making confidence 
• Evaluating vital Master Data domains prone to the ramifications of data downtime 
• Developing contingency plans to handle potential data downtime scenarios  

• Viewing data transformation as a comprehensive endeavor that involves people, processes, and technology 
• Implementing effective change management strategies to help employees adapt to new data-driven processes 
• Investing in training and upskilling your workforce to ensure they are equipped to leverage data effectively 
• Defining key performance indicators (KPIs) to measure the impact of data transformation on business value 
• Embracing an agile mindset, allowing you to iterate and adapt your data transformation strategies as needed 

Ankur Gupta, Head of Data Products, Danske Bank 

Sachin Sharma, Head of Data Change and Innovation, Danske Bank 

Nivedh Iyer, Group Head of Data Management, Group Data & Analytics, Danske Bank 

• Implementing MDM to establish a single, reliable source of truth for critical data, ensuring consistency across the organization 
• Improving data quality, accuracy, and operational efficiency with automation 
• Identifying the future of data governance & its implications for businesses 
• Developing a comprehensive data catalog that enables easy discovery and understanding of available data assets 
• Assigning data ownership responsibilities to individuals or teams to ensure accountability and stewardship 

• Fostering a shared understanding of data's significance across functions, aligning everyone towards a common purpose 
• Forming multidisciplinary teams that collaborate on data-driven initiatives, breaking down siloed thinking 
• Developing centralized platforms for data intelligence, enabling cross-functional accessibility and insights 
• Implementing robust data governance to manage data quality, privacy, and security in a holistic manner 
• Creating mechanisms to collect feedback on collaboration efforts, driving continuous improvement  

Aman Aneja, Director of Analytics, Fairview Equity Partners 

Peter Jackson, Chief Data and technology Officer, Outra 

- Developing a clear vision that aligns with the changing landscape and communicates it effectively to your team

- Formulating a comprehensive strategy to adapt to global changes, considering technology advancements, data implications, and people challenges

- Keeping lines of communication open, sharing insights on changes and their impact on the organization

- Encouraging a culture of agility and innovation to swiftly respond to evolving challenges and opportunities

- Regularly assessing your strategies, adapting them as the global landscape evolves

• Exploring evolving stakeholder dynamics with data leaders 
• Evaluating shifting investment agendas and prospects for data leaders in 2024 
• Spotlighting emerging trends driving enhanced data capabilities and value 
• Prioritizing AI-enhanced analytics using natural language and generative AI 
• Unleashing data potential through the lens of data observability 

Lisa Allen, Director, The Pensions Regulator  

Ramakrishnan Subramanian, Director- Head of Data and Analytics, SquareTrade  

Yousra Aoudi, Quantitative Analyst, VP, BNY Mellon 

• Designing data products with end-users in mind ensuring intuitive, user-friendly, and providing actionable insights 
• Utilizing advanced analytics techniques such as machine learning and AI to extract deeper insights from your data 
• Thoroughly testing and validating your data products for increased accuracy and reliability 
• Incorporating mechanisms for users to provide feedback on the data products. Use their input to make improvements 
• Prioritizing data security and privacy to ensure sensitive information is handled appropriately 

• Invest in technologies such as IoT sensors, machine learning, and blockchain to enhance data collection accuracy and traceability 
• Monitoring sustainability efforts and key performance indicators (KPIs) using data to enhance visibility 
• Crafting initiatives that uphold data integrity while championing energy conservation 
• Disseminating data organization-wide to unearth valuable insights into sustainability 
• Engaging with internal and external stakeholders to gather feedback, address concerns, and align strategies 

Ronald Root, Head of Data and Analytics, Van Oord 

Dario Morelli, Head of Data Analytics, TrueLayer 

Jenny Brooker, Chief Data Architect, Central Digital and Data Office 

• Leverage the cloud's elastic nature to scale resources up or down as demand fluctuates, enabling efficient cost management and agile responses 
• Tapping into cloud's global presence to expand into new markets swiftly and serve customers seamlessly across regions 
• Adopting cloud-native practices like DevOps to streamline software development, improve collaboration, and deliver updates more frequently 
• Implementing cloud-based disaster recovery solutions for data backup, ensuring business continuity in the face of disruptions 
• Collaborating with cloud service providers and third-party vendors to access specialized expertise, tools, and services  

• Development of digital proficiency and technological competencies, fostering adeptness in navigating and utilizing technology across diverse domains 
• Enhancement of communication and negotiation proficiencies, resulting in prosperous business transactions and enriched interpersonal connections 
• Creation of platforms for experimentation and deriving lessons from setbacks, culminating in a more robust and inventive mindset 
• Augmentation of adeptness in managing uncertainty and ambiguity, equipping individuals to skillfully navigate unforeseen circumstances and uncover prospects amidst challenges 
• Amplified agility and versatility across varied scenarios  

• Enabling end-to-end visibility for easy identification of any gaps, delays, or inconsistencies that might impact your analytics and AI outcomes 
• Identifying and addressing resource bottlenecks, data processing delays, and other inefficiencies 
• Detecting potential issues before they impact critical business decisions 
• Validating the inputs and outputs of your AI models using data observability tools 
• Minimizing downtime with automated alerts and notifications for real time performance deviations  

Eliot Frazier, Head of AI Infrastructure, AI for Good Foundation 

Pedro Rente Lourenço, Global Head of Data Science, Laing O’Rourke   

• Examining the 2023 regulatory landscape and the hurdles confronting financial institutions 
• Ensuring data accuracy and completeness by constructing a sturdy control framework 
• Optimizing efficiency by effectively handling data overlaps for cost reduction 
• Elevating regulatory data quality through data standards and unfinished business 
• Driving regulatory reporting standardization and digitization: progress and insights from industry and regulatory initiatives  

• Conducting thorough assessments of the IT infrastructure, identifying vulnerabilities, potential points of failure, and areas for improvement 
• Developing robust resilience and disaster recovery plans to minimize downtime in the event of an outage and ensure business continuity 
• Aligning IT practices with relevant financial regulations to ensure compliance, minimize legal risks, and avoid penalties 
• Implementing strong data governance practices to ensure the accuracy, security, and compliance of data used in analytics and reporting 
• Evaluating the risks associated with IT service partners and vendors, ensuring they meet regulatory requirements and adhere to best practices 

• Enhanced efficiency contributing to the triumph of strategic goals 
• Utilizing a unified source of truth for minimized data processing and improved customer interactions 
• Extracting and showcasing the worth from your consolidated data source to executive leadership 
• Employing MDM strategies to create a master record for key entities, ensuring data uniformity 
• Utilizing data integration, ETL (Extract, Transform, Load), and data warehousing technologies to consolidate data 

Emma Eatch, Head of Clinical Analytics, Boots  

Gareth Hagger-Johnson, Director of Data Strategy, The Nottingham  

Pedro Duarte, Global Head of Client Data Insights & Predictive Analytics, BNY Mellon 

• Exploring the advantages of varied team structures for enhanced coordination and collaboration 
• Leveraging internal data to shape novel and inclusive diversity & inclusivity strategies 
• Acknowledging and addressing both deliberate and unintended consequences of quotas within your team 
• Elevating diversity & inclusivity engagement through advanced learning and development initiatives 
• Establishing channels for team members to provide feedback on diversity and inclusivity initiatives 

• Demonstrating the value of data-driven decisions through success stories and tangible outcomes to garner support from top executives 
• Designing customizable dashboards that allow different teams to access relevant metrics and KPIs based on their specific needs 
• Implementing user-friendly analytics tools that allow non-technical users to explore and visualize data independently 
• Creating a centralized data platform where employees can access, manipulate, and analyze data without relying on IT or data specialists 
• Offering training programs and resources to enhance employees' understanding of data concepts, terminology, and analysis techniques 

Andy Wrigley, Former Head of Communications- Data and Analytics, HSBC  

Pedro Duarte, Global Head of Client Data Insights & Predictive Analytics, BNY Mellon 

Peter Jackson, Chief Data and Technology Officer, Outra 

• Utilizing data virtualization techniques to access and integrate data without physical data movement, reducing energy consumption 
• Leveraging cloud platforms for data storage and processing, offering energy-efficient infrastructure and scalable resources 
• Implementing smart data management practices that prioritize frequently accessed data for more efficient storage and retrieval 
• Ensuring data accuracy and quality to prevent the need for data reprocessing, reducing energy waste 
• Encouraging remote work and virtual meetings to decrease the need for physical travel, leading to lower emissions  

• Understanding your organization's unique requirements, goals, and constraints to determine the most suitable approach for your data governance 
• Introducing iterative practices by starting with a smaller scope or pilot projects 
• Adopting agile methodologies that promote flexibility and collaboration 
• Defining relevant metrics to measure the effectiveness of data governance initiatives from both enterprise and iterative perspectives 
• Implementing a change management strategy to address cultural shifts required for both enterprise and iterative approaches