Boston CDAO Virtual Executive Summit

As organizations continue developing models and advancing their AI/ML program, some organizations have encountered the next set of challenges related to developing efficient ways to deploy, manage and monitor production models at scale. Depending on how long organizations have been developing AI/ML models, organizations may be at different stages in this maturity curve as they begin to design their program and capabilities for governing models.

Join this session to explore:

• Connection of data governance with model governance, including taking steps to address model drift or data drift
• Deploying models and determining ways to productionize and monitor models on premise or in the cloud
• How peers at other organizations have approached Model Governance for their AI/ML models

Today’s data leaders are facing unique challenges that require innovative solutions and the implementation of emerging technologies; Master Data Management in particular requires machine learning. Join this keynote for a thought-provoking presentation by A.M. Turing award winner Michael Stonebraker on the next generation of MDM and why human-guided machine learning is critical for advancing big data, unlocking data’s full potential, and driving analytical insights.

Join this informal and interactive networking session to connect with your Boston CDO peers. In this session, the Boston community will have the chance to share their takeaways from the program in small groups and walk away with at least one new connection.

Traditional data governance is aimed at protecting the integrity of key business data, focusing on data quality and lineage. But in the age of modern analytics, there is a renewed emphasis around increasingly raw data, creating the overwhelming task of managing and protecting hundreds if not thousands of data sources. Join Joe DosSantos, CDO of Qlik, as he outlines how modern data discovery and catalog techniques can advance data governance and how these techniques can accelerate the production of high value predictive models at speed in a Data Ops methodology.

Today’s leaders are challenged to re-envision business models, embrace new ways of engaging customers, empower employees, optimize operations and transform products and services. Hear how your peers are employing new technologies that fuel data driven solutions to these challenges within their organizations.

In this session, explore strategies for:

• Building a confident employee community that uses data to fuel innovation and critical decision making
• Balancing broad access and use of data with governance, compliance and ethical use
• Utilizing best in class technologies to improve scalability and productivity of data management activities

2020 has turned predictive models on their heads. Machine learning and AI tools are more important than ever, as they are increasingly embedded in customer analytics and digital experience platforms. Learn from your peers about how they are using new technologies to improve these customer experiences in the new era.

In this session we’ll discuss:

• Use cases for augmented analytics
• Benefits from these technologies and how to measure their success
• Personalization and privacy issues to avoid along your AI/ML journey

Leaders need faster access to new data to better understand their evolving business and accelerate digital transformation. The COVID-19 pandemic has made historical data obsolete, and many organizations are experiencing a data deficit where their models were trained to recognize behavior patterns that no longer exist. The path forward lies in giving analytics teams fast access to all data, no matter the source, to find new patterns and retrain models. Gartner calls this X Analytics, the ability to improve decision-making with faster access to newer data sources, thus uncovering new behavioral patterns. This will address the loss of the great data science work that's been done, and help get machine learning models focused on the right data.

This keynote will explore: 

• Pain points associated with delays in digital transformation projects

• How X Analytics addresses these, with examples across industries

• Strategies for building an X Analytics architecture that fills data, technical and organizational gaps