Quality, accuracy, and relevance are key to collecting and managing data. However, this paradigm is hard to achieve in an environment in which businesses are constantly forced to adopt new technology, thus creating a gap between the priorities of data professionals and business decision-makers.
Innovation takes flight via emerging technologies that promise to make data operations more efficient. C-suites and data leaders to need work together to ensure that priorities are consistent, and the data foundation is stable.
Leaders in data science take the stage at the Data Analytics Leadership Forum on July 13th to discuss how to improve existing data strategy to future-proof your infrastructure.
Network and learn from industry leaders to discover:
If the past year and a half has taught us anything, we have learned that it is crucial to prepare for the unexpected. Organizations have relied more heavily in recent years on data science teams to maximize on opportunities that will affect their bottom line. Because of this demand, the role of the data analyst is constantly evolving.
In this session, Chris Hutchins, VP, Chief Data & Analytics Officer, Northwell Health, will guide data executives to learn how to lead through constant disruption, and on ways to improve data quality and governance.
VP, Chief Data & Analytics Officer
Companies everywhere are struggling to manage vast amounts of data at scale. With no shortage of valuable information, data practitioners must extract value from surmounting amounts of data, and work in tandem with key decision makers to make the most of insights. To keep up with demand, data leaders have developed an operational mindset to better manage more complex data infrastructures.
In this discussion, industry leaders weigh in to analyze why developing a DataOps mindset can improve business intelligence and to enhance the quality and reliability of your company’s data.
AVP - Enterprise Data & Analytics Leader
SVP, Associate Director - Enterprise Data Governance
Former VP HBO Max Content Intelligence & Metadata
Director, Data & Analytics, IT Strategy & Services
With the development of advanced Embedding theories, Deep Learning Neural Network models have achieved higher and higher model prediction accuracy than ever before. As a result, improved Deep Learning Neural Network models begin to find more and more important applications in AI Automation to improve operational efficiency and reduce costs for most companies.
In this discussion, James Mou, PhD., explores the potential of speech recognition models to convert voice data to text and extract useful information for predictive modeling and other business use cases.
Director of Data Science/Principal AI Machine Learning Research Scientist
The future of the workplace is automated. AI specialists have a larger role in the IT world than ever before as business leaders are incorporating machine learning solutions into every angle the business.
Operationalizing AI strategies can lead to accelerated growth for an organization if executed properly. In this discussion, AI experts share their strategies to fast-track machine learning technology model deployment to increase productivity and performance within their organization.
Vice President, Artificial Intelligence, Data, & Analytics
Executive Director, Data Science & Analytics
Vice President of Product: Machine Learning & AI
Adjunct Faculty Member
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