Sep 23–26, 2019

Call closed 11:59pm 03/12/2019 EDT.

Do you have a great idea to share?

Strata brings together the world’s data experts and innovators, and we invite you to be a part of the program. This is a key opportunity to share how the strategic use of data can shape the future of both business and technology.

If you have a success story, cautionary tale, best practice, or compelling vision you can tell in a no-nonsense, pitch-free way—here’s a chance to present at one of the largest annual gatherings in technology and business.

Proposals will be considered in two categories: Technical and Non-technical. You’ll be asked to choose one of these categories when you submit your proposal.

The topics below are guidelines and suggestions—but we love to be surprised. If you need some pointers, see our tips on how to submit a great proposal. The deadline for submissions is 11:59 pm ET March 12, 2019.

Technical (for practitioners such as developers, data scientists, machine learning engineers, and researchers)

Data science and machine learning

Data science and machine learning are enabling layers that will impact organizations and businesses in the years to come.

  • Data science fundamentals (including statistics and machine learning)
  • Interesting use cases and case studies
  • Managing data science teams and projects
  • The latest methods and algorithms from statistics and machine learning, including deep learning, XGBoost, Bayesian machine learning, distributed training, and “human-in-the-loop” machine learning systems.
  • Adversarial and Secure machine learning
  • A deep dive into popular tools and frameworks for prototyping and developing machine learning and data science products (open source projects, commercial software, managed services, and AutoML).
  • Domain specific datasets, tools, and techniques (data science in marketing, security, advertising, finance, HR, etc.)
  • Advanced analytics for specific data types and sources including temporal data, geospatial data, images, and text.
  • Tools for privacy-preserving analytics including differential privacy, federated learning, and homomorphic encryption.
  • Ethics: model fairness, transparency, interpretability, and explainability.
  • How tools and technologies from Artificial Intelligence are influencing data science and business strategy.

Streaming and IoT

Data collected and generated by sensors and devices—including the difficulties of storing, analyzing, and publishing such information; and the challenges of extracting understandable, meaningful insights from the resulting torrent.

  • Open data standards and interoperability
  • Introduction and/or deep dive into popular data ingestion, stream processing frameworks, storage engines, and analytic tools, including managed services in the cloud.
  • Architecting end-to-end streaming platforms and applications
  • Real-time applications: case studies and lesson learned
  • New applications involving data gathered from IoT, aerial imaging, machine data, and other sensors.
  • Analytics: from simple counts, to anomalies, correlations, forecasts, and online learning.
  • Scaling machine learning using model compression, federation, and pushing more computation towards edge devices.

Data engineering and architecture

  • Managing data engineering teams and projects
  • Introduction and/or deep dive into popular frameworks, tools, and platforms including Hadoop, Spark, Kafka, Arrow, Pulsar, and managed services in the cloud.
  • The emerging compute infrastructure for data science and machine learning (considerations include: security & privacy, scale, latency, throughput, query performance, availability, cost, automation, encryption, reproducibility, collaboration).
  • Hardware acceleration (compute and I/O)
  • Architecting data platforms, data applications and data products (on-premise, cloud, and hybrid architectures)
  • Data sets and data sources: including prepping, cleaning, organizing and augmenting data for analysis, the creation of training data, and data exchanges.
  • Data storage and data management technologies.
  • Data security and privacy technologies.
  • ETL and modern data integration, including designing, building, and managing data pipelines.
  • Best practices and tools for machine learning model lifecycle management and for managing risks in machine learning.
  • Metadata and data governance strategies.
  • Real-world case studies of data technologies in action, from disruptive startups to industry giants.
  • Enterprise adoption: how organizations are making the move from legacy data stores to big data, and the best practices—and roadblocks—to becoming a data-driven organization.
  • The role of decentralization technologies (including blockchains) in modern data products.

Business Analytics and Visualization

  • Data visualization: tools and best practices
  • Scalable and real-time business intelligence

Automation in data science and data

  • AutoML: automation tools and methods for model building, data preparation, feature engineering, and other aspects of machine learning
  • Automation tools for managing data and security infrastructure

Security and Privacy

Recent regulations in Europe (GDPR) and California (Consumer Privacy Act) have placed concepts like “user control” and “privacy-by-design” at the forefront for companies wanting to use analytics and machine learning. The good news is that there are new privacy-preserving tools and techniques – including differential privacy – that are becoming available for both business intelligence and ML applications.

  • Data security and privacy
  • The use of data, analytics, and machine learning in security and cybersecurity.
  • Privacy-preserving analytics.
  • Secure and robust analytics, including secure machine learning and aspects of machine deception (such as machines deceiving machines, or people deceiving machines).

Non-technical (for executives and strategic decision-makers with responsibilities in technology, marketing, finance, research and development, and general management)

Case Studies

  • Overview and tour through specific use cases, including approaches taken, and the benefits—and drawbacks—of solutions.
  • We are interested in a wide range of industries including (but not limited to) technology, media/marketing/advertising, healthcare, biotech, education, retail and e-commerce, transportation and logistics, and financial services.

Executive Briefings

and tutorials on how to build strategies and data-driven business models that deliver customer insight, drive efficiency and innovation in products & services, modernize architecture, reduce costs, and lower risk.

  • Strategies and business models that deliver customer insight.
  • Using data to increase efficiency and innovation.
  • Modernizing architecture.
  • Cultural change and organizational adoption of data.
  • An overview of the utility and scope of data technologies and analytic methods.

Culture and Organization

  • Organizing and managing data science teams and projects.
  • Data-driven digital transformation.
  • Democratizing data and analytics (including internal training programs and data academies).

Visualization and user experience

Data doesn’t matter if it doesn’t produce outcomes. This track tackles augmentation, user experience, new interfaces, interactivity, and visualization.

  • Analytics and reporting.
  • Data-driven digital transformation.
  • Democratizing data and analytics (including internal training programs and data academies).

Law and Ethics

Open data and heightened privacy concerns mean new, and often controversial, thinking on governance, ethics, and compliance, as well as a renegotiation of the pact we make with an increasingly digitalized life lived, often in public.

  • The impact of data technology on society
  • Privacy, confidentiality, and data protection
  • Data (privacy) regulations including GDPR and the California Consumer Privacy Act.
  • Fairness, accountability, and transparency in data science and machine learning
  • Best practices for managing risks in machine learning

Required information

You’ll be asked to include the following information for your proposal:

  • Proposed title
  • Description of the presentation
  • Suggested primary topic in either the Technical or Non-technical category
  • Audience information:
    • Who is the presentation is for?
    • What will they be able to take away?
    • What prerequisite knowledge do they need?
  • For tutorial proposals: hardware installation, materials, and/or downloads attendees will need in advance
  • Speaker(s): biography and hi-res headshot (minimum 1400 pixels wide; required). Check out our guidelines for capturing a great portrait.
  • A video of the speaker (required).
  • Reimbursement needs for travel or other conference-related expenses (if you are self-employed, for example) Note: If your proposal is accepted and you are traveling internationally, we can provide a formal invitation letter upon request.
  • Proposal length: either a 40-minute session or 3 hour tutorial

Tips for submitting a successful proposal

Help us understand why your presentation is the right one for Strata. Please keep in mind that this event is by and for professionals. All speakers must adhere to our Code of Conduct. Please be sure that your presentation, including all supporting materials and informal commentary, is welcoming and respectful to all participants.

  • Keep proposals free of marketing and sales pitches.
  • Pick the right topic for your talk to be sure it gets in front of the right program committee members.
  • Be authentic. Your peers need original ideas in real-world scenarios, relevant examples, and knowledge transfer.
  • Give your proposal a simple and straightforward title.
  • Include as much detail about the presentation as possible.
  • If you are proposing a panel, tell us who else would be on it.
  • If you are not the speaker, provide the contact information of the person you’re suggesting. We tend to ignore proposals submitted by PR agencies and require that we can reach the suggested participant directly. Improve the proposal’s chances of being accepted by working closely with the presenter(s) to write a jargon-free proposal that contains clear value for attendees.
  • Keep the audience in mind: they’re professional, and already pretty smart.
  • Limit the scope: in 40 minutes, you won’t be able to cover Everything about Framework X. Instead, pick a useful aspect, or a particular technique, or walk through a simple program.
  • Explain why people will want to attend and what they’ll take away from it
  • Don’t assume that your company’s name buys you credibility. If you’re talking about something important that you have specific knowledge of because of what your company does, spell that out in the description.
  • Does your presentation have the participation of a woman, person of color, or member of another group often underrepresented at tech conferences? Diversity is one of the factors we seriously consider when reviewing proposals as we seek to broaden our speaker roster.

Other resources to help write your proposals

Important dates:

  • Call for speakers closes on March 12, 2019
  • All proposers notified by May 2019
  • Registration opens in May 2019

Code of conduct

All participants, including speakers, must follow our Code of Conduct, the core of which is this: an O’Reilly conference should be a safe and productive environment for everyone. Please be sure that your presentation, including all supporting materials and informal commentary, is welcoming and respectful to all participants, regardless of race, gender, gender identity and expression, age, sexual orientation, disability, physical appearance, national origin, ethnicity, or religion. Read more »

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