Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Call for speakers

Call closes 23:59 — 21 November 2017 GMT.

Submit a proposal

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.

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 23:59 GMT 21 November.

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, and “humans-in-the-loop” machine learning systems.
  • A deep dive into popular tools and frameworks for prototyping, developing, managing, and deploying data science projects into production.
  • Data sets and data sources: including prepping, cleaning, organizing and augmenting data for analysis, and the creation of training data.
  • Domain specific datasets, tools, and techniques (data science in marketing, security, advertising, finance, HR, etc.)
  • Advanced analytics for specific data types and sources including time-series and event data, images, and unstructured text.
  • How tools and technologies from Artificial Intelligence are influencing data science and business strategy.

Data engineering and architecture

  • Introduction and/or deep dive into popular frameworks, tools, and platforms including Hadoop, Spark, Kafka, and managed services in the cloud.
  • The emerging compute infrastructure for data science (considerations include: scale, latency, throughput, query performance, availability, cost, automation, encryption).
  • Architecting data platforms, data applications and data products (on-premise, cloud, and hybrid architectures)
  • Real-world case studies of big 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.
  • Sessions on how to transition from a software engineer or a data scientist into a machine learning engineer
  • Managing data engineering teams and projects

Streaming systems and real-time applications

Data collected and generated by things—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.

Big data and data science in the cloud

Emerging technologies and case studies, particularly around finance, transportation, logistics, marketing, human resources, and business management

How new data science, machine learning, and big data approaches are changing a particular vertical.

  • Predictive programming
  • Detecting fraud
  • Analytics, learning, and custom content creation
  • Optimizing ad returns with audience information
  • Gaming analytics and measuring engagement

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
  • Augmented and virtual reality
  • Design, interactivity, and visualization
  • Designing for interruption and contextual interfaces
  • User experience and data-driven design

Platform Security and Cybersecurity

Data needs tools like encryption for security and privacy; increasingly, data and algorithms can improve our collective security regime. But security teams are in a constant race with adversaries who try to game those algorithms.

  • Data governance
  • The role of data in better security
  • Securing execution (of machine learning and analytics)
  • Adversarial information and bad actors

Law, ethics, governance

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
  • Getting ready for GDPR
  • Open, public, and government data
  • Fairness, accountability, and transparency in data science and machine learning

Data-driven business management

Case studies, 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

Required information

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

  • Proposed title
  • Description of the presentation
  • Suggested main topic
  • 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)
  • 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.

Proposals will be considered for the following types of presentations:

  • 40-minute session
  • 3-hour tutorial

Tips for submitting a successful proposal

Help us understand why your presentation is the right one for Strata Data Conference. 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, regardless of race, gender, gender identity and expression, age, sexual orientation, disability, physical appearance, national origin, ethnicity, religion, or political affiliation.

  • 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.
  • Keep proposals free of marketing and sales.
  • 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 21 November
  • All proposers notified by January 2018
  • Registration opens in January 2018

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, religion, or political affiliation. Read more »

Create a proposal