Analytics & Intelligence

The tools and techniques that turn raw data into vital insights, letting you explore information, convince others, and decide better.

Data is not knowledge. It takes insights to convert raw information into understandings you can act on.  On the Analytics & Intelligence stage, we’ll explore ways to unearth novel connections and discern unexpected patterns, applying the latest methods and technologies to the analysis of data. If you’re an analyst, reporter, trader, manager, or investigator, you’ll find tracks including:
 

Data Analytics

The first step in any business decision is analytics—the analysis of data, using statistics and comparisons to identify changes or patterns. Techniques include:

• Visualization, helping operators to perceive otherwise elusive patterns.
• Analysis techniques, including statistics, predictive modeling, data mining, correlation analysis, and optimization.
• How to set metrics that accurately reflect your business, and the key performance indicators (KPIs) that will keep it on track.
   

Decision Intelligence

Companies that use data-driven analytics instead of intuition have 5%-6% higher productivity and profits than competitors. When faced with a business decision, gathering intelligence to choose the right course is vital. In the Decision Intelligence track, we’ll look at:

• Next Best Action decision-making approaches.
• Human-in-the-loop computing for service and support.
• Data storytelling that turns numbers into narratives.
   

Applied AI and Machine Learning

While generative AI has taken the world by storm in recent months, machine learning has long been used to extract meaning from data. From expert systems that help navigate complex, multi-factor decisions to automatic classification to prediction, this track looks at how AI is being applied in data science today. It covers topics such as:

• AI fundamentals.
• Expert systems.
• Classifiers.
• Language models.
• Training and private data models.
• Real-world case studies of applied AI.

Business Strategy & Transformation

Navigating the game-changing possibilities of big data and AI, so you can build a team, draft a business case, and execute competitive strategies to reshape an industry in your favour.

Data science and AI are powerful new tools, but they only matter if you can fundamentally alter the value your business creates in a sustainable, repeatable way. The Business Strategy and Transformation stage offers three compelling tracks tailored to CEOs, founders, strategy officers, Human Resource professionals, product managers, CMOs, and CROs that will show you how to transform your business and reinvent your industry:
 

 Data Strategy

A strategy is simply a plan for how to win. And with data, the game has changed, creating entirely new strategies. To succeed, you need a clear vision of the outcomes you’re after, based on what’s now possible. Then you need to get buy-in and funding. This track covers topics such as:

• Winning in a data-driven world.
• Building a business case.
• Investment, funding, and ROI.
• Business transformation case studies.
   

Business Reinvention

Learn how to understand what’s now possible—and what’s obsolete—and change the way your organization functions. From competitive strategies to value chain disruption, this track is where you’ll learn to turn possibility into outcome, covering topics like:

• Turning strategy into results.
• Data-enabled value chain disruption.
• Driving cultural change.
   

Teams and Talent

Every technological change requires new skills—and makes others obsolete. To make the most of the data opportunity, you need the right team, and that means hiring, retaining, upskilling—and yes, rightsizing—your team. With AI there are new options, such as automation and human-machine collaboration. But how do you know your team is delivering the goods? In the teams and talent track we tackle topics such as:

• Hiring, replacing, and upskilling workers.
• Automation and chimeric work.
• Why diversity and inclusion are critical when building data products.
• Managing, measuring, and results.

Governance, Privacy, & Security

How to protect your organization, your users, and your data in an ever-changing legal landscape.

With great power comes great responsibility—and there are few technologies as powerful as Data Science and AI. This stage is for regulators, lawyers, compliance officers, infosec professionals. and CFOs tasked with safeguarding the organization and its customers. It includes three tracks packed with vital insight:
 

Governance and Compliance

Data is heavily regulated, and while the Internet knows no borders, companies must navigate complex legal jurisdictions or face significant, sometimes calamitous, penalties. Topics in this track include:

• Best practices for compliance.
• Quality management.
• Attribution, sources, and their compensation.
• Accuracy and liability.
• Governance frameworks.
   

Data Privacy

Sharing data among partners to reap its full potential while keeping personal information private is a constant challenge. Powerful algorithms can stitch together seemingly innocuous information that violates personal privacy, and how you prepare for a breach is often more important than how you respond to one. The data privacy track includes:

• Dealing with customer data.
• Anonymity and encryption.
• Privacy law and regulation.
• Breaches and responses. 
   

Information Security

As data becomes more vital, attackers are more motivated than ever to steal it. Big Data and AI present new attack surfaces, and generative AI can create novel zero-day exploits that evade traditional protections. In this track, we dive into the risks and emerging threats of a data-driven world with topics such as:

• Risk management, and the tradeoffs between perfect security and business agility.
• Identity, digital signing, verification, and impersonation.
• Threat detection, digital signatures, and heuristics.
• Algorithmic vulnerability.
• Data exploits and adversarial data tactics.

Engineering & Infrastructure

The foundational platforms and technologies on which data-driven, AI-enabled businesses operate, and how to keep them running fast and flawlessly.

They call it Big Data for a reason. Powerful processing, high-bandwidth networking, and massive storage—along with the teams who design, deploy, and automate those platforms—underpin any data strategy. This stage is ideal for technologists, developers, architects, open source contributors, CIOs, and CTOs.
 

Data Engineering

Working with data involves entirely new professions such as Data engineering, MLOps, and Attribution. Dive into these new fields with topics such as:

• What is data engineering, and why it matters.
• Data sourcing, curation, and acquiring the raw inputs for analysis and models.
• Batch versus streaming data, the unique challenges of both, and how to combine them.
   

Scale & Performance

Large-scale data lakes and data warehouses store petabytes of unstructured and structured data, while streaming platforms deliver thousands of data points every second that must be analyzed and categorized in real time. In this track, we look at squeezing every ounce of performance from the latest tools and projects:

• Increasing data velocity.
• Historical and real-time processing.
• DataOps and managing a fast data pipeline.
• Automation, elasticity, and on-demand computing resources.
   

Uptime & Resiliency

The best systems in the world can’t help if they’re offline. Data platforms must, above all, be reliable, which means maintaining backups, red-teaming disasters, and maintaining careful plans for recovery. In this track we’ll consider:

• The resilient data team.
• Backup, outage detection, and recovery.
• Crisis management and how to keep your cool in an emergency.
   

The Data Stack

The modern Data Stack consists of hardware, software, and tools, all working in concert to turn raw data into business advantage. In this track, we’ll look at each of these components in detail, including:

• Relational, graph, columnar, and NoSQL databases, streaming platforms, file systems, and more.
• Extract, Transform, and Load (ETL) technologies for batch and stream processing.
• High-capacity storage systems, including data warehouses, data lakes, data lakehouses, and more.
• Processing platforms such as Hadoop and Spark that can divide a huge task among massively parallel compute infrastructure.
• Analytics to help analysts query, visualize, and report insights intuitively.
• Tools to ensure compliance, including encryption, identity management, anonymization, data masking, and auditing.
• Monitoring systems that detect outages, maintain logs, trace problems, and identify gaps in information.
• Modeling tools including OLAP engines, BI tools, and analytical databases.

Data Ops tools used by practitioners for version control, change management, and orchestration.

Emerging Tech, Society, & Ethics

Cutting-edge startups, groundbreaking science, and how to mitigate the unintended consequences of innovation while keeping humanity and ethics at the core of progress.

While much of Data Science and AI is widely applied today, there remain vast opportunities for innovation. On the Emerging Tech, Society and Ethics stage we look at what we might build—and whether we should. The Emerging Tech stage is where you’ll hear from the investors and entrepreneurs making fringe science and early innovation into tomorrow’s next big thing. With a focus on the topics that investors, accelerators, startups, academics, ethicists, researchers, and government officials care about most, this stage includes three tracks that offer tantalizing glimpses into what the future might hold:
 

Generative AI

A few short years ago, we were in an AI Winter. But the launch of widely available generative AI, from GPT to Stable Diffusion and beyond, revitalized the field; now businesses around the world are clamoring to deploy it. Topics include:

• Images, video, and diffusion.
• Chat, agents, and Large Language Models.
• Coding, copilots, and assisted developers.
• Plugins, connections, and APIs.
   

Tech at the Fringe

Technology moves fast, and while we’re still building out our data and AI stacks, new tools are rapidly approaching. Distributed, decentralized data models help organizations that don’t trust one another coordinate their activity; augmented environments create new layers of data atop the physical world; quantum processors make some of computing’s hardest tasks trivial. In this track, guided by startup and VC perspectives, we look at the cutting edge of modern computing, including:

• Quantum computing.
• Augmented, virtual, and mixed reality.
• Blockchain and decentralized data.
• Human/machine (and machine-in-human) interfaces.
   

Ethical Algorithms

Few people worried about the impact of the mainframe on humanity. Even the personal computer was, at its inception, a niche product. But now that billions of humans spend a third of their lives online, it’s clear that technology isn’t agnostic. It has serious consequences for fairness, employment, democracy, and the nature of truth itself. In this track, we’ll look at the moral and social implications of our algorithmic, data-driven future, exploring how technology can help lift our species up while identifying the guardrails and guidelines we must adopt to avoid harm. Topics include:

• Responsible AI.
• Adversarial attacks and model poisoning.
• The societal impact of data biases, unintended consequences, and job insecurity.

MODERN ARCHITECTURES, DATA PRODUCTS, & AI

An exploration into the forefront of technological advancements shaping the landscape of data architecture as we know it. 

In today’s rapidly evolving data ecosystem, existing legacy solutions have been stretched by the scale and complexity of the present data volume and structure. New technologies, processes, and architectures dare to create the latest innovative practices resulting in scalable, resilient, and efficient data ecosystems. This stage is ideal for the doers of data looking to maximize architectural flexibility and incorporate innovative new ideas for a next generation data architecture - the data engineers, data architects, data scientists, developers, open source contributors, CIOs, and CTOs.

Modern Architectures

Gone are the days where Hadoop and Teradata can rest as cornerstones in your data ecosystem. By applying the principle of separating storage and compute, the data landscape has entirely changed - lending the way to modern architectures like the modern data stack, the data lakehouse, and more. In this track, you will learn about applications of data modernization such as:

• Implementing a data lakehouse
• The rise of modern table formats
• Building a data mesh for a hybrid architecture
• Data virtualization in the cloud

Data Products

Data products are a curated collection of purpose-built and reusable data sets with business-approved metadata designed to solve specific, targeted business problems. Learn how to move from data to insight faster and easier with data products by exploring track topics such as:

• Utilizing Open AI and data products to deliver real-time insights
• Revolutionizing data quality management with machine learning
• Empowering data ownership through the implementation of data products

Generative AI

There’s no doubt most organizations are increasing their investments in Generative AI such as ChatGPT and recognize its potential to revolutionize productivity and customer experience. Join this track to strategize which Generative AI dreams are worth chasing, what foundations you need to build to leverage AI, and how to take advantage of these AI developments while avoiding major disasters. You’ll hear from industry experts about topics such as:

• Testing and deploying Generative AI Solutions
• Leveraging Generative AI toward increased operational efficiency
• How AI could fail you—and how to make sure it doesn't