Name: Streaming Analytics for No-Code, Low-Code or Loadsa-Code
Start: 2022-07-12T20:00:00Z
End: 2022-07-12T20:00:32.000Z
Location: BrightTALK
Rating: 5

KX is the maker of kdb+ the world’s fastest time series database and analytics engine. Built for the world’s most demanding data environments, our industry-leading software can process and analyze time series and relational data at unmatched speed and scale for richer insights that drive better business decisions. Capable of running on any cloud, on prem, or at the edge, KX empowers developers and data engineers to build high-performance data-driven applications and turbo-charge their favorite AI and ML analytics tools. KX is trusted by the world’s leading companies across a range of sectors including capital markets, healthcare, manufacturing and telecommunications to deliver business value including risk mitigation, enhanced regulatory compliance, increased machine uptime and rapid development and launch of new products and services. For more information visit www.kx.com.

Data may be plentiful in capital markets but clarity is often in short supply.

In our latest episode of Data in the AI Era, Peter Finter sat down with Cat Turley, CEO and founder of ExeQution Analytics, to talk about the future of trading analytics and what it takes to turn data into action when speed, trust, and transparency are non-negotiable.

From her early days building analytics platforms at multinational banks to her current role helping firms make smarter use of high-cost data, Cat offers hard-won insights into the human, technological, and cultural forces shaping the front office. Cat’s message is clear: in capital markets, data isn’t the product. Understanding is. And too often, there’s a gap between the two.

Data in the AI Era - Episode 5 -  The future of trading analytics and getting more from your data

In this episode of Data in the AI Era, Peter Finter is joined by Ashok Reddy, our CEO, to unpack what it really means to compete in the real-time economy, and why most capital markets firms are still held back by infrastructure that can’t scale. Ashok explains how firms are redefining speed and adaptability, and why temporal AI may be the key to building systems that perceive, reason, and act across time.

Data in the AI Era - Episode 4 - Trading in time: Why temporal AI is the next frontier for capital markets

In the latest episode of Data in the AI era, host Peter Finter sits down with Heidi Lanford, a seasoned data and analytics leader with experience driving AI adoption at organizations like Fitch Group and Red Hat. Now a co-founder at NavAlytix AI, Heidi brings fresh insights into why AI projects succeed or fail and how businesses can overcome common obstacles to unlock true AI-driven transformation.

AI is often positioned as a purely technical challenge—one that requires better algorithms, larger datasets, and more computing power. However, Heidi argues that these elements, while important, are not the primary barriers to success. Instead, the real obstacles lie in leadership buy-in, cross-functional collaboration, and the ability to drive cultural shifts within an organization. Without these factors, even the most advanced AI solutions can struggle to gain traction or deliver meaningful business outcomes

Data in the AI Era - Episode 3 - AI success isn’t about technology — it’s about leadership

In this episode of Data in the AI era, host Peter Finter sits down with Time Magazine technology pioneer Mark Palmer to explore how capital markets firms are navigating the AI revolution. They discuss the practical applications of generative AI, the challenges of integrating AI into a highly regulated industry, and how AI can enhance—rather than replace—human decision-making.

Data in the AI Era - Episode 2 - Can GenAI drive real value in capital markets?

In the first episode of Data in the AI Era, Erin Stanton shares her expertise on navigating the challenges of data readiness, strategy, and management in today’s AI-driven landscape. Drawing on over 15 years of experience, she reflects on the evolution of her organization’s data strategy and the intentional steps taken to ensure their data supports scalable, high-impact AI initiatives.

Erin discusses how to prepare data for AI through transparency, disciplined processes, and a mindset open to learning from failure. From launching monthly ‘Machine Learning Mondays’ to hand-curating datasets, her team has fostered a culture grounded in collaboration and precision. Her insights demonstrate how a well-crafted data strategy can help organizations unlock AI’s potential while staying aligned with business goals and managing risk.

Data in the AI Era - Episode 1 - Redefining data readiness for AI success

What does it take to stay ahead in crypto’s high-stakes, high-speed environment? 

In a recent webcast, Nick Shindo, Business Development Director for Japan at KX hosted a conversation with Jad Sarmo, Head of Quantitative Development at B2C2, one of the world’s top institutional liquidity providers in digital assets. Together, they explored what it means to operate effectively in a market that never stops moving.

Mastering Crypto Data - Building analytics infrastructure for fragmentation, volatility and real-time edge

In a rapidly evolving landscape, trading research continues to grow in significance. As trading shifts towards more dynamic, learning-based models, understanding and measuring the incremental value of trader decisions, strategy behaviour and model outputs becomes even more critical. Effective trading research should not only explain what happened but delve deeper to explore the reasons driving this behaviour and identify actionable insights to improve future trading decisions.  

In this 45-minute interactive virtual workshop, we invite industry peers from both buy and sell side financial institutions to join us in a discussion, including a live analysis demo and Q&A session with industry experts and market practitioners, on how to go beyond measure & interpret your data to address real-world challenges in trading research and execution performance.

Optimize Your Execution: Trading Research Best Practice

Faster Data. Smarter Decisions. Stronger Outcomes.
The speed and precision of real-time analytics in capital markets are no longer just a competitive advantage—they’re essential for survival. From algorithmic trading and risk management to dynamic pricing and client trust-building, real-time data drives success across the financial world.

In this 40- minute webinar you’ll:
- Learn about the real-time data trends reshaping capital markets.
- Explore how generative AI, temporal analytics, and scalable data solutions are being used.
- Learn how industry leaders are managing billions (even trillions) of data points with groundbreaking technologies like KX.
- Gain actionable strategies to modernize legacy infrastructure without disruption.

Discover how firms like JP Morgan and leading hedge funds are transforming their operations with real-time analytics.

Real Time Insights within Capital Markets

KX and BestX join forces to discuss the many tech, regulatory, and market factors that are driving change within Best Execution Analytics. Topics covered include the adoption of the cloud, regulatory changes such as MIFID2 and RG97, and process automation.

As BestX co-founder Ollie Jerome shares: “A lot of our very large clients have gone through a similar process of management change over the last two or three years where now you have folks from an Equity background managing the cross-asset desks, including Fixed Income and FX.”

BestX is a 7-year-old technology company that partners with KX. They deliver Best Execution Analytics to help over 30 banks and almost 180 of the world's largest hedge funds asset managers clients to define, achieve and record a process of Best Execution across FX, Fixed Income, Equities, Commodities, Futures, and Crypto. They currently represent about $45 trillion of AUM (Assets Under Management).

Innovation, Change and opportunities in the world of Best Execution Analytics

“Three Innovative Quant Trading Apps and Three Ideas Everyone Should Steal”, is an interactive exploration of three innovative applications on Wall Street that leverage high-frequency data in creative ways for sense-and-respond intelligent action (trade execution), live-streaming research and strategy (for intraday trade strategy adjustment), and continuous risk management. While these use cases may seem particular to the capital markets, they exist everywhere.

This one-hour session with Conor Twomey (KX Head of AI Strategy) and Mark Palmer (Time Magazine Technology Pioneer) will explore these innovative use cases and explain three ideas everyone should steal in any application area dealing with streaming and historical data, and high-value decision-making in real-time.

Three Innovative Quant Trading Apps and Three Ideas Everyone Should Steal

Quants develop – and help implement - digital transformation within firms. These ideas can change, or even revolutionize, their organization across functions such as trading and risk management. Whatever your area of focus, today’s market unpredictability presents excitement, opportunities, and challenges for quants including: 
• Market volatility driving high volumes of data
• A model building ecosystem centered on the Python programming language
• The increasing use of machine learning
• The rise of cloud and microservices

Join Conan Hales, CFA, CQF, Solutions Engineer, KX and Harry Darell Brown, VP of Product Management for Financial Solutions, KX to explore the challenges and opportunities of Quantitative Research in 2022.

Referencing real-world examples, our speakers will discuss: 
• Why KX insights is the ideal platform for time series data analysis and model 
        development using languages like Python.
• How KX is positioned for the world of streaming data.
• A case study of how KX enabled an exchange to commercialize their unique dataset.

Register today and discover the future of quantitative research.

Quantitative Research Tooling for High Volume Financial Markets  (APAC)

Using Python, and perhaps Pandas? This webinar explains how you can provide production data workloads to your Python code and Jupyter Notebooks, adding 8x to 100x more data power to your models and reducing the time needed to take research into production.

This webinar explores how to: 
• build a data ingestion pipeline
• dramatically transform Python time series workflows and analytics (asof joins; xbar for time series formatting)
• augment and improve your data science in Jupyter Notebook using KX's Python Interface

Watch this webinar to see a demonstration of a computational finance workflow and to understand the same rigor and capabilities that can be deployed to any use case that utilizes Python and highly granular and high-frequency datasets.

kdb Microservices for Ultra-High Velocities on a Postage Stamp Footprint

With decades of combined experience at the sharp end of the Financial Services sector, the panelists discuss the various requirements that make up the perfect platform for trading analytics. In more detail, they’ll explore how a trading analytics solution should:
• Optimize trading outcomes
• Be futureproof
• Deliver Best Execution

The team discuss the opportunities for optimization and its dependency on finding value from multiple data volumes - and how repeatable any optimizations are. The panel also debate benchmarking, data management, application development, data pipelines, transparency, and more through real-world examples. We'll also have a deeper dive into how TCA can be utilized as a tool to help traders and data scientists better understand how their trades performed and can inform strategies for improvement.

Time Series Analytics - Powering Trading Outcomes (APAC)

When it comes to business initiatives driving technology spend, 1 in 3 organizations state that their top initiative over the next 12 months is to improve their data analytics for real-time business intelligence (Enterprise Strategy Group).  

This is largely driven by the fact that when using data to make informed business decisions, it is no longer enough to rely on business intelligence derived solely from historic (big) data. Instead, to keep pace in the digital era, insight must be combined - even supplanted - by continuous real-time data analytics.  

Taking advantage of real-time analytics, however, is not so straightforward. Data is growing, distributed data environments are increasingly dynamic, and with so many vendors and tools to choose from, organizations need help. 

In this discussion, ESG Senior Analyst Mike Leone is joined by Conor Twomey, Head of Customer Success at KX and Kiran Singh, Data Analytics & Data Science Leader at NextEra Energy, to discuss in-depth how organizations can find clarity from the chaos.  

Join them to learn about: 
- The business objectives driving the need for faster decision making through streaming analytics 
- The common challenges preventing success and the capabilities that matter most in streaming analytics platforms 
- What to look for in a modern data platform and how to determine if it is right for your business 
- How the cloud is supporting real-time data initiatives across different industries 
- A customer case study and common use cases for analytics that are delivering proven value for organizations 
- And more

A New Data Frontier: The Value of Streaming Analytics to a Data-first Business

Time Series Analytics - Powering Trading Outcomes

This discussion between Vats Vana, Vice President of IoT Strategy & Products at KX, and guest speaker Paul Miller, Vice President, and Principal Analyst at Forrester, covers several topical issues in Manufacturing, including:

• The catalysts for change – including a need for deeper analytics from ever-evolving Edge communication.
• Trends and challenges in the industrial IoT space.
• The value of contextual data and the importance of understanding how timely insights need to be.
• The connection between insights, decisions, actions, and outcomes.
• The measurable ROI of data analytics from Manufacturing IoT
 
Calling upon decades of experience, Vats and Paul examine the relationships and conflicts between tech, data, people, and processes, the cultural gap between OT and IT, and attempt to establish whether real-time is the right time. 

Through case studies and real-world examples, Vats will lead the discussion on the value of ‘context’ and how rich insights help drive the decisions, actions, outcomes, and change needed for modern manufacturers to survive and thrive.

The Value of Timely Data in Modern Manufacturing

In a world where the smallest margins represent millions of dollars, attempts to manipulate the market are inevitable. And with few regulations governing this “new” asset class, Cryptocurrency markets are no exception and are considered particularly vulnerable to hacking, fraud, money laundering, and other criminal activity. 
 
Justin Handsman, FICC & Derivatives Specialist, KX, and Charles Gregory, VP of Product Management for Surveillance Solutions, join host Steve Wilcockson, KX to explore the challenges, opportunities, and nuances of Trade Surveillance across multiple markets.  
 
Referencing real-world examples, the team discusses:
• The similarities and differences between traditional and digital exchanges, from the exchange and trader point of view.
• Impact of data volume and the difficulties of building the “perfect picture” - compounded by market depth challenges.
• The importance of real-time transactional visibility.

Trade Surveillance in Equities and Crypto Markets

A monolithic approach to building systems rarely succeeds.  And yet there is clear value in having knowledge workers access data across an enterprise.  The Data Mesh concept put forth by Dehghani proposes a divide and conquer approach to tackling monolithic complexity by aligning implementation to the idea of domains.  In this workshop we will discuss an engineering approach for Data Mesh deployment that provides a practical architecture and governance framework to deliver connectivity across domains while encouraging a decentralized approach to stewardship and implementation. 

Learn how to accelerate delivery of data products using a domain-driven design approach.
Learn about best practices in data engineering aligned to a domain-driven approach to delivery of data products.
Learn how to deliver using autonomous, decentralized teams while avoiding anarchy in architecture and deployment.

An Engineering Approach to Implementation of the Data Mesh Concept

The modern data architecture is all about scale, allowing organizations to store and utilize high volumes of data. There are 2 components to realizing this scale: (1) Building an architecture with the right tools; (2) Building a team that can take proper advantage of these tools. This session will explain how you can achieve both. 

We’ll cover the key components of successful modern data architectures, along with how successful organizations are utilizing these components to achieve democratization and scale insights within their organizations.

• See examples of modern data architectures on the major cloud platforms, including AWS, GCP, Azure, Snowflake, and Databricks
• Learn tips and tricks for using modern data platforms to their full potential by scaling people, processes, and processing

Hear success stories from organizations that have successfully modernized their data warehouses

Bio: Nike Start is a Sr Sales Engineer for Asia Pacific & Japan region focused on helping companies transition to the modern stack. He has 10 years of experience engineering, analyzing, visualizing data across multiple verticals. When he’s not working with data he enjoys traveling, surfing, and spending time with his wife and 2 dogs.

LinkedIn Profile: https://www.linkedin.com/in/nikestart/
Location: Los Angeles, California – USA

How to Scale Insights With Your Modern Data Warehouse Architecture

Why do customers churn? Why is revenue down? What are my top channels to acquire new customers? Why is my business losing more orders in rural areas?

The answer is in your data.

As we move forward in modernizing the traditional data stack, companies are collecting more and more data.  Getting through all the data is a growing challenge.  Operationalized ML powered analytics can make analysts more efficient and eliminate rote data science work. Data teams need the ability to rapidly process cloud-scale data, separating signal from noise with pre-built and operationalized AI/ML tools.

Join this webinar to learn the basics of how to:

-Rapidly sift through data (in seconds)
-Determine key drivers of metric change
-Leverage artificial intelligence and machine learning in your data stack

Analysts vs. Machines: AI/ML for cloud-scale data

According to a recent Gartner study, it is estimated that 85% of big data projects fail. Main reason for this failure being that datasets significantly grow over time n size and get more complex; thus, the traditional BI solutions can’t keep pace to manage them. Augmented Analytics has proven to be the best solution for the dilemma. By utilizing AI and Machine Learning capabilities to BI solutions you can gather precise appropriate data, achieve comprehensible information patterns, perfect the data analysis models and anticipate better BI insights.

In this session we will learn how AI is helping the BI industry to transform into a better, more insightful data representations with more accurate predictions and recommendations. Using multiple AI elements, Augmented Analytics are revolutionizing the BI industry through the power of AI and Machine Learning. It is what every industry in any sector needs today to get the best out of Big Data and BI.

Takeaways:

What are Augmented Analytics and who are they for?
The benefits of adopting Augmented Analytics for BI
The AI elements for Augmented Analytics
Augmented Analytics challenges
Best practices and examples of utilization

Augmented Analytics: Discover How AI is Revolutionizing the BI Industry

Data has become the fuel that powers the Artificial Intelligence ecosystem. Global enterprises continue to face hurdles in training, development and testing of AI/ML models by Dev & Test teams, Data Scientists and MLOps teams: (a) access to data and sharing data is impeded by restrictions and regulations, (b) retention and storage of raw data is expensive and, (c) sparseness and bias in data. Gartner predicts that by 2024, 60% of data used for the development of AI and analytics projects will be synthetically generated. In this webinar, we will discuss how synthetic data can help maximize the usefulness of data by safely sharing it with whom, when and what. We will describe how recent advances in AI Generative Adversarial Networks (GANs) models can be used with structured, time series, incident log and session journey data effectively to create high-fidelity synthetic data that addresses privacy challenges.

Powering AI/ML Operational Innovation with Generative Synthetic Data

Orchestrating data science and machine learning in an industrial setting is difficult. The data landscape is challenging, littered with complex problems and without the necessary freedom to design experiments or create observational studies on real-world processes. On top of that, you must manage stakeholders, operate cloud technologies, write software or wrap your solution into a product required to run predictions 24/7/365 – all while supporting business operations! With all this on your plate, you will need strategies to successfully incorporate MLOps in your organisation. In this talk, we will explore:


How to “bootstrap” ML Engineering (MLEng) and MLOps practices in your organisation.
Different ways to organize your MLOps teams to create optimum value.
Where to start on your MLOps journey.
How to avoid some common “gotchas” of starting out in MLOps.

Just Build It! How to Make MLOps a Reality In Your Organisation

Many organizations are rightly focusing on making sure that their data is being managed, governed, and quality controlled. While this is necessary, it is not enough to add value to the business. For this investment to pay off, the data has to be used to drive decisions. This is easier said than done, and requires careful planning upfront to ensure that your architecture is built in a way that will be able to do more than reporting. I will discuss what steps you need to take before you go too far in your data journey.

Key Takeaways:
Learn what is needed for your data investment to pay off.
What are some common mistakes?
What to watch for in the coming years?

Building a Data Architecture that Adds Value to Your Business

Cloud data warehouses have fundamentally changed the way businesses capture and store data, making more complete and complex analysis possible than ever before. While most aspects of the modern data stack have continued to evolve, reporting and business intelligence tools haven’t delivered new ways to explore data and uncover ‘why’ a key metric changed.

It’s time to augment manual reporting and BI workflows. Decision intelligence allows data analysts to leverage the power of machine learning and AI to quickly and comprehensively analyze cloud-scale data—uncovering statistically relevant factors impacting metric change. By using decision intelligence along with BI workflows, data analysts can fully leverage the high-volume, high-dimensional data to quickly answer ‘why’ KPIs change and guide decision-making.

In this webinar you’ll learn:

-Industry trends driving the next wave of data and analytics—decision intelligence—forward 
-Data analyst workflows for using Sisu with BI tools, specifically highlighting using LookML to create datasets for Sisu
-Real-world examples of companies improving decision-making with decision intelligence

Augmenting BI tools with decision intelligence to improve decision-making

The increasing adoption of multicloud is leading to business-critical data getting more siloed. This makes it difficult and time-consuming for data engineers, architects, and analysts to quickly gain value from data. BigQuery Omni connects to various cloud data sources and provides a single pane of glass to access the data using SQL – like queries without leaving the interface. Looker allows you to explore, share, and visualize your company’s data to make better business decisions. In this session, you’ll see a demo of how to break down data silos using BigQuery Omni and Looker to securely and cost effectively analyze and visualize data across clouds.

Cross-cloud analytics and visualization with BigQuery Omni and Looker

Data teams are challenged with an explosion of data, and increased demand for analytic insights from a wide range of data consumers. Cloud data lakes represent the first destination for the newest and fastest-growing sources of customer and operational data, but making that data accessible for Business Intelligence and reporting workloads typically requires building and maintaining complex, manual, and ad hoc ETL processes and proliferating data copies. 

In this session, learn how an open lakehouse architecture simplifies and streamlines access to the data lake, and enables self-service analytics for technical and non-technical data consumers.

Accelerate Time to Insight with an Open Lakehouse Architecture

Advanced Big Data Analytics for the Data-Driven Business

Whatever your preferred form of analytics, interface preferences, coding skills, or architecture – cloud, on-premises or hybrid - handling more data more simply is in easy reach. In this session, we will explore, with examples from multiple industries, 3 simple steps data scientists, analysts and their teams can take to run powerful, scalable real-time analytics that reduce compute costs, data complexity, and risks. They include: 

• simplifying data management and storage processes, including real-time data. 
• automating and integrating continuous intelligence that combines new and historical data. 
• customizing code and visualization to drive faster, more useful decisions, commensurate with your organization’s bespoke needs and unique culture.

Streaming Analytics for No-Code, Low-Code or Loadsa-Code

Real Time Analytics

Streaming Data

Big Data Analytics

Big Data

Big Data Storage

BigQuery

Cloud Analytics

Data Analytics

Data Architecture

Cloud Architecture

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Cloud Computing

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Application Development

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Business Intelligence and Analytics

Get powerful marketing data and analytics insights from influential experts. Connect with thought leaders and colleagues to get the most up-to-date knowledge on the analytics that quantify your investments.

Data and Analytics

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Fintech

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Streaming Analytics for No-Code, Low-Code or Loadsa-Code

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KX