B23 Announces B23 Data Platform integration with the Google Kubernetes Engine (“GKE”)

Today we are excited to announce our B23 Data Platform integration with the Google Kubernetes Engine (“GKE”).   Kubernetes is an exciting technology that helps customers orchestrate complex containerized workloads using templatized configurations.  In many ways, Kubernetes is an extension of the same automation and orchestration concepts we started developing with cloud-based virtual machines five years ago when we introduced the B23 Data Platform.   That’s why it made perfect sense to enhance our existing data platform offerings with multiple cloud-vendor Kubernetes services which will extend our data engineering and applied machine learning workloads to even more environments.   B23 has been “productionizing” the difficult and non-differentiated data engineering activities for Fortune 20 companies, financial services, large cybersecurity, leading telecommunications providers, and many other firms for several years.   This integration will make it easy to service more customers who prefer Google Cloud Platform (“GCP”) as they ask B23 to build, manage, and operate complex data pipelines.   This video shows a brief overview of how we have simplified the process to extend customer machine learning workloads onto GKE.   https://www.youtube.com/watch?v=cwiW0JEe8Lc&t   B23 provides managed data engineering and applied machine learning services for its customers so they can focus on the extracting the business value of data – and not focus on commoditized engineering. Building and operating durable data analysis infrastructure, and running algorithms at scale, on a 24/7 basis, are challenges that most modern organizations are facing today. By partnering with B23, our customers’ business analysts, data scientists, and machine learning engineers are free to focus on their core-competency, performing data analysis that will be most impactful to their business.   The B23 Data Platform supports a variety of data-processing and analysis-centric software.  We support both open source software, as well...

Exploring Credit Default Swap (CDS) Market Data Using Modern Data Science Techniques

October 2nd, 2018 Title VII of Dodd-Frank Wall Street Reform and Consumer Protection Act addresses the gap in U.S. financial regulation of OTC swaps by providing a comprehensive framework for the regulation of the OTC swaps markets.  The objective of this blog is to describe how to rapidly and securely analyze credit default swap (“CDS”) transaction data using cloud computing and advanced machine learning (“ML”) techniques.  We obtained the CDS data from the Depository Trust and Clearing Corporation (“DTCC”).   A fundamental technology enabler for our customers is the B23 Data Platform which is a Cloud-based artificial intelligence (“AI”) engine to discover, transform, and synthesize data from a variety of sources to provide unique and predictive insights.   The B23 Data Platform is used by data-centric enterprises in many different industries including technology companies, government agencies, and financial institutions to securely use the Amazon Cloud to gain insight from very large data sets. Scope and Accomplishments of our efforts include: Created secure Machine Learning (“ML”) Analysis Cluster in representative Customer Private Cloud Ingested 5 years of CDS data in into the ML analytics cluster in 1 minute Identified anomalous CDS trading activities for complex market transactions Created established CDS compliance reporting metrics Identified CDS market characteristics at individual products and individual series levels     Identify Anomalous Transaction Activity or Faulty Reporting in Markets B23 investigated several transactions in the DTCC CDS data that looked peculiar.  Figure-2 shows a relatively small number of transactions composed of multiple line items in the data set that exhibited anomalous market activities. In the example above, “yellow” nodes are correction activities, and “red” nodes are cancellation activities. Walking through each set of transactions, the following activities are occurring: A single contract is created for a value of $52M (blue node labeled “new”) The $52M original CDS is corrected to a...

Four Reasons Why Data Engineering is a Zero-Sum Game for Most Organizations

September 17th, 2018   Data engineering is hard and getting it exactly 100% right results in a single outcome – machine learning engineers and quants can now do their job effectively.  The analysis of data and the subsequent execution of those insights is the competitive differentiator and core competency of business – its heart and soul.  Data engineering is the commoditized heavy-lifting every organization needs to perform to get the analysis correct.  This is why we see data engineering as a zero-sum game.  Getting data engineering right means organizations are just breaking even – it simply allows other employees to do their job properly.  Getting it wrong means everything and everyone else dependent on data engineering cannot operate effectively. Outsourcing the commoditized heavy-lift data engineering is the least risky and most cost-efficient path to achieve the economic and market-leading competitive advantages organizations need to compete.     Prioritize Algorithm Development Over Data Engineering Modern organizations should prioritize and invest in the algorithm development, quantitative research, and machine learning aspects of data science.  These activities can make or break firms who use data for a competitive advantage.  Applying machine learning in a meaningful way using data formatted specifically for those algorithms is not a trivial task.  To be successful, organizations should recognize the undifferentiated and differentiated activities associated with extracting insight from data and decouple the activities required to get data into a specific format (or schema) to support those algorithms from the development and tuning of those algorithms.   Race Car Drivers and Data Mechanics An interesting social phenomenon we’ve observed over the past several years is that we have yet to meet a data engineer that wasn’t secretly plotting a career change to become a machine learning engineering and/or quant and with a more data science-centric job title to-boot.  If machine learning engineers and quants...