Q&A with Alastair Speare-Cole, President of Insurance at QOMPLX

Alastair Speare-Cole, President and General Manager of the Insurance Division at QOMPLX, leads the overall strategy for the business unit, the development of QOMPLX’s underwriting-as-a-service platform, the management of the company’s Managing General Agent (MGA), as well as setting the direction for the company’s next-generation insurance decision platform that leverages a wide variety of data and advanced analytics to provide advanced risk and portfolio management solutions. Prior to joining QOMPLX, he served as Chief Underwriting Officer at Qatar, and he served as the CEO of JLT Towers from 2012 to 2015. He was also COO at Aon Re for ten years and has also held board appointments at reinsurance and banking subsidiaries in the United Kingdom.

Over the span of my career, I have seen this industry move from crude and manual deterministic models to stochastic models, SAAS actuarial tools, catastrophe modeling, and entity-level capital modeling.



MEDIA 7: Can you please tell us a little bit about yourself and your professional career?
ALASTAIR SPEARE-COLE:
I have spent much of my career involved in reinsurance where portfolios of risk are analyzed and traded. Over the span of my career, I have seen this industry move from crude and manual deterministic models to stochastic models, SAAS actuarial tools, catastrophe modeling, and entity-level capital modeling. And so, my career has shadowed this trajectory. Especially in the eighties, when I was involved in designing and building some of the early models.


M7: QOMPLX has recently partnered with COMBUS. What does this partnership bring to the table?
ASC:
I have long been a fan of COMBUS and Will Gardner, whom I have worked with in the past. The insurance industry has become reliant on too few dominant models. Models as we have seen in recent months over COVID-19 approximate reality but are always just that, an approximation. And it should be no surprise that their limitations show differences between what is predicted and what actually happens. One strategy to combat this is to have multiple ways of modeling the same thing. The insight gained from different views is key to developing a better understanding. The success of COMBUS and other independent modeling companies is vital to creating a diversity of views and our aim is to try to make them more easily accessible and integrable into insurers’ and reinsurers’ platforms.


Models as we have seen in recent months over COVID-19 approximate reality but are always just that, an approximation. And it should be no surprise that their limitations show differences between what is predicted and what actually happens.



M7: How do QOMPLX’s Insurance products help businesses overcome the problems in the insurance value chain?
ASC:
There has been an arms race going on within the insurance industry around gathering and using data for risk selection, risk pricing, and getting clients through the front door. Everyone wants faster insight and the ability to make faster decisions. Our concept is to provide people with a Lego kit of enterprise-grade software that will help them link everything together- from ingestion, organization, storage, and analysis for structured and unstructured data, co-integrated with insurance-specific workflow solutions built on a common underlying data fabric to delivering an edge in decision making, risk selection, and loss control.


M7: What are some of the challenges in risk management in the post-COVID-19 era?
ASC:
Business interruption has been an increasing concern for all risk managers, not just those who worry about the ‘just-in-time’ process and supply chain. COVID-19 has demonstrated that the business interruption coverage offered by insurers is often poorly constructed, not tailored to a world where intangible assets are as valuable as tangible and where there are some risks that are so systemic that only governments can cope with the accumulation of tail risk.


There has been an arms race going on within the insurance industry around gathering and using data for risk selection, risk pricing, and getting clients through the front door. Everyone wants faster insight and the ability to make faster decisions.



M7: What do you see as the future of InsurTech? How is QOMPLX contributing to its growth?
ASC:
There is a huge wave of investment in InsurTech. Whilst the ideas these startups encapsulate may inspire permanent evolution of insurance, as individual businesses many will fail. Tackling a small slice on an insured’s risk, however originally done, is not going to work if the results in insured having to buy dozens of niche products that still leave gaps. Insurers will not continue to support InsurTechs that cannot build scale and scaling means distribution which is expensive. And most InsurTechs like to build their own software when they could adapt off-the-shelf products. And this means that much of their seed money goes on this which is inessential, rather than distribution which is crucial. We are looking hard at ways of working with other partners to allow people who have a great idea to take it to market, rather than trying to build a standalone business which may saddle the idea with costs that it cannot afford.


M7: What is the best advice you’ve received?
ASC:
I think it is encapsulated by Kipling’s poem “If.”

ABOUT QOMPLX

QOMPLX helps organizations make intelligent business decisions and better manage risk through our advanced, proprietary risk cloud. We are the leaders at rapidly ingesting, transforming, and contextualizing large, complex, and disparate data sources through our cloud-native data factory in order to help organizations better quantify, model, and predict risk. Our specialized experts and technology solutions in cybersecurity, insurance, and finance power leading global corporations and mission-critical public sector agencies.

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SingleStore | January 25, 2024

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Now generally available, and ready for both analytical and transactional processing for apps originally built on MongoDB. Announced in public preview in early 2023, SingleStore Kai is an API to deliver over 100x faster analytics on MongoDB® with no query changes or data transformations required. Today, SingleStore Kai supports BSON data format natively, has improved transactional performance, increased performance for arrays and offers industry-leading compatibility with MongoDB query language. Projections: To further advance as the world’s fastest HTAP database, SingleStore has added Projections. Projections allow developers to greatly speed up range filters and group by operations by introducing secondary sort and shard keys. Query performance improvements range from 2-3x or more, depending on the size of the table. With this latest release, SingleStore becomes the industry’s first and only real-time data platform designed for all applications, analytics and AI. 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SingleStore Announces Real-time Data Platform to Further Accelerate AI, Analytics and Application Development

SingleStore | January 25, 2024

SingleStore, the database that allows you to transact, analyze and contextualize data, today announced powerful new capabilities — making it the industry’s only real-time data platform. With its latest release, dubbed SingleStore Pro Max, the company announced ground-breaking features like indexed vector search, an on-demand compute service for GPUs/ CPUs and a new free shared tier, among several other innovative new products. Together, these capabilities shrink development cycles while providing the performance and scale that customers need for building applications. In an explosive generative AI landscape, companies are looking for a modern data platform that’s ready for enterprise AI use cases — one with best-available tooling to accelerate development, simultaneously allowing them to marry structured or semi-structured data residing in enterprise systems with unstructured data lying in data lakes. “We believe that a data platform should both create new revenue streams while also decreasing technological costs and complexity for customers. 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SingleStore has announced support for vector search using Approximate Nearest Neighbor (ANN) vector indexing algorithms, leading to 800-1,000x faster vector search performance than precise methods (KNN). With both full-text and indexed vector search capabilities, SingleStore offers developers true hybrid search that takes advantage of the full power of SQL for queries, joins, filters and aggregations. These capabilities firmly place SingleStore above vector-only databases that require niche query languages and are not designed to meet enterprise security and resiliency needs. Free shared tier. SingleStore has announced a new cloud-based Free Shared Tier that’s designed for startups and developers to quickly bring their ideas to life — without the need to commit to a paid plan. On-demand compute service for GPUs and CPUs. SingleStore announces a compute service that works alongside SingleStore’s native Notebooks to let developers spin up GPUs and CPUs to run database-adjacent workloads including data preparation, ETL, third-party native application frameworks, etc. This capability brings compute to algorithms, rather than the other way around, enabling developers to build highly performant AI applications safely and securely using SingleStore — without unnecessary data movement. New CDC capabilities for data ingest and egress. To ease the burden and costs of moving data in and out of SingleStore, SingleStore is adding native capabilities for real-time Change Data Capture (CDC) in for MongoDB®, MySQL and ingestion from Apache Iceberg without requiring other third party CDC tools. SingleStore will also support CDC out capabilities that ease migrations and enable the use of SingleStore as a source for other applications and databases like data warehouses and lakehouses. SingleStore Kai™. Now generally available, and ready for both analytical and transactional processing for apps originally built on MongoDB. Announced in public preview in early 2023, SingleStore Kai is an API to deliver over 100x faster analytics on MongoDB® with no query changes or data transformations required. Today, SingleStore Kai supports BSON data format natively, has improved transactional performance, increased performance for arrays and offers industry-leading compatibility with MongoDB query language. Projections: To further advance as the world’s fastest HTAP database, SingleStore has added Projections. Projections allow developers to greatly speed up range filters and group by operations by introducing secondary sort and shard keys. Query performance improvements range from 2-3x or more, depending on the size of the table. With this latest release, SingleStore becomes the industry’s first and only real-time data platform designed for all applications, analytics and AI. SingleStore supports high-throughput ingest performance, ACID transactions and low-latency analytics; and structured, semi-structured (JSON, BSON, text) and unstructured data (vector embeddings of audio, video, images, PDFs, etc.). Finally, SingleStore’s data platform is designed not just with developers in mind, but also ML engineers, data engineers and data scientists. “Our new features and capabilities advance SingleStore’s mission of offering a real-time data platform for the next wave of gen AI and data applications,” said Nadeem Asghar, SVP, Product Management + Strategy at SingleStore. “New features, including vector search, Projections, Apache Iceberg, Scheduled Notebooks, autoscaling, GPU compute services, SingleStore Kai™, and the Free Shared Tier allow startups — as well as global enterprises — to quickly build and scale enterprise-grade real-time AI applications. We make data integration with third-party databases easy with both CDC in and CDC out support.” "Although generative AI, LLM, and vector search capabilities are early stage, they promise to deliver a richer data experience with translytical architecture," states the 2023 report, “Translytical Architecture 2.0 Evolves To Support Distributed, Multimodel, And AI Capabilities,” authored by Noel Yuhanna, Vice President and Principal Analyst at Forrester Research. "Generative AI and LLM can help democratize data through natural language query (NLQ), offering a ChatGPT-like interface. Also, vector storage and index can be leveraged to perform similarity searches to support data intelligence." SingleStore has been on a fast track leading innovation around generative AI. The company’s product evolution has been accompanied by high-momentum growth in customers and surpassing $100M in ARR late last year. SingleStore also recently ranked #2 in the emerging category of vector databases, and was recognized by TrustRadius as a top vector database in 2023. Finally, SingleStore was a winner of InfoWorld’s Technology of the year in the database category. To learn more about SingleStore visit here. About SingleStore SingleStore empowers the world’s leading organizations to build and scale modern applications using the only database that allows you to transact, analyze and contextualize data in real time. With streaming data ingestion, support for both transactions and analytics, horizontal scalability and hybrid vector search capabilities, SingleStore helps deliver 10-100x better performance at 1/3 the costs compared to legacy architectures. Hundreds of customers worldwide — including Fortune 500 companies and global data leaders — use SingleStore to power real-time applications and analytics. Learn more at singlestore.com. Follow us @SingleStoreDB on Twitter or visit www.singlestore.com.

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Data Visualization

SensiML Unveils Data Studio - Next-Generation Sensor Data Management for AI / ML

SensiML | December 20, 2023

SensiML Corporation, a leader in AI software for IoT and a subsidiary of QuickLogic, announced the launch of Data Studio, a ground-breaking platform designed to redefine the landscape of sensor data management. With a focus on practicality and efficiency, Data Studio empowers engineers and data scientists by offering an integrated solution that addresses the most time-consuming tasks in AI engineering projects - creating high-quality datasets for evaluating and developing ML models. According to Cognilytica, a well-respected AI / ML consulting firm, approximately 80% of the total time for machine learning (ML) projects is allocated to data preparation. These tasks include data identification, aggregation, cleansing, labeling, and augmentation – all of which are supported in SensiML's collaborative development environment. SensiML Data Studio significantly improves productivity and simplifies dataset management for anyone working on sensor data ML projects. With real-time connectivity, intuitive visualization tools, sensor data video synchronization, and robust support for large-scale collaborative projects, it offers a seamless experience for developers on edge devices, gateways, PCs, and cloud platforms. A comprehensive overview of all the features of Data Studio can be found on the SensiML website. The primary features are highlighted below: Effortless Data Capture and Import - Capture live sensor data, analyze it instantly, and label any data for seamless insights. Collaboratively Label Sensor Data - Employ flexible labeling methodologies for sensor data, including manual, AI-assisted, and custom – and sync video for effortless complex labeling. Store and analyze data locally on your computer or remotely. Data Analysis and Model Evaluation - Visually compare ML models, filter, transform, and fuse sensor data – all with built-in tools and your own Python expertise. Label and Data Versioning – Keep track of your labels and model results with versioned labels. Easily export your project to an open format. "SensiML Data Studio makes sensor data management and analysis more accessible and efficient, empowering developers to build better, more impactful applications using sensor data across a wide range of industries," said Chris Knorowski, CTO of SensiML. SensiML Data Studio is poised to transform sensor data analysis, offering a valuable resource for researchers, engineers, and data scientists across diverse sectors from agriculture and consumer wearables to medical devices, smart buildings, and factory maintenance. About SensiML SensiML, a subsidiary of QuickLogic (NASDAQ: QUIK), offers cutting-edge software that enables ultra-low power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The company's flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labeling, algorithm and firmware auto-generation, and testing. The SensiML Toolkit supports Arm® Cortex®-M class and higher microcontroller cores, Intel® x86 instruction set processors, and heterogeneous core QuickLogic SoCs and QuickAI platforms with FPGA optimizations.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

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