Fraudsters have been around forever, and so have the good people trying to stop them. The methods the two employ change over time, though. With artificial intelligence taking center stage over the past decade, using machine learning for fraud detection has become popular in many industries. In this article, we’ll explore how to use machine learning for fraud detection, some of the most commonly used algorithms, and best practices so you can get the most out of this powerful technique. It’s important to note that although we mostly refer to examples of financial fraud in this post, the concepts ..read more

A short glimpse at Wikipedia tells you, “Benchmarking is the practice of comparing business processes and performance metrics to industry bests and best practices from other companies. Dimensions typically measured are quality, time and cost.” Over the past year, SQream started taking the “art” of benchmarking seriously, as part of migrating its data analytics platform into the cloud, creating its hybrid offering. First, our efforts were mostly internal, trying to understand whether our product showed any value compared to other cloud data analytics giants, such as Amazon Redshift, Google BigQ ..read more

Growing Data = Longer Total Time to Insight Taking and making sense of our data is critical to every part of the organization. Life has become increasingly digital, with data collected from touchpoints such as personal devices and edge sensors resulting in new challenges for organizations needing to interpret this data, as well as the professionals themselves tasked with the hands-on job. And, even before this, there is the continual challenge of taking massive and growing data stores, analyzing them, and providing insights comprehendible to non-data people. For sure, GPU analytics can help ..read more

Analytics Challenges and Solutions in Manufacturing THE MOVE TO SMART MANUFACTURING As the world moves into a new age of digitalization, industries across the board are undergoing major change. In finance and banking, healthcare, mining, retail, advertising and virtually every other niche, data has become the most important asset and the main driver of business. IoT has become a mainstay of daily life, sensors are everywhere, and everything has gone ‘smart,’ including manufacturing. Both production and business processes within manufacturing have benefitted from Industrial IoT. Machine Learnin ..read more

DATA BOOM – OR DATA BUST? As with most industries, the financial sector has experienced a data boom. The last two decades have seen technological advancements – from the internet to AI to 5G – contributing to this data explosion, as devices have proliferated, and data has continued to grow with no end in sight. With the dizzying pace of data volume growth, new and more complex financial business models have become the norm, as organizations seek to use their data to answer critical business questions: What is our competitive advantage? How can we better meet the needs of our customers through ..read more

We are excited to announce that we have acquired Panoply,  a no-code cloud data platform, to expand SQream’s cloud offering. This acquisition is part of our vision to provide the fastest analytics platform at any scale of data in a completely seamless experience. Together, our companies will become the leading powerhouse in future-ready data analytics platforms, offering the most scalable and easy-to-use hybrid data platform in the industry and powering high-scale hybrid environments (cloud, edge and on-premise).    SQream will leverage its portfolio of SaaS cloud offerings to p ..read more

(This is part two of a two-part series describing how future AI outcomes might be hindered by today’s databases bottlenecks.) In part one of this series, I discussed McKinsey Institute’s report on the different paths organizations took on their way to integrating AI into their business. The bottom line of that report was that companies which attempted to integrate AI into the business and delivered a high positive impact on their operating income had similar characteristics of how they approached their AI journey. Those characteristics: understand that this is a long-term journey which will in ..read more

(Esta es la segunda parte de una serie de dos partes, que describe cómo los resultados futuros de IA podrían ser obstaculizados por las congestiones de las bases de datos de hoy en día). En la primera parte de esta serie, he analizado el informe del McKinsey Institute acerca de las diferentes trayectorias adoptadas por las organizaciones en su camino a la integración de la IA en su negocio. La conclusión de ese informe fue que las compañías que intentaron integrar la IA en el negocio y que aportaron un impacto altamente positivo en sus ingresos operativos, abordaron su trayectoria de IA con ca ..read more

(Esta es la primera parte de una serie de dos partes que describe cómo los resultados de IA podrían ser obstaculizados por las congestiones de las bases de datos de hoy en día). Un informe reciente del The McKinsey Institute ha demostrado por qué se requiere planificación, paciencia y determinación para darse cuenta y manifestar realmente el valor de la IA y el Aprendizaje Automático en las organizaciones. Los autores del informe han entrevistado a ejecutivos de aproximadamente 800 compañías de las industrias de tecnología, medios y telecomunicaciones, mapeando la trayectoria de IA de estas or ..read more

(This is part one of a two part series describing how future AI outcomes might be hindered by today’s database bottlenecks.) A recent report by The McKinsey Institute demonstrated why it takes planning, patience and determination to truly realize and manifest the value of AI and Machine Learning in organizations. The report’s authors surveyed executives from roughly 800 companies in the technology, media and telecommunications domains, mapping these organizations’ AI journey over the last few years. When they analyzed the results, they realized that only 10% of these companies, which were cate ..read more