ANALYTICS INSIGHTS
Transform data into the best decisions
Recent Analytics Insights
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Insurance customer retention in the AI ageInsurance customer retention has shifted with AI. To enable growth, insurance companies must reduce churn, keep policyholders renewing coverage over time, and protect policies in force. Retaining loyalty now requires insurers to lean into new technological capabilities. See what the "96% rule" means, how our customers are breaking free of the customer retention hole, and the role analytics and AI play.
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Quantum AI examplesQuantum AI has seemed theoretical for years. But it’s gaining momentum in real-world applications. Explore quantum AI examples across financial services, insurance, health care and manufacturing to see how problems beyond classical AI’s reach are solved. And learn about hybrid quantum-classical approaches, where quantum systems and classical computing work together, each handling the parts of a problem they solve best.
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4 strategies to change your approach to fraud detectionFraud detection methods must evolve as fraud threats continue to evolve rapidly. See how financial institutions are using analytics and AI to refine their fraud strategies – and discover four ways to improve your organization's risk posture.
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Trustworthy AI: How to move from ambition to ROITrustworthy AI is artificial intelligence technology that is designed, developed and deployed with human-centricity and other core principles in mind. Learn what it means to trust AI in the face of constant, real-world business pressures. And see why treating trustworthy AI as a repeatable operating model provides a long-term business advantage – and financial returns.
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Is your customer engagement platform really engaging? As decisioning and orchestration become the convergence point within the MarTech stack, the middle layer is where it all comes together. It brings together data, decisioning and activation to enable real-time, personalized customer engagement across channels. Uncover the essential features of a modern customer engagement platform and why prioritizing the middle layer is vital for marketers.
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Benefits of cloud services for analytics and AIThe benefits of cloud services include flexibility, efficiency and strategic value. Learn how cloud services and managed cloud work – including how they help organizations move faster, control costs and modernize legacy systems. Dig into their advantages, including core infrastructure components, delivery models and the strategic value of scalability, security and cloud sovereignty.
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Accelerating innovation with smarter AI hardwareAI is now woven into strategic objectives, and the energy efficiency of AI hardware directly affects operational expenses, geographic decisions and ESG commitments. But AI hardware systems in their current form and at their current growth rate are not sustainable. Leaders must begin to recognize hardware choices not as a technical detail but as a key part of their strategy.
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Fraud detection and machine learning: What you need to knowMachine learning and fraud analytics are critical components of a fraud detection toolkit. Discover what you’ll need to get started defending against fraud – from integrating supervised and unsupervised machine learning in operations to maintaining customer service.
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La IA y la humanidad: una colaboración para resolver los grandes retos del mundoLas comunidades desplazadas, la crisis alimentaria mundial y los desastres naturales no solo afectan a la calidad de vida, sino también a la capacidad de supervivencia. ¿Y si la inteligencia artificial fuera el impulso inesperado que la humanidad necesita? ¿Y si pudiera ayudarnos a lograr un cambio positivo y duradero? Descubra la forma en que la IA está contribuyendo a encontrar soluciones a los grandes retos del mundo.
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Cinco tecnologías de IA¿Cuál es la diferencia entre la inteligencia artificial y el aprendizaje automático? ¿Podría explicar los conceptos básicos de tecnologías como la IA generativa (GenAI) y los agentes de IA? Lea nuestra información general para entender estas y otras tecnologías que están detrás del auge de la inteligencia artificial.
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La IA transforma los seguros: ocho ejemplos de su funcionamientoEl uso de IA en seguros puede aumentar el valor para los clientes, las compañías de seguros y las partes interesadas por igual. Los ejemplos van desde una detección de fraude más fuerte y un servicio al cliente mejorado hasta procesos de suscripción optimizados y ventajas competitivas. Descubre más información sobre cómo la IA está transformando la industria.
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¿Qué son los modelos de IA?La modelización de IA implica la creación de programas que utilizan un algoritmo o una combinación de algoritmos para permitir a los ordenadores pensar, aprender y predecir resultados. Al igual que el cerebro humano, los modelos de IA absorben datos de entrada —números, textos, imágenes, vídeo, sonido— para aprender a predecir resultados o resolver tareas específicas sin instrucciones explícitas en cada paso.
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Así funciona la tecnología gemelos digitalesDescubra cómo la tecnología gemelos digitales está transformando sectores como la salud y la fabricación, y cómo puede marcar el futuro del modelado de datos y la optimización de procesos.
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Big data en el sector público: cómo los datos y el análisis impulsan los programas públicosEl big data en las administraciones públicas es vital cuando se analiza y utiliza para mejorar los resultados de los programas de los sectores público y privado, desde la respuesta a emergencias hasta la eficiencia del personal. Los enormes volúmenes de datos que se crean a diario son la base de cambios importantes para las agencias gubernamentales de todo el mundo.
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Unlocking a strategic approach to data and AIAI is only as good as the data that powers it – this is a fundamental truth about data and AI that defines the limits of what’s possible with artificial intelligence. It may seem surprising, but it's rarely a bad algorithm or a bad learning model that causes AI failures. It's not the math or the science. More often, it's the quality of the data being used to answer the question.
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Ansiedad frente a la IA: mantener la calma ante el cambioLa ansiedad frente a la IA no es ninguna broma.— Tanto si temes que tu trabajo se quede obsoleto, que la información se distorsione o simplemente perderte algo, comprender la ansiedad que provoca la IA puede ayudarte a vencerla.
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Datos sintéticos para impulsar avances de IADescubre por qué los datos sintéticos son vitales para las iniciativas de IA, cómo los utilizan las empresas para desbloquear el crecimiento y cómo pueden ayudar a abordar los retos éticos.
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Edge computingTraditional methods capture, store and analyze data later, slowing response times. With edge computing, IoT data is processed at the edge of a network where it’s created or collected – avoiding delays and enabling real-time processing and action.
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¿Qué son las alucinaciones de IA?Separar la realidad de la ficción generada por la IA puede ser difícil. Aprende cómo los grandes modelos lingüísticos pueden fallar y conducir a alucinaciones de IA y descubre cómo utilizar la GenAI de forma responsable.
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¿Qué son los chatbots?Los chatbots son una forma de IA conversacional diseñada para simplificar la interacción humana con las computadoras. Aprende cómo se utilizan los chatbots en las empresas, cómo pueden incorporarse a las aplicaciones analíticas
Analytics: The Basics
- Overview
- Big Data Analytics
- Data Mining
- Predictive Analytics
- Statistical Analysis
- Blockchain
- Neural Networks
- Data Science
- Algorithm
Analytics
If you're reading this, you probably know at least a little – maybe even a lot – about analytics. According to Wikipedia, analytics is "the discovery and communication of meaningful patterns in data." Seems obvious, right? Where the real story with analytics lies, however, is in what you can do with it. Think in terms of possibilities. Opportunities. Discoveries. Made possible by technological advancements in analytics that were never even dreamed of not so very long ago, within your lifetime. So let's explore the topic of analytics together. We'll share insights from the brightest minds in analytics – insights that you can turn into ... well, it's up to you.
Big Data Analytics
As data floods your organization on a daily basis, the question is no longer "What is big data?" Instead, it's "What can we do with the big data we have?" The answer, of course, is integral to the future of your business. And big data analytics opens up the world of the possible. With big data analytics, you can implement data mining, predictive analytics, text mining, forecasting and optimization to explore your options and make the best possible business decisions.
Data Mining
¿Ha oído hablar del comerciante detallista que descubrió que frecuentemente se compran pañales y cerveza al mismo tiempo? ¿Qué hay de la empresa de telecomunicaciones que puede predecir su probabilidad de cambiar de operador con base en cuántos de sus amigos ya se han cambiado? Estas empresas utilizaron la minería de datos para descubrir información completamente inesperada en sus datos. ¿Qué secretos se ocultan en sus almacenes de datos en eterno crecimiento? Averígüelo con la minería de datos
Predictive Analytics
So you’ve embraced the power of collecting and analyzing data. That’s a great first step, but it just scratches the surface. To get the most out of your data, you need to anticipate the future, not just analyze the past.
Whether you want to attract new customers, assess payment risk, identify fraud or forecast energy demand, predictive analytics can provide answers to your most complex questions.
Statistical Analysis
Statistical analysis isn’t just for sports geeks and political pollsters. Every day, statistics plays a critical role in informing public policy and improving human welfare. The field of statistics has grown from sampling populations to analyzing even the biggest of big data sources.
Today’s statisticians are tracking endangered species, improving agricultural processes and predicting the best treatments for complex diseases. There's a lot going on with this influential field and the statistical programming techniques used by today’s statisticians.
Blockchain
What can you store in blockchain besides Bitcoin transactions? Insurance claims, medical records and even voting records can all be stored in blockchain. Simply put, blockchain is a distributed data architecture that stores data and transactions – and validates the accuracy of those transactions by consensus. Learn more about blockchain and how you can analyze blockchain data.
Neural Networks
Neural networks are computer programs designed to imitate how the human brain learns and searches for patterns. Often referred to as artificial neural networks (ANNs), they are ideally suited to solving real-world problems in different fields. Much like a human brain, they can be trained over time to improve their interpretations of data.
Data Science
Data science is a multidisciplinary field that broadly describes the use of data to generate insight. Unlike more specialized data-related fields, such as data mining or data engineering, data science encompasses the complete life cycle of translating raw data into usable information and applying it for productive ends in a wide variety of applications.
Algorithm
An algorithm is a list of step-by-step instructions designed to accomplish a specific task or solve a specific problem. When you think about it this way, algorithms are pervasive. A French onion soup recipe, for example, is an algorithm. Combine these inputs to generate an output. Algorithms basically work the same way with computers. Whether they’re converting binary numbers into letters so you can read what’s on your tablet screen or recommending stocks based on your risk tolerance, algorithms are fundamental in modern computing. This page will focus on computer algorithms.
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