Lately I've been binge-watching a lot of police procedural television shows. The standard format for almost every episode is the same. It starts with the commission or discovery of a crime, followed by forensic investigation of the crime scene, analysis of the collected evidence, and interviews or interrogations with potential suspects. It ends
Tomar decisiones inteligentes a partir de Big Data Analytics se ha convertido ahora en una posibilidad real. La analítica, sin duda, ha revolucionado la manera en la que las compañías se relacionan con sus clientes y ha permitido que se adelanten para brindarles nuevas y apropiadas ofertas en el momento
In many ways financial services is about risk management. Regulatory pressures such as BCBS 239, stress-testing, IFRS9, Solvency II and the Fundamental Review of Trading Book have hugely strengthened that focus. But there are other concerns too. Cost pressures are increasingly important, as is the rise of challengers to the
Since the idea of an “IoT analytical lifecycle,” may be understood in many different ways, let’s start with a definition. Performing analytics at the data center and the cloud is well established practice, and is still quite relevant. With growing numbers of connected devices and availability of computing capabilities at
Machine learning has a high profile currently and is riding a wave of exposure in the media that includes articles about subjects from self-driving cars and self-landing rockets, to computers beating the world’s best players at Go, the most computationally complex board game in the world. Is there an opportunity
SAS was founded on the principal of using analytics to change the world. From fighting cancer and researching the Zika virus to changing the lives of Ghana women by teaching them how to code, SAS has remained committed to helping solve critical humanitarian issues using data and analytics. What SAS has been
Big-Data-Themen und die damit verbundene Datenanalyse werden für Unternehmen immer wichtiger. Wie in meinem vorherigen Blog ausgeführt, ist Big Data (Analytics) deutlich mehr als einfach nur Business Intelligence für große Datenmengen. Stattdessen bilden sie die Basis für neue Geschäftsmodelle und Erfolg oder Misserfolg von Unternehmen oder gar ganzen Branchen.
In an increasingly connected world, the automotive industry is embracing opportunities from the Connected Vehicle and Connected Dealer to reach the Connected Customer. At the recent Automotive Analytics Executive Forum, we heard terrific success stories and far-reaching experiments aimed at facilitating the best customer experiences whether buying a vehicle or getting
Ob der Big Data-Hype schon vorbei ist oder nicht, sei dahingestellt: Big Data ist es jedenfalls nicht – und gibt immer noch genügend Gesprächspunkte für eine Blog-Serie. Im ersten Beitrag zum Thema Big Data Governance möchte ich auf den disruptiven Effekt von Big Data auf Unternehmen eingehen. Hier sind meiner
En muchas ocasiones, el término Big Data es empleado genéricamente para referirse a la recolección y análisis a gran escala de grandes cantidades de datos. Sin embargo, en SAS contemplamos la definición de este concepto más allá del procesamiento de datos masivos y lo consideramos relativo al contexto y situación
Ich wette, dass bei einer Umfrage „Wer mag unstrukturierte Textdaten analysieren?“ die meisten antworten würden: „Ich eher nicht.“ Und das hat einen guten Grund. Denn diese Frage bedeutet: Wenn du unstrukturierte Daten in Textform analysierst, musst du viel Zeit mitbringen. Zeit für die manuelle Regelerstellung in Systemen wie Excel, Zeit
In einem vorherigen Blogpost bin ich darauf eingegangen, welche Möglichkeiten Elektromobilität bietet. Autos tauschen sich aus, wo die nächste Ladestation steht, was hinter der Kurve wartet, oder das Auto kann mit der Werkstatt in Kontakt treten, um einen Wartungstermin zu vereinbaren. Und darauf, wie sich ganze Geschäftsmodelle verändern: Der Automobilhersteller
A huge proportion of big data is unstructured text (such as client interactions, product reviews, call center logs, emails, blogs and tweets). Organizations starting to invest in advanced analytics often overlook the value text analytics could add to the process. But when data scientists or analysts get to work exploring
Wir haben das Zeitalter von Big Data und Internet of Things (IoT). Müssen sich Versicherungen überhaupt noch Gedanken über Data-Warehouse-Konzepte machen, denen ein relationales Datenmodell zugrunde liegt? Oder werden alle relevanten Daten mittelfristig in Big-Data-Strukturen abgelegt werden und damit klassische DWHs überflüssig? Diese Fragen bewegen derzeit viele Versicherungen.
It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus
It's the age of big data and the internet of things (IoT), but how will that change things for insurance companies? Do insurers still need to consider classic data warehouse concepts based on a relational data model? Or will all relevant data be stored in big data structures and thus
You could say we've been working toward the internet of things (IoT) since computers were first invented. Look at how airplanes have changed from flying by wire to now, quite literally, flying by IoT (or connected plane). The connected car is another example of how big data analytics is the
Cloud y Big Data han sido en los últimos años, dos de los principales temas que han movido las inversiones tecnológicas empresariales. ¿Imagina el poder que puede alcanzar una solución integrada en una organización? SAS, sí. Las posibilidades de mejorar la gestión de los datos administrándolos desde la nube son
"Tap into all your demand signals. Organize. Visualize. Analyze. Predict. Orchestrate. Optimize." The availability and collection of data are compelling companies to invest in demand signal management solutions to take advantage of the vast amount of information to support their planning processes. However, many have not gotten the return on
What does it really mean when we talk about the concept of a data asset? For the purposes of this discussion, let's say that a data asset is a manifestation of information that can be monetized. In my last post we explored how bringing many data artifacts together in a
Data monetization, at its simplest, is the process of turning data into bottom-line value for a company -- often through improving efficiency and/or customer experience, and building customer loyalty as a result. This may sound simple, but in practice, it’s anything but. Good data, advanced analytics and real-time decision making
Letztens ist mir das Buch des Naturwissenschaftlers und Comedians Vince Ebert in die Hände gefallen. Es war anfangs sehr lustig und unterhaltsam, bis zu dem Kapitel, in dem es um das Thema Big Data ging. Danach führe die Analyse großer Datenmengen dank des Phänomens „Zufall“ zum Big Fail. Im Folgenden möchte
The digital disruption is creating unforeseen events, such as new competitors, products and services that threaten the performance and positioning of consolidated players. Big data and analytics prove themselves, through successful user cases, as the answer to intercept the demand, prevent churn, draw an integrated view of the customer, manage
Schon mal was von Big Data gehört? – „Klar, hab ich schon mal gehört“, sagen jetzt viele. „Damit habe ich aber gar nichts zu tun, ich arbeite doch nicht in der IT-Branche“, kommt als Nächstes. „Weit gefehlt!“, sage ich dann. Sie glauben nicht, dass Fußball, Big Data und Hochschulen etwas
A long time ago, I worked for a company that had positioned itself as basically a third-party “data trust” to perform collaborative analytics. The business proposition was to engage different types of organizations whose customer bases overlapped, ingest their data sets, and perform a number of analyses using the accumulated
Once upon a time, the only way to buy and sell advertising inventory was based on the relationship between advertiser and publisher. The exchange was manual, with advertisers paying publishers an agreed-upon price for every impression. The process involved a lot of phone calls, spreadsheets, negotiation and re-negotiation, all of
Today, I was in a conversation about using Hadoop (a big data platform) for master data management (MDM). I still find it amazing when we have the discussion of what systems feed another system. Many of our friends have spent years creating MDM for customer, product, etc. with success. I'm a
El concepto de Big Data ha revolucionado la forma de entender el manejo de la información de forma global y estratégica, comprendido hoy en día en dos variantes: el volumen en el manejo de la información, y su velocidad de generación. Para entenderlo y conceptualizarlo, hay que tomar en cuenta
.@philsimon with some recommendations on modernization.
Modernization is a term used to describe the necessary evolution of information technologies that organizations rely on to remain competitive in today’s constantly changing business world. New technologies – many designed to better leverage big data – challenge existing data infrastructures and business models. This forces enterprises to modernize their approach to data
