An uncertain global economic climate has created a sense of unease and a heightened risk when managing finances for individuals and banking and financial institutions. Helping banks and financial institutions manage risk is a central pillar of how SAS adds value for our customers, who must ensure their organization is
Do you get excited about the newest innovations and technologies on the market, the world of data and the future of AI? Then mark your calendars because SAS Innovate in Las Vegas is just around the corner. SAS Innovate, our annual event for business leaders, technical users and SAS partners,
Humanity collectively creates 2.5 quintillion bytes of data daily, presenting incredible opportunities for organizations—especially those who collect it in the cloud. Cloud-based data offers excellent insights for organizations, including a meaningful look at customer needs and operational improvements. However, these benefits come with risks, especially regarding security. According to IBM
Whenever I talk to actuaries across EMEA, they are very consistent in the many anchor points they see for actuarial modernization in order to be future-proof. In this article, I would like to share some of them. Anchor points for actuarial innovation The most time-consuming task is, of course, getting
Two sayings I’ve heard countless times throughout my life are “Work smarter, not harder,” and “Use the best tool for the job.” If you need to drive a nail, you pick up a hammer, not a wrench or a screwdriver. In the programming world, this could mean using an existing
SAS® Viya™ 3.1 represents the third generation of high performance computing from SAS. Our journey started a long time ago and, along the way, we have introduced a number of high performance technologies into the SAS software platform: In-Database processing where SAS data quality and analytical processing occur within the
Whether your resolution is to get SAS certified or to become a more advanced SAS programmer, we’ve got you covered with these new titles and upcoming SAS Press books, many of which will be making their bookshelf debut at SAS® Global Forum 2017 in Orlando, FL! Want to be notified when a new book
One question I get asked a lot is: What is the most exciting new statistical feature in the 14.1 release? And they get a bit frustrated when I say: It depends. But it does depend! SAS statistical software provides a broad array of capabilities that help users track disease outbreaks,
SAS Global Forum では、毎年SASの全てのキーマンが集結します。もちろん2015も例外ではありませんでした。2014年にスマートメーター活用セミナーの講師として来日もした、グローバルセールス＆開発マネージメント ビジネスディレクターのTim Fairchildおよび、エネルギーソリューション担当プロダクトマーケティングマネージャーのAlyssa Farrellと短い朝食ミーティングで意見交換をしてきました。日本では電力小売り自由化もありアナリティクスの活用が進んでいますが、世界的に見てもエネルギー業界にアナリティクスの大きな潮流がやってきています。 それを表すかのように、SAS Global Forum 2015において非常に多くのユーティリティ業界に関するプレゼンテーションがありました。それをご紹介します。 生存時間分析を使用した変圧器の寿命予測とSAS Enterprise Minerを使用した過負荷状態で変圧器を使用している際のリスクモデリング（Predicting transformer lifetime using survival analysis and modeling risk associated with overloaded transformers Using SAS® Enterprise MinerTM 12.1） 「いつ変圧器が故障するのか？」 これが米国のユーティリティ企業が毎日頭を悩ませている問題である。ユーティリティ企業のインフラで最も重要なものの一つが変圧器である。コストを削減し計画的にメンテナンスし、故障による損失を低減するためには、この変圧器の寿命を把握することが重要である。そしてもう一つ重要なことは、過負荷による突発的なパフォーマンスダウンを避けるために高リスクな変圧器を事前に特定しメンテナンスすることである。この論文の目的は、SASを使用して変圧器の寿命を予測し、それらの故障に繋がる様々な要因を特定し、メンテナンスを効率的に行うために変圧器を、負荷状態に基づいて、高リスク、中リスクそして小リスクといったカテゴリに分類するモデルを作成することである。この研究で使用したデータは、米国のユーティリティ企業のものであり、2006年から2013年までのデータである。このデータに対して生存時間分析を行った。Cox回帰分析（比例ハザードモデル）を使用して、変圧器の故障の要因を特定した。また負荷に応じたリスクカテゴリを作成するために、いくつかのリスクベースモデルを使用した。（続きはこちら） 顧客クラスタリングにおけるイノベーティブな方法(An Innovative Method of Customer Clustering) この論文は、SASを使用して顧客セグメントを作成する新しい方法について紹介する。著者はある巨大なユーティリティ企業が提供している９つのプログラムに登録している顧客を調査した。これらにプログラムとは、請求の平準化、支払方法、再生可能エネルギー、効率化、機器の保護、使用量レポートなどである。640,788の家庭のうち、374,441のデータが利用可能であった。これら約半数（49.8%）の分析対象顧客はいくつかのタイプのプログラムに属しており、顧客の特徴を通してこれらプログラムの間の共通性を見出すためには、多くの場合、クラスター分析と相関マトリックスが利用される。しかし、所属しているか否かという二値という性質により、これらの手法の価値はかなり限定的になる。それだけでなく、各プログラムは相互に排他的であることもその一因となる。これらの制限を乗り越えるために、各顧客がどのプログラムに属するかの予測スコアを算出するために、PROC LOGISTICを使用した。（続きはこちら） ブラジルの電力部門における電力損失の査察のためのターゲット選定の改善のためのモデリング－CEMIGの事例(Modeling to improve the customer unit target selection for inspections of
I'm not just talking about all the energy put in planning, scheduling, and supporting this great event, which is in Dallas, TX next week, April 26-29. I'm talking about all the presentations about using analytics in energy related industries, particularly in Utilities and in Oil and Gas. There is a
“When I started using predictive analytics in 1991, I had a desktop computer with a 600 megabyte hard drive running SAS® 5.0 something,” said Olivia Rud, respected business intelligence thought leader and author of Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer Relationship Management. Technology has vastly improved
AI seems to be mentioned everywhere these days. But how can AI be used in day-to-day work? Here, Katherine Taylor explains an example of "practical AI" in banking using SAS Visual Data Mining and Machine Learning. She'll explore more business problems and industries in future posts.
SAS is transforming the motor insurance claims process by integrating of tools - blending data management, ML, and UX into 1 complete system.
Whether for better or for worse, many people agree that generative AI is a game changer that will revolutionize the way we live and work. Optimists believe that generative AI is an opportunity to improve and expand our technological knowledge. At the same time, catastrophists fear that AI in general
SAS' Hamza Ghadyali reveals how to build computer vision solutions with privacy baked into their design.
As we continue the fight against COVID-19 and reflect on the pandemic response, it’s in our nature to look for opportunities to work together and help others. Indeed, we’ve seen numerous acts of heroism alongside tragic stories of loss. In the research community, we are inspired by efforts to find
“Lo que sabemos es una gota de agua; lo que ignoramos es el océano”
What does an analytics platform look like? We might imagine a large server with data and software used for a wide range of operational reporting, BI and data analysis. There may be development and test environments. But typically most users will be working in the same production environment, sharing the
“In God we trust, all others must bring data” - Dr. W. Edwards Deming En el mundo de los sommelier se denomina cata a la acción encaminada a examinar, valorar, comparar o identificar vino (te o café, etc.) mediante el empleo de un análisis realizado con nuestros sentidos: es lo
This episode covers one of the greatest challenges in Dutch data science: how to distribute €43 billion (no, that’s not a typo) among all Dutch health care insurers in a fair, equal and transparent way. To learn more, I visited the biggest health insurer of the country, Zilveren Kruis, and
Joseph Woodside shares examples of applied experimental learning in healthcare from his book, "Applied Health Analytics and Informatics Using SAS".
Deep learning (DL) is a subset of neural networks, which have been around since the 1960’s. Computing resources and the need for a lot of data during training were the crippling factor for neural networks. But with the growing availability of computing resources such as multi-core machines, graphics processing units
This blog post introduces the use of deep learning to train a deep neural network to further improve performance; and hybrid architectures.
Hybrid computers that marry CPUs and devices like GPUs and FPGAs are the fastest computers, but they are hard to program. This post explains how deep learning (DL) greatly simplifies programming hybrid computers.
Looking for use cases for analytics to derive value at an electrical utility? We have identified over 125 ways you can use analytics to improve the business processes at an electric utility. I recently posted a series of blog posts discussing four different use cases. Now I'd like to share
Having addressed the adaptability and power of an analytics environment in my last two posts, I thought I'd close out this mini-series of blogs by providing the business and technology implications of three attributes that need to define any truly open and unified analytics environment: Cohesion Business: The platform enables
Data monetisation is a hot topic these days. Especially for people like me watching the movements of early adopters – companies who are using data to create new revenue streams or even create new businesses to capture those revenue streams. DataStreamX is a notable start-up whose sole business is cashing
Phase 2 von IFRS9 wird nun akut. Und damit steht jetzt auch das Thema Impairment im Mittelpunkt. Der Go-Live für Banken ist für das Jahr 2018 geplant. Bis dahin gibt es jedoch noch einiges zu tun. Viele Projekte beschäftigen sich mit der Umsetzung und mit der Auswahl der Software. Potenzielle
Insurance relies on the ability to predict future claims or loss exposure based on historical information and experience. However, insurers face an uncertain future due to spiraling operational costs, escalating regulatory pressures, increasing competition and greater customer expectations. More than ever, insurance companies need to optimize their business processes. But
In my previous post, I talked about how the Internet of Things promises new ways to use sensor and machine data by creating a highly efficient world that demands constant analysis and evaluation of the state of events across everything that surrounds us. I have also explained why it is
