Blend, cleanse and prepare data for analytics, reporting or data modernization efforts
Joyce Norris-Montanari discusses data engineers in the second of her two-part series.
Encouraging data sharing can sometimes feel like refereeing kids on Christmas morning. “Mom said you have to SHARE!” my sister bawled, grabbing at the Game Boy in my hand as I held the toy just out of her reach. I had just gotten it as a Christmas present and had
Phil Simon raises two key questions to ask in his second post on data literacy.
K(o)ennen Sie schon „DevOps“? Machen Sie SAS? Dann lohnt sich eventuell ein frischer Blick auf die Kombination! Denn immer mehr Unternehmen probieren, ihren produktiven Betrieb auch in die Hände der Software-Entwickler zu legen (2 von 3 laut Jenkins) – speziell in der Analyse, insbesondere beim agilen Modellieren und dem Veredeln
By now you’ve seen the headlines and the hype proclaiming data as the new oil. The well-meaning intent of these proclamations is to cast data in the role of primary economic driver for the 21st century, just as oil was for the 20th century. As analogies go, it’s not too
The data revolution is causing disruption everywhere. Even governments are feeling the effects. While the public sector may be less prepared for the impending changes, government has good reasons to embrace the data revolution. First, governments have been collecting data for years. They have lots of data. This supports programs,
Todd Wright shares results of a SAS survey about consumers' data privacy concerns.
Phil Simon chimes in on the increasingly important topic of data literacy.
Data scientists need good skills in communication, data mining, data wrangling and more. Joyce Norris-Montanari explains.
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
As analíticas vão ser imprescindíveis no futuro. Está é a opinião generalizada da maioria dos gestores. No entanto um estudo levado a cabo pelo SAS sobre Plataforma Analítica - “Here and Now: the need for an analytics platform”, revelou que apenas 35% está efectivamente com projectos de implementação ao nível
Data management gets lost in the enthusiasm around Artificial intelligence (AI) and machine learning (ML). Not surprising, when it's an algorithm that decides what search results to show you, guides the self-driving cars on the roads, and powers the anti-fraud bots that monitor every credit card transaction we make. Charles
Finanzdienstleister haben aktuell massive Herausforderungen beim Management ihrer Daten: Der Kostendruck zwingt einerseits zu einem hocheffizienten Betrieb („run“). Zugleich wandeln sich andererseits die Prozesse im Business, Stichwort Digitalisierung („change“). Die drückenden Regeln der Aufsicht scheinen sich nicht vereinen zu lassen mit dem Anspruch der Kunden, flexibel, fix und doch datensparsam
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
Joyce Norris-Montanari cites five things she considers essential to data management for analytics.
보험 업계는 변화에 느리다는 평을 받는 보수적인 산업이지만 지난 5년간 엄청난 변화와 혁신을 경험하고 있습니다. 보험사들은 고객과 유통 관계에서 혁신을 모색하며 디지털 이니셔티브에 많은 투자를 하고 있는데요. 이와 함께 데이터 분석, 인공지능 등의 정보기술(IT)을 활용해 기존 보험 산업을 혁신하는 인슈어테크(insurtech)가 디지털 기회를 활용할 수 있는 보험 업계의 새로운 분야로 떠오르고
Phil Simon chimes in with some tips on how to set these folks loose.
Los avances tecnológicos, las expectativas de los clientes y las regulaciones que redefinen el mercado, han sido puntos clave para que el sector de los seguros se enfoque en el crecimiento de sus ventas, su rentabilidad y la lealtad y el cuidado de sus clientes. ¿Qué ha pasado entonces? las
Who says technical people can't have fun!?! Similar to Throwback Thursday / #TBT (when people post one of their old/nostalgic photos on social media), I like the tradition of Fun Friday when I use a fun data topic to test our software - a test can be just as rigorous using
I've worked at SAS for over 27 years and have often been asked: What does SAS do? or Why should I choose SAS? It all boils down to one question: Why SAS? While there are many approaches to answering this question, I recently came up with three short, yet powerful,
Jim Harris says people still play a pivotal role in data-driven decision making.
How do you deploy your model so that business processes can make use of it? This post explores how SAS Viya applications can directly add models to a model repository, and specifically focuses on how to deploy them with SAS Model Manager to Hadoop.
El universo de los datos es una realidad que está evolucionando más rápido de lo que muchos esperaban. Su impacto en los negocios ha sido tal, que la gran mayoría de las empresas están implementando o pensando en implementar soluciones de analítica que les permitan consolidar toda la información que
How should a data trust process work? David Loshin elaborates.
With SAS Data Preparation and SAS Decision Manager, you can perform out-of-the-box column and row transformations to increase your data quality and build the foundations for data-driven innovation. This blog will discuss how you can leverage SAS Decision Manager to enrich data when preparing it through SAS Data Preparation.
필자는 데이터 전문가와 엔터프라이즈 시스템 컨설턴트로서 오랜 경력을 쌓아왔으며, 저자와 대학 교수로서 교육 활동도 진행하고 있습니다. 그 동안 200여 개의 개인, 50개 이상의 그룹을 위한 데이터 분석 프로젝트를 감독하면서 수많은 분석 프랙티스를 연구해왔는데요. 중요한 한 가지는 분석 프로젝트를 성공적으로 시작하기 위한 단 하나의 청사진은 존재하지 않는다는 것입니다. 하지만 다양한 성공 또는 실패
Think that the company has let up in the last two years? Think again.
近年、クラウドファーストを唱える企業が増加し、データ分析のために、クラウド上に展開されている分析サービスを活用したり、クラウド上に独自に分析アプリケーションを構築するケースも増えています。 しかし、クラウド上にある分析サービスやアプリケーションで分析する対象のデータは、オンプレミス上に蓄積されているケースが大半であり、クラウドからこれらのデータにアクセスできるようにするための作業や環境設定は面倒かつ非効率で、膨大なデータをクラウドとやり取りするなどの運用コストも大きく、かつセキュリティのリスク回避も考慮しなければなりません。 こうした課題を解決するために、SAS ViyaではSAS Cloud Data Exchange (CDE)を提供しています。 SAS Cloud Data Exchange (CDE) は、プライベート/パプリックのクラウド上にあるアプリケーション（＝SAS Viya）からファイヤーウォールの後ろにある、顧客のオンプレミス上にあるデータに安全かつ確実にアクセスし、大量のデータをクラウドへ高速に転送することを可能とするデータ接続機能です。 CDEは、SAS Viyaのセルフサービス・データ準備向け製品であるSAS Data Preparationに含まれる機能です。 CDEを使用すれば、クラウド上にあるSAS Viyaからオンプレミス上にある様々なデータソース（Oracle, Teradata, Hadoop etc.）へ最小限の手順で容易かつセキュアにアクセスすることが可能になります。 サポート対象データソース： ・DB2, ODBC, Apache Hive, Oracle, Redshift, SQL Server, Postgres, SAP HANA, Teradata, SAS Data Sets CDEでは、最小限の一つのポート（Https port）を使用し、オンプレミス上にあるデータソースにアクセスするための資格情報（ユーザーID /パスワード）も保護された領域に格納し、使用するため、安全性が高められています。 また、クラウド上のSAS Viyaが複数のワーカーノードで分散構成されている場合には、オンプレミス上のデータを並列で高速にSAS Viya環境へロードすることが可能です。 利用手順概要は以下の通りです。 オンプレミス側にSAS Data Agent
Focus on data governance, quality and storage if you want to do data management for analytics right.
Jim Harris says learn the lineage of the data that fed the analysis before you get dazzled by visualizations or algorithms.
