Blend, cleanse and prepare data for analytics, reporting or data modernization efforts
David Loshin discusses two common roadblocks in moving Hadoop from proof-of-concept to production.
Kim Kaluba describes how a customer data strategy can help you achieve an omnichannel vision.
Joyce Norris-Montanari poses the question: Is Hadoop/big data technology actually ready for MDM?
In nahezu einem Jahr findet die neue EU-Datenschutz-Grundverordnung (DSGVO) Anwendung. Wer bisher dachte, das hat noch Zeit und wird nicht so heiß gegessen, wie es gekocht wird, der wurde von der Ankündigung des Bayerischen Landesamts für Datenschutzaufsicht überrascht: Bayern kündigt schon erste Kontrollbesuche an. „Abwarten und nichts tun ist mehr
.@philsimon chimes in on some oft-overlooked differences.
When developing SAS applications, you can feed database tables into your application by using the libname access engine either by directly referring a database table, or via SAS or database views that themselves refer to one or more of the database tables. More on Automation with SAS: Let SAS write
Do you know how master data management and data warehouses are different? Jim Harris explains.
Welche Rolle Datenqualität und Data Governance beim Data Management für Analytics spielen, habe ich mit meinem Kollegen Gerhard Svolba zuletzt an dieser Stelle diskutiert. Doch was genau macht modernes Datenmanagement aus, und welche Rolle spielen dabei neue Technologien à la Hadoop und Co.? Und wie sieht überhaupt die künftige Zusammenarbeit
Todd Wright explains what GDPR means and shows how SAS can help you prepare for it.
David Loshin explains 4 struggles of syndicating master data across the enterprise.
GDPR is coming at all of us like a fast moving car on a narrow road late at night. At least that’s what it seems like to me sometimes when I see customers come to realize the full implications of the new law and get a stunned look on their
Data-driven businesses outperform competitors. Matt Magne says SAS Data Governance and SAS MDM can help you get there.
Dylan Jones says spend time setting a vision of how to transform your data landscape – not debating definitions.
Jim Harris discusses how the lines between data management and analytics are fading.
Kürzlich habe ich mich mit meinem Kollegen Michael Herrmann darüber unterhalten, wie Big Data die Anforderungen an Datenmanagement und vor allem an die Datenqualität verändert – und wie die IT, der Data Scientist und die Fachabteilung besser zusammenarbeiten können. Heute geht es darum, wie Daten nachvollziehbar und transparent gemacht werden
David Loshin explains why MDM is such a valuable tool in helping to detect fraud.
Lenin und ich sitzen im Publikum und applaudieren heftig: Seine Chefin hat ihren Vortrag beendet über „Datenqualität als Erfolgsfaktor im Internet of Things“. „Kein Datenqualitätsprojekt ohne Hilfe von oben“, raunt Lenin mir zu, "Unterstützung vom Boss ist manchmal wichtiger als tolle Software." Ich will beleidigt darauf hinweisen, dass seine Chefin
Via streaming data, Jim Harris says machines can learn some amazing things without being programmed with domain knowledge.
Kennen Sie Kevin Ashton? Der britische Technologie-Pionier hat am Massachusetts Institute of Technology (MIT) einen internationalen Standard für RFID mitbegründet. Was aber vielleicht noch wichtiger ist: Vor fast 20 Jahren hatte er eine Vision von Computern, die Informationen über Gegenstände des Alltags und der Fabrikation sammeln und mit diesen Daten
Auch wenn der Hype von Gartner für beendet erklärt wurde: An Big Data und der Auswertung entsprechender (oftmals unstrukturierter) Datenmengen kommt kein Unternehmen vorbei. Doch welche Herausforderungen stellen Big Data und damit einhergehende Entwicklungen an das Data Management? Wie können Data Scientists, IT und Fachabteilung heute zusammenarbeiten? Und wo prallen
Analise Polsky says analytics success for midsize business depends on getting the basics right and maintaining a data focus.
@philsimon says that old stalwarts sometimes just don't cut it.
‘포레스터 웨이브: 2016년 4분기 엔터프라이즈 인사이트 플랫폼 스위트’ 보고서에서 단독 리더로 선정 전략∙최신 오퍼링∙시장 입지 평가 항목에서 높은 점수 획득 ··· 기업의 의사 결정 시점에 인사이트 적용을 지원하는 차별화된 리더로서 확고한 입지 개방형∙클라우드 기반 분석 아키텍처 ‘SAS 바이야(SAS Viya)’, 보다 현대적이고 단순화된 아키텍처로 높이 평가 2017년 1월 23일, 서울 세계적인
There is a well-known Russian saying that goes “Если нельзя, но очень хочется, то можно.” The English translation of it can span anywhere from “If you can’t, but want it badly, then you can” to “If you shouldn’t, but want it badly, then you should” to “If you may not,
Batters assess the game before hitting the ball. Similarly, Todd Wright says businesses can make faster, better decisions by understanding events in motion.
In a recent presentation, Jill Dyche, VP of SAS Best Practices gave two great quotes: "Map strategy to data" and "strategy drives analytics drives data." In other words, don't wait for your data to be perfect before you invest in analytics. Don't get me wrong -- I fully understand and
The rise of self-service analytics, and the idea of the ‘citizen data scientist’, has also brought a number of issues to the fore in organizations. In particular, two common areas of discussion are the twin pillars of data quality and data preparation. There is no doubt that good quality, well-prepared
Does age matter? Perhaps not, but maturity certainly does. The level of analytics maturity, in particular, makes a big difference to the options open to companies, and the strategies that they can adopt to get best value from analytics. A model of analytics maturity I like Thomas Davenport’s model of
David Loshin extends his exploration of ethical issues surrounding automated systems and event stream processing to encompass data quality and risk considerations.
