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
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
How should a data trust process work? David Loshin elaborates.
Think that the company has let up in the last two years? Think again.
Focus on data governance, quality and storage if you want to do data management for analytics right.
David Loshin raises questions about what needs to be done to ensure quality analytics.
Better decisions and analytics innovation – fringe benefits of having comprehensive data governance policies.
Kim Kaluba explains why good customer data management starts with trusted data quality.
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Todd Wright says data governance is more relevant than ever – especially in light of the GDPR.
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
You have to be able to trust the data that you are working with, whether it’s data processing or analysis that you are involved with. And there is a strong correlation between that trust and data quality. Is it possible to determine data quality without monitoring mechanisms?
Die Geburtenrate in Deutschland befindet sich derzeit auf dem höchsten Niveau seit 33 Jahren. Eine erfreuliche Entwicklung, und zugleich stellt es Eltern vor die schwere Entscheidung, welchen Namen der Nachwuchs tragen soll. Zahlreiche Webseiten und Bücher bieten Hitlisten und Namensbeschreibungen an, um die Auswahl zu erleichtern. Oder sollte man das
Get on with your day faster by taking a self-service approach to data preparation.
Niezbędnym elementem wszystkich inicjatyw związanych z przetwarzaniem i analizowaniem danych jest zaufanie do danych, które w znacznej mierze uzależnione jest od ich jakości. Czy można określić jakość danych bez mechanizmów jej monitorowania?
Many of the regulations coming into effect after 2010, are the result of the financial crisis that has significantly re-shaped the financial industry worldwide and especially in Europe. One of the major projects that has been undertaken by the statistics team of the ECB, launched in 2011, is the setup
Matthew Magne describes how SAS Data Quality can help you build a trusted data foundation, one stone at a time.
Jim Harris advocates addressing data quality and governance issues on the way to data lakes and Hadoop.
Clark Bradley explains how SAS can make Hadoop approachable and accessible.
Jim Harris explains why data quality is such a fundamental aspect of master data management.
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
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
David Loshin extends his exploration of ethical issues surrounding automated systems and event stream processing to encompass data quality and risk considerations.
Lenin hatte gelächelt und von seinen Erfolgen im Internet of Things berichtet; richtig begeistert war er gewesen. – Aber jetzt murrt er: „Das ist alles Müll! Internet of Trash sollte es heißen! Die Daten stimmen nicht, die Leute schimpfen über das Projekt, der Fachbereich und meine Chefin sitzen mir im
.@philsimon advises to be wary of those promising obvious and facile solutions to increasingly challenging governance and privacy issues.
In my last post I described "4 adaptability attributes for analytical success," and in the past I've discussed the strategic role analytics play in helping organizations succeed now and into the future. Now I'd like to discuss three attributes that define a powerful analytics environment: Speed Accuracy Scalability [NOTE: Any
Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's
