The importance of data governance: An interview with UNT
Learn why Jason Simon from UNT calls data governance critical.
Tag: data quality
Data stewards – Why they’re the unsung heroes of the business
Expect to lose time if you don't include a data steward in your project until you're reviewing the data model.
No, data is not the new oil
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
Data steward: The concierge of analytics
Jim Harris says data stewards are essential to analytics, providing life cycle management for data across the enterprise.
Without data management, forget AI and machine learning in health care
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
Data governance implications for the chief data officer
Reconsider conventional assumptions about data governance – three suggestions for chief data officers.
Trust in the data: Ensuring quality analytics (continued)
How should a data trust process work? David Loshin elaborates.
Amazon's data train just keeps a rollin'
Think that the company has let up in the last two years? Think again.
Data management for analytics: Best practices and examples
Focus on data governance, quality and storage if you want to do data management for analytics right.
Trust in the data: Ensuring quality analytics
David Loshin raises questions about what needs to be done to ensure quality analytics.
Data governance policies: Key to analytics innovation?
Better decisions and analytics innovation – fringe benefits of having comprehensive data governance policies.
Data quality: The not-so-secret ingredient of customer data management
Kim Kaluba explains why good customer data management starts with trusted data quality.
Data retention strategy – Part 2
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Data governance – The topic that never dies
Todd Wright says data governance is more relevant than ever – especially in light of the GDPR.
Data retention strategy – Part 1
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
The SAS platform – It all starts with data management
To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Reusable data quality enables self-service data preparation
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
Data quality monitoring – step 1.
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?
SAS Visual Analytics: Hallo Emilia – Goodbye Daniel
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
Self-service data preparation gives fast access to the data you need
Get on with your day faster by taking a self-service approach to data preparation.
Monitorowanie jakości danych - krok 1.
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?
The data governance path to AnaCredit
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
Move "mountains" and build a trusted data foundation with SAS Data Quality
Matthew Magne describes how SAS Data Quality can help you build a trusted data foundation, one stone at a time.
On the way to data lakes and Hadoop – Part 1
Jim Harris advocates addressing data quality and governance issues on the way to data lakes and Hadoop.
Four ways SAS makes Hadoop easy
Clark Bradley explains how SAS can make Hadoop approachable and accessible.
The importance of data quality to MDM
Jim Harris explains why data quality is such a fundamental aspect of master data management.
Lenin überholt sich selbst im Internet of Tempo (IoT3)
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
Don't let data issues delay analytics
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
Data quality and data preparation in self-service
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
Event stream processing, data quality and risk mitigation
David Loshin extends his exploration of ethical issues surrounding automated systems and event stream processing to encompass data quality and risk considerations.