- Top KDnuggets tweets, Sep 26-28: Any data scientist worth their salary will say you should start with a question - Sep 29, 2014.
CNN embarrassing lack of "Data Quality" - this #Scotland Independence poll adds; Statistical & Machine learning with R; Any data scientist worth their salary will say you should start with a question; Automotive Customer Churn Prediction using SVM and SOM.
- Interview: Pallas Horwitz, Blue Shell Games on Why Data Science is So Critical for Gaming Studios - Aug 14, 2014.
We discuss the role of data science at Blue Shell Games, the importance of "Lean Data", key metrics for online games, cross-product projects and optimizing meeting the data needs across an organization.
- Lavastorm Sun Seekers Caribbean Challenge 2 - Aug 5, 2014.
Use Lavastorm Analytics Engine Public Edition to overcome data quality issues and consolidate the lists. Step-by-step instructions make completing the task a snap! Submit your entry by August 31, 2014.
- Interview: Christophe Toum, Talend on Why Big Data Needs Big Governance - Aug 2, 2014.
We discuss the priority order of data governance for Big Data initiatives, impact of increasing shift towards Hadoop and NoSQL, data quality, current trends, talent crunch, advice and more.
- Interview: Aparna Pujar, eBay on Evolution of Behavior Analytics for User Engagement - Jul 25, 2014.
We discuss Behavior Analytics vs. Web Analytics, important metrics for user engagement, challenges of behavior insights domain, future of multi-screen analytics, key soft skill and more.
- Lynn Goldstein, Chief Data Officer, NYU on the Need for Data Governance - Jun 3, 2014.
We discuss the role of Data Governance, establishing Big Data accountability, impact of Data Governance on Data Quality, and assessing the education available for Data Governance.
- Forrester Research: Build Trusted Data with Data Quality - Apr 1, 2014.
Key takeaways of the report include: How managing data quality brings IT and the business closer together, Different data quality definitions, and advantages of transparency in data quality.