Selecting the right data preparation solution can seem like an overwhelming task, but it doesn't have to be if you keep these three best practices in mind.
- By Frank Moreno
- January 20, 2017
Think of "hybrid integration" as a new paradigm for data and application integration. It's tailor-made for enterprises in which IT resources live both on premises and in the cloud.
Organizations need to seek the right balance of freedom and oversight that enable users to get their work done self-service style. Here are four trends that will help organizations achieve that goal in 2017.
- By David Stodder
- December 16, 2016
The Apache Spark cluster computing framework is widely used in the enterprise, and most adopters expect to increase their use of Spark over the next year, according to a recent survey of big data professionals.
How to get started with data analytics, improve your data wrangling, and set expectations for data governance in self-service BI.
- By Quint Turner
- December 13, 2016
How much unrefined data may start costing enterprises, why a disruptive business strategy requires data security, and why some enterprises are still unwilling to let go of departmental data silos.
- By Quint Turner
- December 12, 2016
It was hard enough to manage testing and quality assurance in the old days. In the context of big data, cloud, streaming data, and other still-emerging technologies, automated testing and quality assurance is a necessity, not a luxury.
Define big data and data science, learn why the U.S. election result doesn’t indicate inherent problems with analytics, and ensure your enterprise is compliant with big data regulation.
- By Quint Turner
- November 28, 2016