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Successful Data Lakes: A Growing Trend

Data lakes have been widely misunderstood, but they are now benefiting from new best practices -- helping organizations gain significant value.

Data Scientist Skills for Success

With the increasing demand for data science professionals, what skills are necessary to succeed in our new digital world?

Why Data Science Must (And Will) Be Automated

Data science today is extremely labor-intensive. The automated or quasi-automated features widely used in self-service BI aren't commonplace in data science, but Gartner says that's about to change.

Data Wrangling Versus ETL: What’s the Difference?

Three things that distinguish data prep from the traditional extract, transform, and load process.

The 10 Vs of Big Data

Big data goes beyond volume, variety, and velocity alone. You need to know these 10 characteristics and properties of big data to prepare for both the challenges and advantages of big data initiatives.

Why Your Top Analysts Are About to Quit -- And How to Keep Them

Four steps you can take to retain top talent by ensuring your analysts stay productive and engaged in their work.

Use a Hadoop-based Data Lake to Empower New Best Practices for Business Analytics

To get full business value from big data and other new data sources, many organizations use a data lake atop Hadoop to capture, process, and manage diverse data at scale for business analytics.

The Future of Text Analytics

As enterprises look to put their best foot forward in 2017, many are increasingly turning to text analysis to improve customer experiences and business processes.

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Q&A with Jill Dyché

Find out what's keeping teams up at night and get great advice on how to face common problems when it comes to analytic and data programs. From head-scratchers about analytics and data management to organizational issues and culture, we are talking about it all with Q&A with Jill Dyche.

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