Tips

Tips

• Why data quality tools matter in today's organizations

  Data is everywhere -- but is it good? Data quality tools could answer that question and help save time and money. So why don't more organizations use the technology?  Continue Reading

• GDPR checklist items data managers might have overlooked

  Now that the General Data Protection Regulation has been implemented, it's a good time for organizations to make sure they haven't missed any GDPR best practices.  Continue Reading

• Good data quality for machine learning is an analytics must

  As companies add machine learning applications, they need to really understand -- and be able to improve -- their data. That's where data quality initiatives come in.  Continue Reading

• How graph data modeling can help evaluate database tools

  Mapping data to a graph model can be challenging -- but it can also help an organization create prototypes to evaluate graph database tools in a growing market.  Continue Reading

• When to choose an S3 big data environment over HDFS storage

  Selecting a storage service for big data in the cloud can be challenging. Expert David Loshin explains usage patterns that could lead organizations to Amazon Simple Storage Service.  Continue Reading

• The benefits of columnar storage and the Parquet file format

  What's behind Apache Parquet's growing popularity? It may be the file format's columnar storage orientation, which leads to benefits including improved query performance.  Continue Reading

• Four first steps for customer data management

  Forrester's Mike Gualtieri details how to develop a unified plan to manage customer data that gives business users what they need to manage CRM programs.  Continue Reading

• Three factors for protecting sensitive data in the GDPR era

  Data privacy is a hot topic nowadays thanks to GDPR and the Facebook data scandal. But how do data security, access control and data protection differ?  Continue Reading

• What goes into a customer analytics data integration framework

  Customer data integration is a minefield for IT teams to navigate. But incorporating a set of core technical functions into an integration architecture can ease the process.  Continue Reading

• Data integration techniques to help keep BI data consistent

  Data integration processes that aren't managed properly can create inconsistent data in BI and analytics applications. Here are some steps to avoid that problem.  Continue Reading

• Why Spark DataFrame, lazy evaluation models outpace MapReduce

  Learn how the Spark DataFrame execution plan works and why its lazy evaluation model helps the processing engine to avoid the performance issues inherent in Hadoop MapReduce.  Continue Reading

• Six data risk management steps for GDPR compliance

  By layering data risk management processes into your data governance framework, you can ensure the personally identifiable information your company stores meets GDPR compliance rules.  Continue Reading

• GDPR rules can spur broader steps to protect sensitive data

  Companies need to make compliance with GDPR's requirements on managing personal data a priority, but they should also work to implement wider data protection efforts in the future.  Continue Reading

• GDPR mandates push data quality improvement into IT spotlight

  Data quality issues cost companies significant amounts of money in lost revenue and added expenses, and their impact will only get bigger when the EU starts enforcing its GDPR law.  Continue Reading

• Seven steps to a successful data lake implementation

  Flooding a Hadoop cluster with data that isn't organized and managed properly can stymie analytics efforts. Take these steps to help make your data lake accessible and usable.  Continue Reading