Tips

Tips

• 5 steps to an improved data quality assurance plan

  Consultant David Loshin offers tips on developing a data quality strategy that can help identify data errors before they cause big business problems.  Continue Reading

• 7 steps to a successful data lake implementation

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

• 10 cloud database migration mistakes to avoid

  Database expert Chris Foot lists the top 10 oversights IT teams commonly make when undertaking a cloud database migration and offers tips on how to avoid them.  Continue Reading

• Cloud database comparison: AWS, Microsoft, Google and Oracle

  IT expert Chris Foot evaluates the leading cloud databases from AWS, Google, Microsoft and Oracle based on their product offerings and migration capabilities.  Continue Reading

• Building leaner, meaner BI data sources

  As business intelligence analysis and reporting platforms become increasingly important in the enterprise, so does the data that feeds them. Are your BI data sources up to par?  Continue Reading

• Data virtualization benefits seen in unified views, IT agility

  Through in-place integration, data virtualization platforms can provide wider access to data and simplify security and governance. But they come with some limitations.  Continue Reading

• The evolution of the data preparation process and market

  Organizations have long struggled with inconsistent data and other issues. Expert Andy Hayler explores how that has led to the rise of the data preparation tools market.  Continue Reading

• 5 things to know about deploying big data systems in data containers

  Planning for security and container APIs, and watching out for infrastructure sprawls are some issues to be aware of before deploying big data in containers.  Continue Reading

• Google Cloud Spanner overview: 4 features to consider

  Cloud Spanner's ability to offer data consistency and horizontal scalability has helped the relational database service gain traction. Learn more about its architecture.  Continue Reading

• Why data silos matter: Settling ownership of data issues

  Data ownership is still seen as a task for IT -- but that can lead to data silos full of redundant information. Find out why you should put the business in charge of its data.  Continue Reading

• Why a solid data governance strategy is a must for companies

  Data management has evolved from centralized data accessible by only the IT department to a flood of data stored in data warehouses. Enter data governance.  Continue Reading

• Trifacta data prep tool helps blend disparate data sources

  Handling diverse data sources usually consumes precious developer time. That led healthcare CRM company SymphonyRM to hand the data prep task to business analysts.  Continue Reading

• How data duplication in healthcare is diagnosed

  Electronic health record systems have helped reduce duplicate patient data in hospitals -- but they haven't cured the problem. Find out how organizations are addressing the issue.  Continue Reading

• How to build a master data index: Static vs. dynamic indexing

  Expert David Loshin explores the differences between static and dynamic indexing in master data management systems, and which queries each approach can support.  Continue Reading

• How deterministic and probabilistic matching work

  Expert David Loshin explores the benefits and challenges of the two classes of record matching in master data management systems: deterministic matching vs. probabilistic matching.  Continue Reading