Tips

Tips

• 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 organizations need a solid data governance strategy

  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

• 11 features to look for in data quality management tools

  As the need for quality data has increased, so have the capabilities of data quality tools. Learn how collaboration, data lineage and other features enable data quality.  Continue Reading

• How data lineage tools boost data governance policies

  Enterprises can bolster data governance efforts by tracking how and when changes are made to data. Expert David Loshin offers advice on what to look for with data lineage products.  Continue Reading

• The gradual evolution of master data management software

  Master data management kicked off with a bang in 2004, then hit a few roadblocks before data governance was embraced by the industry. Expert Andy Hayler looks back at the MDM journey.  Continue Reading

• 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 these steps can help you create prototype applications -- which then can aid in evaluating graph databases.  Continue Reading