Features

Features

Data quality techniques and best practices

• Proactive practices for data quality improvement

  Instead of waiting for data quality to become an issue, consider a proactive approach. Here are some ways to improve data quality in your organization before it becomes a problem.  Continue Reading

• Evaluate your data governance and privacy best practices

  GDPR and CCPA have put data governance in the news recently. With this increase in legislation on data, now is a great time to review your company's data governance policies.  Continue Reading

• How to improve data governance for self-service analytics

  Citizen data scientists and self-service analytics are on the rise as the data scientist shortage continues. Here are some data management best practices to support them.  Continue Reading

• Data catalog management for analytics fraught with unique demands

  Data catalogs for analytics applications demand detailed assessment of user needs, cross-functional teams, ready access, continuous improvement and a self-sustaining system.  Continue Reading

• How to streamline feature engineering for machine learning

  Structured data is necessary in machine learning, but sifting through data is time consuming. Streamlining the feature engineering process can help data scientists be more productive.  Continue Reading

• How AI could save the enterprise data catalog

  Enterprise data catalogs record all the databases and files in a corporation, but sometimes it can become out of date. Advances in AI are helping minimize the problem.  Continue Reading

• DOD CDO shares 7 data management best practices

  The DOD's first chief data officer, Michael Conlin, shares tips on how organizations can modernize their data management practices with the objective of matching private enterprise.  Continue Reading

• Align business and IT drivers through data quality best practices

  Information management consultants talk about the importance of aligning business and IT drivers for a holistic architectural approach to ensuring strong data quality.  Continue Reading

• Information Builders exec talks data management ethics

  In this Q&A, James Cotton, director of the Data Management Centre of Excellence at Information Builders, offers advice on how organizations can carry out ethical data management.  Continue Reading

• Key components of an effective data virtualization architecture

  Experts break down which elements -- both technical and nontechnical -- are most crucial to successfully deploying and managing a data virtualization architecture.  Continue Reading

• Advice on enterprise data cleansing from an SAP VP

  SAP's Kristin McMahon details data cleansing best practices and explains why a good data cleanse needs continual communication, collaboration and oversight.  Continue Reading

• USAA adds data engineering skills to speed data science work

  When the United Services Automobile Association's data science team wasn't getting data in the right format, the team lead realized the USAA needed more data engineers.  Continue Reading

• How to navigate the challenges of the data modeling process

  Data modeling and curation can help businesses more efficiently use data they've collected. There are challenges, however -- beginning with ensuring data quality.  Continue Reading

• 8 tips to improve the data curation process

  A data curation and modeling strategy can ensure accuracy and enhance governance. Experts offer eight best practices for curating data. First, start at the source.  Continue Reading

• How to manage data and the GDPR right to be forgotten

  The General Data Protection Regulation requires organizations to delete personally identifiable information if customers ask -- which can be a challenge. Experts offer four tips.  Continue Reading