Manage

Manage

Learn to apply best practices and optimize your operations.

• How data lineage tools boost data governance policies

  Organizations can bolster data governance efforts by tracking the lineage of data in their systems. Get advice on how to do so and key features in data lineage tools. Continue Reading

• 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

• Data warehousing design and value change with the times

  Big data, the cloud and analytics profoundly shape data warehouse purpose and design. Learn how companies derive value from a repository that at times needs definition. Continue Reading

• Analytics demands add loftier goals to data warehouse strategies

  As the concept of storing data and the technologies needed to do it evolve, companies with set goals in mind are building their data warehouses to maximize analytics outcomes. 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 best practices rely on teamwork, governance, tools

  This handbook provides advice on creating data catalogs to help data analysts and other users find information in corporate systems, plus insight on data catalog trends.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

• Data warehouse technologies evolve as vendors look skyward

  The need for high-speed analytics and low latency are pushing data warehousing vendors to create and reinvent their platforms and, in some cases, reinvent themselves.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

• Top database cloud migration considerations for enterprises

  As the amount of incoming data grows, many organizations are switching to cloud databases. Understanding what database best meets your needs is an important process to tackle.Continue Reading

• Managing unstructured data is crucial to enterprises' AI goals

  Unstructured data makes up a huge portion of most businesses' data volume. But, with data-hungry AI systems coming online, making sense of these stores has never been more important.Continue Reading

• RDBMS (relational database management system)

  A relational database management system (RDBMS) is a collection of programs and capabilities that enable IT teams and others to create, update, administer and otherwise interact with a relational database.Continue Reading

• How enterprises navigate GDPR data management rules

  For businesses that operate in the European Union, complying with GDPR has to be a top priority. In many of these organizations, the burden is falling on the data management staff.Continue Reading

• 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

• 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