Data Lifecycle Management focuses on data governance, data cleansing and quality, and data stewardship. The rubric applies to articles that focus primarily on the high-level preparation, flow, and use of data through an organization, rather than with one single facet such as storage or analysis.
There’s no question that bad data hurts the bottom line. Bad customer data costs companies six percent of their total sales, according to a UK Royal Mail… Read More »Data Observability Goes Far Beyond Data Quality Monitoring and Alerts
Salesforce offers a very comprehensive and flexible model for data security at various levels. Salesforce also has many tools for data sharing and data management,… Read More »Enabling Salesforce Data Backup and Recovery
The Acceldata Engineering team sought a way to identify Kafka Producer-Topic-Consumer relationship metrics, which resulted in us building our own Kafka utility, named Kapxy. The… Read More »Kapxy – A Kafka Utility for Topic Lineage
Why do I think Data Management is the most important business discipline for the 21st century (and why my “Big Data MBA” book and course… Read More »Why Data Management is Today’s Most Important Business Discipline
Data teams struggle to clean and validate incoming data streams using ETLvalidation scripts, which can be costly, time-consuming, and difficult to scale. This will only… Read More »How ETL Validation Scripts Automation Improves Data Validation
KEH is a ReCommerce camera specialist in finding and providing the best high-end camera gear at an attractive price. To better serve a growing demand… Read More »DSC Webinar Series: Boosting Your eCommerce Business with Marketing Analytics
Enterprises collect and use more data than ever before. To make the most out of all this data, they build complex data pipelines, most of… Read More »Benefits of Using Kafka to Handle Real-time Data Streams
