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.
Defining Data Observabilityand Data Quality As companies gather seemingly endless data streams from an increasing number of sources, they start to amass an ecosystem of… Read More »Data Observability Vs Data Quality: What makes them different?
Relational databases in the 1980s were typically designed using the Codd-Date rules for data normalization. It was the most efficient way to store data used… Read More »DSC Webinar Series – Death of the Star Schema.mp4
In this article, let’s discuss one of the trendy and handy web-scraping tools, Octoparse, and its key features and how to use it for our… Read More »Exploring Octoparse for Data Preparations and Product Assessment
The need for high-quality, trustworthy data in our world will never go away. With the growth in data, the need arises more than ever before.… Read More »The Noise in Modern Data Quality
Database performance allows developers or database administrators to enhance the system resources for lasting performance improvements. Databases are like the central nervous system of an… Read More »5 Ways to Optimize Database Performance
Data management (DM) discussions can be frustrating because both those feeling the pain and the consultants who try to help them are–90+ percent of the… Read More »The long game: Desiloed systems and feedback loops by design (I of II)
Your regulatory data project likely has no use case for design-time data lineage. tl/dr Mapping Data Lineage at design time, for its own end, has… Read More »Do regulatory data projects really need design-time data lineage? Probably not.
With the internet producing quintillions of readily available information per day, you could be forgiven to think that data is losing its value. Apparently, data… Read More »Data quality: What and why is it important?
Introduction In recent years technology has become prominent, both at work and at home. Machine learning (ML) and Artificial Intelligence (AI) are evolving quickly today.… Read More »How AI and ML are transforming data quality management?
Businesses today face myriad challenges, some of which are successfully addressed with help from cloud computing. This is where AWS cloud migration which promises to… Read More »AWS Cloud Migration: All You Need to Know
