Data accuracy is the biggest challenge many businesses encounter in their quest to cleanse data. Having accurate data is the foundation of the usefulness of data in all its stages of use.
Data Lifecycle Management
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.
Data profiling focuses on examining and analyzing data, followed by creating a useful summary of that data.
Businesses, whether big or small, know that understanding data is essential to making informed decisions that impact the organization’s bottom line.
The amount of customer data available to brands has absolutely exploded in the past decade, giving brands an opportunity to create personalized experiences. But with… Read More »DSC Webinar Series – Its Time to Turn Data Chaos into Order.mp4
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
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