In the ever-changing world of data-driven decision-making and regulatory requirements, ensuring compliance with data governance standards has become an indispensable aspect of modern organizations. Data compliance refers to the adherence to legal, ethical, and industry-specific regulations that dictate how data should be collected, stored, processed, and shared. Failure to … [Read more...] about Implementing Data Version Control to Ensure Compliance
data quality
How to Build a 5-Layer Data Stack
Like bean dip and ogres, layers are the building blocks of the modern data stack. Its powerful selection of tooling components combine to create a single synchronized and extensible data platform with each layer serving a unique function of the data pipeline. Unlike ogres, however, the cloud data platform isn't a fairy tale. New tooling and integrations are created almost daily … [Read more...] about How to Build a 5-Layer Data Stack
Data Cleaning and Preparation for AI Implementation
Artificial Intelligence and allied technologies such as Machine Learning, Neural Networks, Natural Language Processing, etc. can influence businesses across industries. By 2030, AI is believed to have the potential to contribute about $13 trillion to global economic activity. And yet, the rate at which businesses are adopting AI is not as high as one would expect. The … [Read more...] about Data Cleaning and Preparation for AI Implementation
Top 7 Data Quality Trends to Watch for 2023
Our dependence on data shows no signs of slowing down. We're collecting data directly from customers, appending it from third-party resources, social media, etc. but as many companies have learned, it isn't the quantity of data that matters as much as the quality. To be useful, data must be accurate, complete, valid, unique and structured according to a standardized … [Read more...] about Top 7 Data Quality Trends to Watch for 2023
12 Data Quality Metrics That ACTUALLY Matter
Why do data quality metrics matter? If you're in data, you're either currently working on a data quality project or you just wrapped one up. It's the law of bad data - there's always more of it. Traditional methods of measuring data quality metrics are often time and resource-intensive, spanning several variables, from accuracy (a no-brainer) and completeness, to validity and … [Read more...] about 12 Data Quality Metrics That ACTUALLY Matter