Posts by Giana Reno

Ensuring high-quality data is imperative for every organization, but did you know the role of ML and AI in data quality management? That’s right, many of today’s sophisticated data quality management tools utilize advanced machine language (ML) and artificial intelligence (AI) technology to identify poor-quality data and make it cleaner. ML and AI help to…

Your business, like all businesses, relies on vast amounts of data to manage data-to-day operations and develop long-term strategies. The need to maintain high data quality reinforces the need for constant and consistent data quality monitoring. You need to be alerted if your data quality slips so that you can fix the problem and resume…

You’ve heard the phrase “data observability,” but do you know what it means—or why it’s important? Data observability is all about becoming more knowledgeable about the state and health of your organization’s data. Robust data observability enables you to identify issues with your data, including poor data quality, and helps you to resolve those issues.…

Organizations need to improve data quality to ensure they’re always using accurate and useful data. According to Experian’s 2021 Global Data Management Research report, businesses say that poor quality data: Wastes resources and increases costs (40%) Damages the reliability of analytics (36%) Negatively impacts reputation and customer trust (32%) Negatively affects the customer experience (32%)…

Building a robust data infrastructure is essential to survive in today’s ultra-competitive environment. Do you know how to create an efficient and effective data infrastructure? There are several vital steps to take, starting with defining your data strategy and cleaning your data for the highest possible data quality. Quick Takeaways A robust data infrastructure enables…

To embrace digital transformation, a business must completely rethink how it uses technology in its day-to-day operations. Successful digital transformation requires significant changes to a company’s hardware, software, operational practices, and company culture. And to do this, you need high-quality data that you can attain through robust data quality management. Detailed data analysis relies on…

Without adequate data error handling in a data warehouse, the data stored can become unusable. Traditional methods of data error handling rely on a rule-based approach that is difficult to manage and does not easily scale. Today, data warehouses require a more scalable solution that can autonomously monitor large volumes of data, detecting and correcting…

What data ingestion tools should you use in your organization? It depends on what type of data you’re ingesting and how fast you need it ingested. The quality of the ingested data also matters, which you can ensure by supplementing data ingestion with data quality monitoring. Quick Takeaways Data ingestion imports data from multiple sources…

In the 21st century, data has become a natural resource. Like oil or precious metals in the ground, the data created in human digital activities exist in a raw form that becomes valuable when extracted and used. The potential value of extracted, analyzed data has precipitated the development of new methods and practices for processing…