Marketing’s potential to deliver results relies on data quality, but data accuracy, consistency, and validity continue to be a challenge for many organizations. Inconsistent data quality is holding ...

Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic, and definitions among user groups. A universal semantic layer resolves those ...

Deepak Yadav is an Engineering Leader at Amazon, Data & ML expert, ex-Ask.com, formerly with Amdocs, and Data Influencer. Over the years, I’ve worked with organizations across industries—financial ...

Corporate actions may be one of the most technical corners of financial services, but the conversations at the CorpActions 2025 conference, held in London on 4 November, landed on something far more ...

Data cleaning is a crucial step in the data analysis process. Inaccurate, incomplete, or inconsistent data can lead to flawed insights and poor decision-making. Fortunately, Excel 365’s Power Query ...

One thing I’ve consistently observed as CEO of intelligent pricing platform Competera is that data remains a retailer’s most valuable asset. Whether it’s managing data through data hubs or data ...

In today's data-driven healthcare landscape, medical imaging stands at the forefront of diagnosis and treatment planning. From X-rays and MRIs to CT scans and ultrasounds, these images provide crucial ...

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Yet, unfortunately, many organizations are struggling to maintain clean, actionable data. In fact ...

Learn the definition of data quality and discover best practices for maintaining accurate and reliable data. Data quality refers to the reliability, accuracy, consistency, and validity of your data.