Data cleaning stories
Poor data quality is still holding back AI and reporting, even as businesses add specialist roles and restructure their data teams.
Invalid customer phone numbers can drive up costs, disrupt messages and weaken fraud checks across marketing and support systems.
Poor data quality can make integrated customer records unreliable, driving wasted spend, compliance risk and manual correction work.
Poor data is derailing AI projects and inflating delivery errors, customer service failures and compliance risks across organisations.
Notebook users can now query local pandas data with SQL and pass results back to Python, reducing data shuffling in analytics workflows.
Data quality is overtaking AI as a top concern in 2026, with CDOs under pressure to prove the information behind automated decisions is trustworthy.
Enterprises risk wasted spending and bad decisions because governance frameworks cannot fix inaccurate data already in their systems.
Poor data quality, not platform failure, is usually why Customer 360 programmes miss expected returns and erode trust across teams.
Poor data quality is now a business risk for Chief Data Officers, undermining AI, customer service and compliance across the enterprise.
Poor-quality data can derail AI projects, leaving businesses with biased predictions, weak insights and higher compliance risk.
Law firms can now cut hidden document data from Outlook attachments without maintaining their own server infrastructure.
Bad contact records can send autonomous AI workflows off course, with errors compounding across thousands of customer actions at once.
Finance teams could see faster automation as Ramp places engineers inside clients to build bespoke AI systems on its platform.
Poor data quality could cost supply chains millions a year, and AI will only magnify errors unless records are cleaned first.
Cloud ERP customers could see deployments compressed to 90 days as Epicor adds AI tools to speed migration and cut disruption.
Poor data is costing firms millions, making record matching vital for cleaner datasets, better decisions and lower compliance risk.
Banks and credit unions could cut manual data prep and gain faster customer insights as Armstrong Bank already uses the combined system.
Small businesses can now pull live bookkeeping data into Excel, Word and PowerPoint without leaving Microsoft 365 or exporting CSV files.
Bad data is costing Australian firms about AUD A$493,000 a year and slowing decisions in mid-sized businesses.
The £10 million funding is meant to help brands cut eCommerce data errors, speed up insights and track SKU-level changes in real time.
