Data cleaning stories
Poor data housekeeping is adding cost, risk and delays to RISE with SAP projects, with some firms losing over EUR 100,000 a month.
Bad records can drive up costs, hurt compliance and damage customer service unless firms keep data accurate as it changes.
Incomplete records can lead to missed allergies, duplicate tests and wrong billing, making data quality more urgent than connectivity.
The new feature could cut Spark job runtimes by up to 4x, easing cloud bills for firms running data-heavy AI and analytics workloads.
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.
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.
