A shaky data foundation makes every bold campaign feel like a gamble. The pressure to be perfect is a constant hurdle for teams tasked with syncing systems and reconciling messy spreadsheets.
Google will begin to highlight data quality account issues related to Vehicle ads within Google Merchant Center starting in mid-April 2026. This initiative aims to ensure compliance with Google's Vehicle ads data quality requirements by evaluating both submitted vehicle data and the associated website content for every vehicle uploaded.
On a clear night I set up my telescope in the yard and let the mount hum along while the camera gathers light from something distant and patient. The workflow is a ritual. Focus by eye until the airy disk tightens. Shoot test frames and watch the histogram. Capture darks, flats, and bias frames so the quirks of the sensor can be cleaned away later. That discipline is not fussy.
Salesforce data migration sounds straightforward on paper. In practice, it almost never is. The system goes live, everyone gets access, and nothing seems obviously wrong at first. Then little questions start popping up. A report doesn't quite line up. A dashboard only makes sense after a few extra filters. Sales reps pull numbers into Excel just to feel sure. Before long, Salesforce is technically running, but confidence in the data hasn't caught up.
The real cost of poor observability isn't just downtime; it's lost trust, wasted engineering hours, and the strain of constant firefighting. But most teams are still working across fragmented monitoring tools, juggling endless alerts, dashboards, and escalation systems that barely talk to one another, which acts like chaos disguised as control. The result is alert storms without context, slow incident response times, and engineers burned out from reacting instead of improving.
Handshake began in 2013 as a platform for hiring college grads and launched a human data labeling business about a year ago to serve foundational AI model companies. Cleanlab, founded in 2021, is a startup that provides software for improving the quality of data produced by human labelers. The deal's purpose is primarily to acquire talent, aka an acqui-hire, adding nine key Cleanlab employees to Handshake's research organization.
AI can't just be a feature on the side of the MLS; it has to be built into the core of how we manage listings, deliver insights and support our subscribers' businesses. Rajeev brings a rare combination of deep real estate experience and technology vision that will help us turn AI into real, everyday advantages for the professionals who rely on Bright.
This engine takes topic data schemas, metadata, and test rules as inputs to create a set of FlinkSQL-based test definitions. A Flink job then executes these tests, consuming messages from production Kafka topics and forwarding any errors to Grab's observability platform. FlinkSQL was selected because its ability to represent stream data as dynamic tables allowed the team to automatically generate data filters for rules that could be efficiently implemented.
Marketers instinctively know that better data and higher-quality media drive better outcomes. But bad habits are sticky and they die hard, said Jamie Barnard , CEO of Compliant, a startup that tracks data quality standards across digital media. The ad industry has "a volume-based mentality," said Barnard, who knows that struggle firsthand. He spent nearly 16 years at Unilever as its general counsel focused on global marketing, media and ecommerce before leaving in 2022.
As technology continues to advance and companies look to remain competitive in meeting market demand, the skills that employees will need are also evolving. A growing number of companies are exploring how to address these skills and workforce gaps with artificial intelligence. HR can use AI to reveal "patterns and gaps" and benchmark "current workforce skills against evolving business needs or industry trends," said Lauren Winans, CEO and principal human resources consultant at Next Level Benefits.
The authors have retracted this paper for the following reasons: post-publication, the results were found to be sensitive to the removal of one country, Uzbekistan, where inaccuracies were noted in the underlying economic data for the period 19951999. Furthermore, spatial auto-correlation was argued to be relevant for the uncertainty ranges. The authors corrected the data from Uzbekistan for 19951999 and controlled for data source transitions and higher-order trends as present in the Uzbekistan data.
Yesterday X started rolling out a new About This Account feature, which included what country the account was created from and what country the account is "based" in (which is different from "connected via"). Head of product at X, Nikita Bier, was quick to say that there were " a few rough edges," but promised they'd be resolved by Tuesday.
Within five years, saying your platform offers personalized recommendations will sound as dated as asking someone to rewind the tape. Personalization has already shifted from competitive differentiator to baseline expectation - and the transition is moving faster than most marketing teams realize. The evidence is already visible: 61% of consumers will abandon brands that miss the mark, and 65% expect companies to understand their needs without being told.
Dr. Lora Aroyo, Senior Research Scientist at Google DeepMind, argues that this assumption no longer holds up. Her research at the intersection of data-centric AI and pluralistic alignment challenges the binary worldview that underpins most AI systems. Instead of seeking a single "gold standard" answer, she advocates for embracing disagreement, diversity, and pluralism as the foundation of more reliable, culturally aware AI.
Our industry is rushing headlong toward an AI-powered future. The promise is captivating: intelligent systems that can predict market shifts, personalize customer experiences and drive unprecedented growth. Yet in that race, many organizations are short-changing or even skipping a critical first step. They are building sophisticated engines but trying to run them on unrefined fuel. The result is a quiet crisis of confidence, where powerful technology underwhelms because the marketers don't trust the data it relies on.
On the surface, it seems obvious that training an LLM with "high quality" data will lead to better performance than feeding it any old "low quality" junk you can find. Now, a group of researchers is attempting to quantify just how much this kind of low quality data can cause an LLM to experience effects akin to human "brain rot."
Over 40 minutes, the panel returned again and again to three themes: data quality, organizational alignment and cultural readiness. The consensus was clear: AI doesn't create order from chaos. If organizations don't evolve their culture and their standards, AI will accelerate dysfunction, not fix it. Clean data isn't optional anymore Allen set the tone from the executive perspective. He argued that enterprises must build alignment on high-quality, structured and standardized data within teams and across workflows, applications and departments.
Retail media promises billions in new revenue, but for grocers, the real test is whether their data can deliver. By 2025, RMN revenue is projected to hit $176.9 billion globally, overtaking combined TV and streaming revenues and accounting for 15.9% of total ad spend ( GroupM, This Year Next Year 2024 ). For grocery retailers running on razor-thin margins, this feels like salvation.
"What we had noticed was there was an underlying problem with our data," Ahuja said. When her team investigated what had happened, they found that Salesforce had published contradictory "knowledge articles" on its website."It wasn't actually the agent. It was the agent that helped us identify a problem that always existed," Ahuja said. "We turned it into an auditor agent that actually checked our content across our public site for anomalies. Once we'd cleaned up our underlying data, we pointed it back out, and it's been functional."
The Government Accountability Office (GAO) said that data it reviewed from 23 key US government agencies (out of 24, as the Pentagon was excluded from this report) indicated there were at least 63,934 full-time federal cybersecurity employees, costing the government around $9.3 billion per year. An additional 4,151 contractors were reported to the GAO, and those cost taxpayers an additional $5.2 billion.
