#data quality

#data quality

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.

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.

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."

Almost half of respondents to Ocient's "From Roadmap to Reality" report say that their companies have not experienced meaningful revenue growth from artificial intelligence (AI) investments due to "poor data quality and overtaxed infrastructures." The data analytics firm further found that "security and compliance pressures" are "shaping enterprise deployment strategies that prioritize flexibility and cost savings, driving movement away from the cloud."

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.

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.

We were able to confirm just 11 reported incidents, either directly with schools or through media reports. In 161 cases, schools or districts attested that no incident took place or couldn't confirm one. In at least four cases, we found, something did happen, but it didn't meet the government's parameters for a shooting. About a quarter of schools didn't respond to our inquiries.

Email marketers face numerous challenges in 2025, including low engagement rates, data quality issues, accurately measuring ROI, and personalization. Experts highlight the need for collaboration across departments to overcome these obstacles.

There is a complete reset in how data is managed and flows around the enterprise. If people want to seize the AI imperative, they have to redo their data platforms in a very big way. And this is where I believe you're seeing all these data acquisitions, because this is the foundation to have a sound AI strategy.

The evaluation tasks within the biomedical text mining community face significant limitations, particularly regarding data representativeness, quality, and participant innovation in solution development.