Qualytics

🚀 We’re Hiring: Join Team Qualytics 🚀 We’re on a mission to make data trustworthy across every enterprise. That’s why we’re building the Augmented Data Quality Platform that automatically detects and prevents data disasters before they happen. We are a remote-first team of data obsessives, engineers, and problem-solvers. We move fast, think big, and genuinely enjoy building together. We’re hiring across multiple roles, including: 🔷 Director of Product Marketing 🔷 Enterprise Account Executive 🔷 Business Development Representative 🔷 Senior Enterprise Account Executive 🔷 Senior Sales Engineer If you thrive on tackling hard problems, enjoy working in high-velocity environments, and want to make a real impact in data & AI, we’d love to chat. 👉 Learn more and apply: https://lnkd.in/eyjwfK4f

The 2025 State of Enterprise Data Governance Report from Board.org noted a nearly even split in data governance structures: 36% use centralized models, 36% use federated, and 29% are hybrid. That split implies there is no “best” model — only the one that aligns with your culture, maturity, and structure. What’s more important than the model is building clear accountability, alignment, and business context around it. ✨ Tip / mini-framework ✨ When evaluating your model, ask: - Who owns data decisions by domain? - How do you enforce consistency across domains? - Which outcomes do you measure (beyond compliance)? If you've shifted your governance model, curious to hear why and how did you manage the transition?

Your data warehouse is the heartbeat of your organization’s decision-making. But without effective governance, it can quickly become a liability instead of an asset. Maximizing data value, minimizing risks, and enabling better decision-making make data governance a cornerstone of effective data management. Here's our guide to best practices of data warehouse governance that fuels reliable, trusted data: 1. Establish a governance framework that supports innovation 2. Bridge the gap between data stewards, engineers, and business users 3. Turn governance from a compliance exercise into a growth enabler Read more: 🔗 https://lnkd.in/e7arzuSG

Data without trust = risk As regulators sharpen their focus on AI and data practices, the pressure is on. But compliance shouldn’t just be a box to check. Trusted data builds: ✅ Confidence in decisions ✅ Safer AI initiatives ✅ Faster go-to-market Compliance is the byproduct — the real value is a stronger, more agile business.

Every Chief Data Officer carries the mandate to deliver trusted data to their organization That responsibility requires more than governance committees and sporadic audits. It requires a strategy that embeds data quality at the core of business operations. The CDO’s Playbook for Data Quality outlines the priorities and practices that move an enterprise from reactive controls to continuous confidence. Read more on our website (🔗 below) or get the rundown right here 👇

Big welcome to Erika Childers, our new Director of Content & Brand! We're thrilled to have you! We continue to grow and have open roles in marketing and sales. If you want to join this small but mighty team, start here: https://lnkd.in/eyjwfK4f

Enterprises are burning through $13M+ each year because of inaccurate, incomplete, or outdated data. Gartner highlights the ripple effects: missed opportunities, regulatory exposure, and slow execution across the business. The impact compounds. Once trust in data is lost, confidence in analytics, models, and decisions follows. Proactive data quality prevents this spiral by catching anomalies at the source, validating integrity continuously, and keeping systems aligned with business needs. Reliable data is the foundation of every decision, forecast, and customer interaction. It's not optional.

Data Observability ≠ Data Quality. Think of it like a package: 📦 Observability tracks how it moves and where it gets delivered. ✅ Quality checks if the contents are correct and who received it. Both are essential. But only data quality ensures the data itself is right — not just how it flows. The best programs combine observability and quality to deliver trusted data at scale. 👇 Full guide on the breakdown of Data Quality vs. Data Observability below.