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Qlik’s new CEO discusses why he joined the company, the opportunity he sees at the intersection of data and AI, and what customers and partners can expect from his leadership. Qlik CEO Saugata Saha recently sat down with Jessica Dubois, Senior Director of Global Tech Partners, for his first external conversation since joining the company.

At Datadog DASH in the spring, the Tricentis NeoLoad team met folks in all types of roles – developers, test engineers, CoE leads, and SREs – and one recurring theme we found was that SREs often didn’t know much about how performance validation happens at their companies. That’s a fair division of labor. Seemingly, the performance team’s work would be related but not mission-critical to the infrastructure team’s. But that’s not entirely true.

Website monitoring often feels like choosing between casting a net or wielding a spear. Cloud-based monitoring is your fishing net: you deploy it broadly, covering vast swaths of digital water, capturing issues wherever they occur. It’s automated, relentless, and covers every corner of your online presence, from global uptime to minute performance blips. On the other hand, on-premise monitoring is the spear – deliberate, targeted, and controlled.

GDPR data mapping has evolved from a one-time documentation exercise into continuous operational reality. With €7.1 billion in cumulative fines and regulators increasingly scrutinizing actual data practices versus paper compliance, organizations need tools that provide genuine visibility into where personal data lives, how it flows, and who processes it. The critical gap in most "GDPR compliance software" is that they help you document compliance without actually scanning your data infrastructure.

Database mapping sits at the core of every successful data integration, migration, and ETL project. It's the blueprint that determines whether your customer records, sales figures, and operational data arrive accurate and usable, or quietly broken. With organizations managing dozens of data sources in mid-market environments, getting database mapping right has become essential for maintaining data integrity across increasingly complex tech stacks.

Ask any storage team what has changed over the last five years, and you'll hear a version of the same answer: everything grew and became more complex all at once. More applications, more data, more platforms, more places for a problem to hide. Complexity outpaced the teams meant to manage it. The staffing math makes it worse. Two-thirds of data center operators now struggle to hire or retain qualified staff.

Every school, college, and university is being asked the same question right now: Can we use AI on our own data without putting student records at risk? The promise is real, including personalized learning, faster advising, and smarter operations. So is the fear. AI that touches student information runs straight into FERPA, breach risk, and a simple trust problem: once data leaves your control, you can't govern it.

Testlio's Tom Chmielewski recently sat down with Bozhena Labinska, Merchant Integrations Lead at Solidgate, to explore how teams can avoid payment failures that never trigger an alert.

You can ask two AI tools the same question about your data and get different answers, even when both have access to the same system. There are several reasons this can happen. Each one might run a different model, or have a different set of tools available. The data you thought was the same might not actually be the same. Or one tool might have more context about my account than the other. I want to focus on what happens after I rule those things out: each tool still has to decide what my question means.