Compete by Default, Collaborate by Design

Sophie Clarke

Today we’re going inside one of the more significant strategy shifts Qlik has made in its partnership with Databricks. And it starts with a word swap that changes everything. We used to say: we will partner unless it is competitive. The new stance is: we will compete unless we can partner. That might sound subtle, but it changes how our sellers walk into every single room.

Sophie Clarke

First, some context. Qlik and Databricks have built something real together. Around 400 joint customers spanning the globe. Qlik is tied for number one in driving data to Databricks platforms globally. And we just won the Databricks Global Data Integration Partner of the Year award for 2026. That partnership is not going away. But the landscape has changed, and so has our stance. Databricks is no longer just a data and AI platform we feed. They are now competing directly with us — in analytics with AI/BI Dashboards and Genie, and in data integration with Lakeflow Connect. These are credible products. Our field needs to be ready.

Sophie Clarke

So here’s the new rule: compete by default, collaborate by design. When Databricks is the data and AI platform and the sponsor is a data engineer or platform lead — we collaborate. That co-sell motion is strong and we protect it. But when Databricks is positioned as a BI replacement and the sponsor is a business leader, a CFO, or an executive — we compete. Hard. The field rule is simple: start neutral. Shift assertive the moment Databricks claims the analytics budget or the business decision layer.

Sophie Clarke

In analytics conversations, sellers use four words to cut through the noise. We call it the 4E framework. Everywhere. Everyone. Economy. Exploration. Everywhere means Qlik meets data wherever it has to live — on-prem, regulated environments, hybrid, multi-cloud. Databricks analytics stays Databricks-native. Ours doesn’t. Everyone means Qlik is built for business users at scale — not just technical teams comfortable writing SQL. Executives, field teams, partners, customers. Databricks is improving on this, but it remains a builder-first platform. Economy. Every click in Databricks becomes a SQL query against the warehouse. Compute costs add up fast. Qlik’s associative in-memory engine changes that economics entirely — direct query used selectively, not for everything. And Exploration. A chart answers the question you already know to ask. Associations surface what you didn’t know to ask — what’s related, what’s absent, what’s hiding behind the number. The more complex the reasoning chain, the more Qlik’s value compounds. You don’t need to use all four in every deal. Diagnose the customer’s pain, then lead with the E that hits hardest.

Sophie Clarke

What if a customer has already standardized on Genie as their front-end? Don’t fight it. Pivot to Qlik’s MCP server. We become the intelligence layer powering Genie from underneath. They keep the interface they’ve chosen. They get the full 4E experience — broader data coverage, pre-certified metrics, lower compute costs, and associative discovery beyond what they knew to ask. It’s a strong story.

Sophie Clarke

On data integration, the posture is different. We start partner-first. Databricks is usually the destination we feed, not the competitor we displace. But when Lakeflow Connect is positioned to replace Talend ingestion — that’s the trigger. And then we differentiate with four Cs: Connect, Cost, Confidence, and Choice. Connect: 200-plus sources including the full SAP estate, mainframe, and legacy systems that Lakeflow has no managed connector for. We land the hardest data. Cost: Streaming ingestion on cost-effective compute cuts ingestion spend by up to 80 percent versus warehouse-based approaches, based on Qlik benchmarks. Confidence: Qlik scores data quality in flight and assigns a Trust Score before data lands. AI models fed by Qlik pipelines start from a measured baseline — not just a landing record. Choice: Write once to open Iceberg and mirror zero-copy to Databricks, Snowflake, and Redshift from one pipeline. The target stays the customer’s decision. Lock-in stays off the table. The message is not “pick us or them.” It’s: keep Databricks. Stay open and trusted with Qlik.

Sophie Clarke

So what should sellers carry from all of this? Three things. Lead with the customer’s outcomes, not with features. Anchor every conversation in what the customer is trying to achieve — that’s where Qlik wins. Make a deliberate call on whether you’re partnering or competing. Don’t drift into collaboration by default anymore. Be intentional. And walk in armed. The 4Es for analytics. The 4Cs for data integration. Every scenario has a play. Use it. Databricks matters to our customers. Qlik makes their Databricks investment more powerful. That’s the honest story — and it’s the right one to tell.