1. Why is data collection still such a challenge for organizations ?

Organizations are collecting more data than ever-captured by different teams, using different tools, for different purposes. For many, spreadsheets and manual data tasks are just as common as digital tools and automated processes.

As more teams collect more data and introduce more tools, integration, quality, and security challenges mount. When data gets stuck in silos, errors go unnoticed, compliance risks increase, and teams struggle to maintain a reliable source of truth. That’s why a structured approach to data collection is critical-ensuring systems work together to provide accurate, accessible insights.

2. How does all of this incoming data impact quality ?

Integration challenges make it difficult-if not impossible-to establish and maintain a single source of truth. What often begins as a quick fix-adding a tool here, a plugin there-can quickly turn into a patchwork of disconnected solutions. Each tool may solve an immediate problem, but together, they create inefficiencies, manual workarounds, and increased security risks. Instead of centralizing data, organizations end up with fragmented systems that prevent teams from accessing a complete and accurate picture of their customers.

These inefficiencies lead to poor data quality, which affects every stage of decision-making. Clean, accurate, and timely data is essential for identifying both roadblocks and opportunities, yet many organizations unknowingly operate on outdated, inconsistent, or incomplete data. Without a single source of truth, they may not even realize the extent of the issue, why it’s happening, or the true impact on their operations.

3. You’ve mentioned security and compliance a few times. What’s leaving organizations vulnerable ?

We recently surveyed 400 data professionals, and 91% said security keeps them up at night. And for good reason-IBM estimates that the average cost of a data breach in 2024 was $4.88 million.

One of the biggest vulnerabilities with data is a lack of clear ownership. When there’s no alignment on who owns data and how it should be managed, teams operate in silos, policies become inconsistent, and accountability slips—creating an environment where security and compliance risks thrive.

Moreover, more than half of the professionals we surveyed reported challenges with data integration and analysis. Without a clear data flow, employees resort to workarounds-exporting sensitive information into spreadsheets and sharing it via email. This disrupts workflows, increases compliance risks, breaks the chain of custody, and makes audits nearly impossible.

4. Organizations want to automate more processes, but getting there can be a real challenge. Why ?

System limitations, knowledge gaps, and budget constraints are common roadblocks. But even when automation is in place, broken integrations or homegrown solutions that weren’t built to scale can prevent data from flowing correctly into CRMs and other core systems.

Ultimately, it always comes back to the data. A structured approach to data collection is the foundation of any successful automation strategy. Once that’s in place, organizations can integrate their systems more effectively, eliminate redundancies, and reduce inefficiencies. From there, automation can be implemented intelligently-replacing manual processes where they make the most impact while maintaining human oversight where it’s needed.

5. Data quality is key to effectively adopting AI solutions as well, right ?

Absolutely. AI is only as good as the data it processes. Without structured, accurate information, AI doesn’t enhance efficiency-it amplifies mistakes. When AI-powered tools are fed incomplete or inconsistent data, they generate unreliable outputs.

AI thrives on high-quality inputs. Clean, well-structured data enables AI to automate tasks effectively, deliver meaningful insights, and optimize decision-making. But without a strong data foundation, even the most advanced AI tools will struggle to deliver real value.