.jpg)
Let’s start with you. Who are you, and what problem are you trying to solve in social intelligence?
I’m Cara Buscaglia, and my work sits at the intersection of data, technology, and real human decision-making. The core problem I’m trying to solve in social intelligence is simple but persistent: social data is incredibly powerful, yet too often inaccessible, intimidating, or disconnected from how businesses actually operate.
For years, social data lived in isolated dashboards, separate from search, sales data, research, reviews, and customer experience metrics. My focus has been on breaking down those silos and helping organizations treat social intelligence as a living data source, one that reflects real human behaviour and delivers insight into brands, competitors, industries, audiences, and cultural trends in real time.
Through my work with leading agencies and global brands, I’ve helped teams connect social, earned, owned, and behavioral data into unified systems of truth. Whether it’s spotting micro-trends before they scale, uncovering innovation opportunities, enhancing customer experiences, navigating reputational crises, or combating misinformation, the goal is always the same: to make insight actionable, credible, and anchored to a clear business objective.
At its best, social intelligence doesn’t just report what happened it helps organizations anticipate change. My mission has been to democratize that capability, ensuring insight isn’t locked within a single team but embedded across marketing, communications, product, and leadership. With AI, this vision is becoming a reality faster than ever before.
When it comes to social data, what do you think is still misunderstood or underdeveloped?
The biggest gap is the assumption that more data automatically equals better insight. In reality, the hardest and most underdeveloped part of social intelligence is signal clarity, the ability to automatically clean, categorize, and contextualize data based on a specific objective.
Noise, bots, misinformation, and coordinated behavior distort reality. Without addressing that, organizations risk reacting to volume instead of truth. I’ve seen this firsthand while helping healthcare organizations like Moderna build global misinformation frameworks, and while supporting brands through moments of reputational pressure where false narratives can spread faster than facts.
What’s still misunderstood is that social data isn’t a crystal ball. It needs guardrails. It needs correlation with other datasets search, reviews, sales, market research data to validate what’s real and what matters. That’s why I’ve pushed for AI that doesn’t just summarize conversations, but understands context, credibility, and intent making social data more actionable based on real customer problems.
Until we fully solve for trust, relevance, and alignment to research goals, social intelligence will remain underutilized. The future lies in systems that surface meaning, not just mentions, driving action, better decisions, and ultimately enabling attribution to the true holy grail: ROI.
What’s something you’ve seen lately—a trend, tool, or behavior—that felt like a glimpse of the future?
The most exciting shift I’m seeing is the rise of connected intelligence particularly through MCP-style connectors that allow multiple datasets to speak to each other seamlessly. When social data is combined with search, reviews, market research, CRM, or sales data, insight becomes measurable in ways it never was before.
I’ve already seen glimpses of this future by unifying the audience, search, syndicated research, reviews, and social data into a single intelligence layer, one that delivers far richer context and confidence. Increasingly, this intelligence is going to be activated through a team of AI agents that can partner with humans on analysis, strategy planning, and execution handling scale and complexity while leaving judgment, strategic decisions and creativity to people.
What’s still missing, and what I hope to see next, is automation that does this intelligently by default. Tools shouldn’t require users to manually stitch data together. They should understand the question, surface the right signals, and activate AI agents to support decision-making and follow-through fitting naturally into existing workflows to make teams faster, more efficient, and more strategic.
When that happens, social intelligence stops being a reporting function and becomes an ROI engine. That’s the future I’m working toward.
If we want social intelligence to be more than a tech category, what needs to change in how we build or buy the tech?
Social intelligence shouldn’t be treated as a standalone category at all. It’s not “nice to have” technology, it's a foundational data source that reflects how people, brands, regulators, professionals think, feel, and behave at scale.
What needs to change is how we build technology around outcomes, not features. Too much tech is still optimized for power users, rather than for the broader organization that needs access to insight. I’ve been intentional about pushing for platforms that democratize data so insights reach marketers, communicators, product teams, and executives alike.
Buyers also need to stop asking, “What does this tool do?” and start asking, “What decisions will this help us make?” That mindset shift changes everything from how tools are designed to how success is measured and ROI is calculated.
When social intelligence is integrated into daily workflows and connected to other data sources, it becomes a shared language across teams. That’s when it transcends category labels and starts driving real business impact and ROI attribution.
What’s the hardest part of turning data into action—and how do we make that easier without dumbing it down?
The hardest part is expectation management. Data is often treated as predictive certainty, when in reality it’s directional intelligence. Insight only becomes powerful when it’s tied to a clear objective and validated against other signals or datasets.
I’ve learned that action doesn’t come from simplifying data, it comes from structuring it around the right objective and audience. That means defining what matters, filtering out noise, and correlating insights across sources. When we helped major customers blend reviews, social, and contact center data, or supported them in understanding consumer shifts during periods of political change, the value came from context.
The solution isn’t to dumb data down, but to design systems that guide interpretation. AI can surface patterns at scale, but humans still need to ask the right questions. When technology supports that partnership, data becomes a catalyst driving outcomes, enabling better decisions, and deepening relationships with key stakeholders and audiences.
What’s a quote, concept, or model you return to often when things get messy?
I come back to the idea of systems of signals. Insight doesn’t arrive fully formed; it emerges through iteration, contradiction, and sometimes discomfort. A bit of mess is necessary before structure appears.
When building intelligence frameworks or guiding teams through ambiguity, I remind myself that clarity is created, not found. You test, refine, connect signals, and slowly patterns emerge that can actually drive action.
That mindset has shaped how I build both technology and teams. Progress doesn’t require perfection, it requires momentum, curiosity, and the willingness to sit with complexity long enough to make sense of it.
What’s the last non-work thing you read, watched, or played that reshaped how you think?
Positive Intelligence by Shirzad Chamine genuinely changed how I show up in the world. It reshaped how I think about relationships, leadership, and my own internal dialogue.
The biggest takeaway for me was the idea that how we respond especially under pressure matters more than the situation itself. That perspective has influenced how I navigate complex client moments, mentor others, and even how I approach innovation.
In an industry driven by speed and change, that grounding has been invaluable. It’s reminded me that progress is as much internal as it is technical and that empathy, awareness, and resilience are as critical as any tool we build. “The Sage perspective accepts every outcome and circumstance as a gift and opportunity.”
Get Social with SILab
