Data Insights Utilization

Stop pasting interview transcripts into ChatGPT and asking for a summary. You’re not getting insights—you’re getting blabla. Here’s how to actually extract signal from qualitative data with AI. A lot of product teams are experimenting with AI for user research. But most are doing it wrong. They dump all their interviews into ChatGPT and ask: “Summarize these for me.” And what do they get back? Walls of text. Generic fluff. A lot of words that say… nothing. This is the classic trap of horizontal analysis: → “Read all 60 survey responses and give me 3 takeaways.” → Sounds smart. Looks clean. → But it washes out the nuance. Here’s a better way: Go vertical. Use AI for vertical analysis, not horizontal. What does that mean? Instead of compressing across all your data… Zoom into each individual response—deeper than you usually could afford to. One by one. Yes, really. Here’s a tactical playbook: Take each interview transcript or survey response, and feed it into AI with a structured template. Example: “Analyze this response using the following dimensions: • Sentiment (1–5) • Pain level (1–5) • Excitement about solution (1–5) • Provide 3 direct quotes that justify each score.” Now repeat for each data point. You’ll end up with a stack of structured insights you can actually compare. And best of all—those quotes let you go straight back to the raw user voice when needed. AI becomes your assistant, not your editor. The real value of AI in discovery isn’t in writing summaries. It’s in enabling depth at scale. With this vertical approach, you get: ✅ Faster analysis ✅ Clearer signals ✅ Richer context ✅ Traceable quotes back to the user You’re not guessing. You’re pattern matching across structured, consistent reads. ⸻ Are you still using AI for summaries? Try this vertical method on your next batch of interviews—and tell me how it goes. 👇 Drop your favorite prompt so we can learn from each othr.

Everyone wants AI magic. Few want to invest in the plumbing. We live in a moment where the promise of AI is louder than the reality of data. I see it every week with teams. The excitement is real. The budgets are flowing. The pilots look impressive. But when you lift the lid a different story shows up. Data that lives in twelve places. Metrics that mean one thing in finance and another in operations. Ownership that sounds like “we think it sits with them”. Governance that feels optional. And still we keep sprinting toward AI, hoping it will smooth over the cracks. It never does. It makes them more expensive. Here is the part no one markets: AI built on weak data creates more rework, more delays and more organisational friction than leaders expect. If you want real value, start with the boring foundation: → Clear definitions → Reliable data → Agreed owners → Confidence in the outputs When these are in place AI finally becomes what everyone wants it to be: simple scalable repeatable. So here is my question for every exec team: What is the data truth you have been quietly avoiding? ♻️ Repost to help someone get their data AI-ready. 🔔 Follow Clare Kitching for insights on unlocking value with data & AI. 💎 Get more from me with my free newsletter here: https://lnkd.in/ghBtk6jR

We optimize ads based on what we see - but without conversion tracking, we’re often making decisions in the dark. In this new video I created with LinkedIn for Marketing, I break down why conversion tracking is crucial and how to set it up effectively in Campaign Manager. From installing the Insight Tag to tracking event-specific actions, I’ll guide you through the steps that can turn your campaign data into real, actionable insights. I also share a real example of how conversion tracking helped us uncover which ads were actually driving demo requests and it wasn’t the one with the most clicks. 🎥 Watch the video to learn how to get started 👉 For more best practices, visit  https://lnkd.in/eBN7a7kj #LinkedInAds #CertifiedMarketingExperts #ConversionTracking #B2BMarketing #DigitalAdvertising #linkedinadvertising

New Update: Amazon DSP campaign and creative APIs are now generally available. This is a build on many of the announcements from #unBoxed2024 What is it? This new feature allows users to create, read, and update their Amazon DSP campaigns, ad groups, targets, and creatives through a programmatic interface. How does it work? These APIs enable technology providers and advertisers to develop custom experiences within their own applications and seamlessly run Amazon DSP campaigns within existing workflows. The new APIs can be used in conjunction with existing audience and deal resources, providing a comprehensive toolkit for end-to-end campaign management. Users can now store Amazon DSP campaign data locally, simplify campaign and creative creation, and automate optimizations to maximize campaign performance. Why should I care? This update is a game-changer for Amazon DSP users. Here's why it matters: 1. Efficiency boost: Streamline your campaign and creative creation process, significantly reducing activation time for new campaigns. 2. Better data control: Store and manage Amazon DSP campaign data locally, giving you more control over your data and analytics. 3. Custom optimization: Automate optimizations across campaign, ad group, and targeting settings, allowing for data-driven decisions on bids and budgets. 4. Seamless integration: Easily integrate Amazon DSP into your existing tech stack, enabling you to track campaigns in your own tools and sync campaign metadata with your data storage solutions. 5. Performance improvement: Experiment with new audiences and quickly remove underperforming ones to maximize campaign performance. 6. Real-time adjustments: Automatically adjust bids and budgets in real-time, ensuring your campaigns are always performing at their best. Bottom line: Whether you're a large agency or tech partner looking to integrate Amazon DSP more deeply into your operations or an individual advertiser seeking to automate and optimize your campaigns, these new APIs offer exciting possibilities to enhance your advertising efforts on Amazon's platform. Want to check it out? You can learn more about these new features at the Amazon Ads website (https://lnkd.in/gESdMWhy). For those ready to dive in, check out the developer guide (https://lnkd.in/gQAPRdcs) and reference documentation (https://lnkd.in/gBV-BbVb) to start leveraging these powerful new APIs in your advertising strategy.

The most revealing insight in the 2025 Verizon Data Breach Investigations Report isn't what it says about AI security—it's what it doesn't say. The report documents that 15% of employees routinely access commercial GenAI platforms, with 72% using non-corporate emails and 17% using corporate emails without proper authentication. It shows synthetic text in malicious emails doubling over two years. It reveals third-party involvement in breaches exploding from 15% to 30%. But scan all 104 pages for insights on security incidents involving locally-deployed AI systems, and you'll find... nothing. It's all about cloud AI. This is our current AI security reality: visibility into cloud AI risks but a complete blind spot for local AI deployments. Not because these systems are inherently more secure, but because we haven't built the monitoring capabilities and expertise to detect and report on these incidents. The DBIR shows exploitation of vulnerabilities as an initial access vector grew 34% to reach 20% of breaches. Without proper monitoring of local AI systems: 💥 How will you detect similar exploitation patterns in your internal AI deployments? 💥 What visibility do you have into the 46% of non-managed devices with corporate logins that the report identified as particularly vulnerable? 💥 How will you assess whether your local AI systems face the same authentication weaknesses documented in cloud platforms? Perhaps most critically, the monitoring gap reveals a skills gap. The security leaders who will thrive in the coming years are already building teams that can: 💥 Develop detection and monitoring capabilities for AI systems regardless of deployment model 💥 Apply the DBIR's lessons on credential security (credential theft present in 54% of ransomware victims) to all AI deployments 💥 Translate cloud AI security learnings to local environments and vice versa Forward-thinking Australian security leaders can address this monitoring asymmetry by: 💥 Developing consistent visibility across all AI deployments 💥 Creating security architectures that apply AI security lessons and BPs across deployment models 💥 Building teams with cross-domain expertise in AI security 💥 Sharing intelligence to create industry-wide visibility into the current blind spot The DBIR's silence on local AI security isn't a reason for complacency—it's a call to action. Local AI deployments have tremendous value, not to mention no vendor lock-in and building your teams capabilities and talent. You might not think you need an AI security strategy today, but by the time next year rolls around, you'll need to have a compelling narrative about why you don't. #AISecurityStrategy #DBIR2025 #SecurityIntelligence #TalentDevelopment

When you’re starting out as a business owner, staying connected with your customers feels natural—it’s part of the grind. But as your business grows, keeping up with the subtle changes in what they need can get tricky. That’s where AI-driven customer insights can make a big difference. I’ve seen firsthand how these tools can be a game-changer, and I want to share what I’ve learned with the entrepreneurs in my network. Instead of manually sifting through feedback or trying to spot trends on your own, AI does the heavy lifting. It picks up on trends in customer behavior, sentiment, sales, and more that might be hard to catch when you’re busy managing day-to-day operations. The best part is that it removes the guesswork and manual effort from the process, allowing you to focus on what really matters: growing your business and delivering exactly what your customers need. And don’t worry, diving into AI insights doesn’t have to be complicated There are plenty of easy-to-use tools designed for scaling businesses (including the insights tools we offer at RingCentral). I’ve dropped a few resources in the comments to help you get started. ⬇️ Let your data work for you, not the other way around!

AI and data are changing how we protect our organizations, and there are some smart ways CISOs can make the most of these tools. First, machine learning helps spot unusual behavior by analyzing tons of data in real time—things like odd login times or unexpected scripts running. Yet, models need to keep learning, so regularly updating them with new info and analyst feedback is key. Bringing data scientists into security teams can really sharpen threat detection by tailoring insights to your specific setup. Plus, custom AI models can help hunt threats, spot vulnerabilities, and even flag AI-generated attacks. Transparency is important too. Explainable AI helps everyone understand why a system flags something, building trust and better decisions. At the end of the day, close teamwork between security pros and data experts makes all the difference. #AI #MachineLearning #Cybersecurity #CISO

Dear New Business Analyst, You've probably heard about the Fourth Industrial Revolution, which refers to the wave of generative AI tools that have been creating a buzz since OpenAI launched ChatGPT in 2022. Depending on one's perspective, this technology is a game changer, particularly in business analysis. Business analysis requires strong analytical skills, problem-solving abilities, and excellent communication. AI tools won't change that, but they will help you work faster and more accurately, especially with repetitive or generative tasks. This gives you more time for higher-level strategic thinking. As a new business analyst, the real advantage of utilising AI stems from understanding the fundamental business analysis techniques and integrating these tools with your expertise. You can gain an edge by learning to incorporate these tools into your workflow. First and foremost, it's important to perceive AI as a valuable productivity tool in the workplace. Just as you depend on Excel spreadsheets or PowerBI to create dashboards (if you work with datasets), AI can fulfil a similar role in simplifying tasks and helping you excel. Here are a few ways you can utilise AI to enhance your skills as a Business Analyst: ✅ Data Analysis: AI can quickly analyse datasets, identify patterns, and provide insights that would take hours to uncover manually. ✅Automated Report Generation: Instead of spending hours formatting reports, AI tools can generate well-structured summaries and reports from raw data, helping you effectively communicate findings. ✅User Story Generation: With AI, you can enter the right prompt, depending on your requirements, and get a string of user stories you can modify and add to your product backlog. ✅ Enhance Communication: Utilise AI to draft documents, emails and presentations. ✅ Skills development: As a new BA, you can ask questions as prompts and receive simplified explanations with potential use cases, making AI your learning companion. Here is an example of how you can use AI as a productivity tool: You receive an email from your team containing an attached Excel file filled with data. Your team requests your insights on the data within an hour before meeting with a stakeholder. Using AI, you can quickly respond to the email, analyse the large datasets, identify patterns, present insights, determine next steps, create an agenda, and generate meeting notes. It's like having a superpower. Remember, AI won't take over the world. It's the people who effectively use AI that will thrive. #businessanalyst

How we've leveraged insights from LinkedIn Ads data to narrow in on our ICP When we first launched Impactable's LinkedIn ad service, our approach was "the broader, the better." Like most early startups, we believed that anyone with a business online was a potential client. Over time, the demographic data told a different story. Targeting everyone diluted our impact. Analyzing click-through rates, conversions, and client intake forms began to paint a clearer picture of who was truly benefiting from our services. This insight was a game-changer. We began to pivot, concentrating on sectors where we saw the most traction and intentionally stepping back from markets that, while initially appealing, didn't align with our strengths as we scaled. This strategic shift wasn't just about cutting out less profitable sectors; it was about doubling down on where we could make the most significant difference. By niching down based on data insights, we could tailor our services, hone our expertise, and ultimately, deliver more value to our clients. The shift wasn't just about who we chose to serve but about becoming the best at serving them. #DataDrivenDecisions #MarketingStrategy #LinkedInAds #NicheMarketing