Nicola Askham

I’ve been in business for nine years this month. Here are nine (of the many) things I’ve learnt about data storytelling: 1. As sexy as data visualisation sounds, we’re now saturated in data visuals that are too easy to ignore. 2. The audience decides what works, not me. 3. The hard part isn’t finding numbers, it’s understanding what matters. 4. Without context, data is too easy to misread. 5. The same dataset can tell multiple truths, depending on the lens. 6. People repeat the story back, not the numbers. 7. The majority of people who want to tell data stories don’t, because it’s hard. 8. Data analysts have more power and influence than they realise. 9. People connect with the message, not the method. What would you add? ---- Kia ora, I'm Kat 👋 I wrote The Data Storyteller's Handbook to help improve how we communicate data insights. Check it out here: https://lnkd.in/gWs2kTP Use the code 9YEARS for 50% off the PDF (September 2025 only).

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Inference shall be a differentiating factor among the Data Scientists and an Important Element to focus in the Enterprise level data science and decision making. Even for any organisation that cares about the evidence that could answer the business question need to emphasise the Inference (simple statistical inference to causal inference), Statistical Inference is something helps in building better predictive models, This is the right time that “Noise in the Data Science Community is settling down”, and giving space for such dialogues and unique opportunities! A new practice of DS needs to be “Evidence and Inference” focused! Even if we want to understand the patterns of how adapted Gen AI systems helps in the business problems , how to perform evals of an Agent or a RAG solution - Inferential Mindset is somethting critical! ps - just thought of recording my thoughts and ideas as a video for better communication and engagement! 🙂‍↔️

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All of these graphs contain exactly the same information. But the last one is easier to process and remember. Just one week to go until the RSS conference where I'll be joining a panel chaired by Clare Griffiths to talk about #accessibility in #dataviz. This is a sneaky preview of the plot transformation I'll work through to show how optimising colours and typography can play a part in making graphs accessible, beyond (and including) making sure that the colours are colour-blind friendly. The talk signposts to a bunch of useful resources, which I'll share here in due course!

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The Blueprint for Data Trust is Here Stop Governing. Start Enforcing.   The Provocation
 In today’s AI-driven enterprise, data quality is not a metric, it’s resilience itself. A recent dialogue revealed a core challenge:
   “These practices aren’t just about improving ‘data quality’… they’re about resilience.”
   This insight became our catalyst. If resilience is the true target, what does an actionable Blueprint look like? So we built it - driven by this very provocation.   Three Non-Negotiables for Data Trust 1.    Contracts Must Be Alive
 Metadata contracts, without active enforcement, are nothing but architectural theater. The strongest systems convert policy breaks into real-time alerts and automated tagging, creating closed accountability loops for both producers and consumers. 2.    Lineage Demands Context
 Visibility without accountability is risky. Lineage must be enriched with SLA tags, PII flags, and consumer priorities - transforming simple observability into enforceable governance. Omitting these tags leaves teams exposed to procedural and regulatory failures. 3.    Ingestion Is Security
 Ingestion is now a strategic security checkpoint. Deduplication and validation at this stage aren’t just best practices, they’re frontline breach detectors. In complex AI pipelines, a single corrupted upstream event can cascade undetected, causing system-wide impact.   The Blueprint The Data Trust Architecture Blueprint serves CDOs, CIOs, and platform leaders who demand real outcomes: ·     Enforce trust in real time: Move your approach from passive governance to active, policy-driven enforcement. ·     Automate resilience: Embed blast-radius-aware lineage and self-healing contracts into every pipeline, drastically reducing incident response times. ·     Secure AI & ML futures: Align your platforms with evolving standards for AI risk mitigation, explainability, and ethical oversight. ·     Eliminate fire-drills: Replace costly clean-ups with resilient-by-design operations for lasting business impact.   What’s Next This is Version 1.0, a living blueprint. It’s not just a framework; it’s an executive-led movement to redefine data trust for the age of AI. Your experience and feedback will shape its ongoing evolution and practical value.   Download the Data Trust Architecture Blueprint. Let’s build more than data platforms. Let’s build trust   Transform Partner – Your Strategic Champion for Digital Transformation

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Is YOUR organisation drowning in data… and still flying blind, even with AI at your fingertips? You’re not alone. The truth? Technology isn’t the problem. Transformation is. Every business is collecting more data than ever before. But most are struggling to convert it into decisions, growth, and results. At Data Cubed, the team don’t do “just tools.” They engineer transformation, turning your scattered data into a strategic weapon. Here’s what real transformation looks like: ➡️ From disconnected data to a unified, secure, and governed platform ➡️ From manual processes to automated, AI-powered workflows ➡️ From static dashboards to insights that drive strategic decisions ➡️ From outdated tools to scalable, future-ready data systems ➡️ From no direction to a clear data + AI strategy with real ROI ➡️ From missing skills to on-demand experts, ready to deliver And the results speak for themselves: ✅ £750k saved by automating 15+ FTEs of manual work ✅ $60k cut by switching off unnecessary accounts ✅ 50% uplift in Data Maturity & AI Readiness ✅ £1.5m+ in value created by treating business data as a true asset ✅ +92 NPS from doubling down on client success If your data isn’t accelerating your business… it’s slowing you down. 🧠 Ready to turn your data into momentum? Start by visiting: 👉 https://data-cubed.co.uk/ Or connect with the expert team: Helen Tanner, Alex Smith, and Hassan Moulavi, who are always ready to help. ♻️ Repost to help others in your LinkedIn network struggling with AI and Data. ------- Follow Dr. Kion Ahadi for insights about leadership and technology

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As a Data Architect, I spend a lot of time talking about data quality, but it’s not just a technical checkbox, it’s something we experience every day in real life. Think about it: - If the label on your medicine is printed incorrectly, the data quality failure could be dangerous. - If your GPS misplaces a road, that inaccurate data point can cause frustration (or worse, accidents). - Even something as simple as getting the wrong price at checkout is a reminder of what happens when data integrity is not maintained. In our systems, poor data quality leads to wrong decisions, compliance risks, and loss of trust. In real life, the stakes can be just as high. That’s why, when we design architectures and processes, quality must be built in, not checked at the end. ✅ Validations at entry points ✅ Automated checks throughout pipelines ✅ Governance policies that enforce consistency ✅ A culture where teams understand the impact of quality on outcomes Data is the foundation for AI, analytics, and decision-making. But if the foundation is weak, everything built on top of it becomes unreliable. So, whether in business or daily life, let’s remember: "good data is not a luxury – it’s a necessity." #DataArchitecture #DataQuality #Governance #TrustInData

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