Tag: mid-market
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Why do data leaders last under three years, and how do you make the role stick?
Data leaders churn because they run data as a control function nobody asked for, while the value they create stays invisible to the board. Make the role stick by operating as a business partner tied to a P&L.
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How do you measure marketing across offline and digital channels?
Digital gets over-credited because it is easy to track. Offline and brand do the work and take the blame. The fix is marketing mix modelling, with geo-lift experiments as the tie-breaker.
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How do you handle consent and privacy without killing personalisation?
Privacy rules feel like they block personalisation because consent lives apart from the profile it governs. Treat consent as a first-class data type: captured once, enforced everywhere. Then it stops fighting revenue.
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How do you deploy AI you can trust, and govern hallucination?
You cannot deploy AI to customers if you do not trust its output. The fix is not more caution. It is AI governance as a function: provenance, human review, explainability.
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Why did your CDP become another silo, and what replaces it?
You bought a CDP to end the single-view problem. Layered onto fragmented systems, it became one more isolated store holding partial profiles. The fix is composable: the warehouse as the single store, with identity, modelling, and activation as services on top.
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Is your data ready for AI? Why most AI projects stall in pilot
AI does not transcend the data underneath it. Feed it fragmented, dirty data and it scales the mess. The fix is to treat data readiness as the precondition for AI spend, not a parallel workstream.
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The Governance Trap: How You Are Killing Your Own Data (3 of 3)
Half your data decay is self-inflicted. You capture age instead of DOB. You expire cookies faster than the law requires. None of this is the regulator fault. It is a design choice you can reverse.
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The Playbook: Act Before the Data Decays (2 of 3)
You do not need better data. You need to act on the data you already have before it decays. A practical playbook for mid-market CDOs.
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The Half-Life of Data (1 of 3)
Data has a half-life. It is worth the most the day you capture it, then it decays. Here is the decay curve by data type, and the one move that beats it.
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Customer Data Maxxing
Use your teams energy to maximise your customer data. Everyone’s “data-driven” now. Sure they are. And yet most mid-sized businesses are sitting on years of customer data and using a fraction of it. It’s the gym membership of business assets — paid for, rarely touched, vaguely guilt-inducing. Here’s the maths that should annoy you. You’ve…