Finding stories in everyday adventures
We’ve all been there. An organization is stuck in legacy, processes are inefficient, and it takes an army of people to get things don. The solution? Buy a tool. And not just any tool — a SaaS product with a sleek interface, strong G2/Gartner reviews, and a demo that makes the future look like it will build itself. Contracts are signed. Seats are provisioned. Invites are sent. Then, weeks go by and very little changes. …
For most of my career, the build vs. buy decision in software has leaned heavily toward buy, especially in the enterprise. It is understandable. Buy is faster. Buy is safer. Buy comes with support, training, and a roadmap. But lately, I have been rethinking that bias. More accurately, AI coding assistants are forcing me to. Claude, ChatGPT, Gemini, and others have fundamentally shifted the landscape. We are entering a world where build is no longer …
One of the biggest mistakes I see in digital projects? Teams rushing to automate business processes without first asking whether those processes make any sense to begin with. Just because something exists doesn’t mean it’s worth preserving. If a process is slow, unclear, or stitched together with manual workarounds, automating it won’t solve the problem — it’ll lock it in. You’ll make the mess go faster, not better. A Real Example In a former company, …
AI is dominating headlines, boardroom agendas, and vendor pitches. But most of the attention is focused on generative AI — tools like ChatGPT that can create text, summarize documents, or even write code. These tools are impressive. But they’re just one part of a much broader AI landscape. For years, companies have used AI to forecast demand, detect fraud, personalize marketing, and reduce churn. These traditional AI applications may not trend on social media, but …
Read moreAI Is More Than Just Generative: A Strategic Primer for Executives
Retail analytics is one of the most dynamic and mature domains in data. It sits at the crossroads of customer behavior, real-time operations, and constant decision-making. But here’s the truth: while the retail context is unique, the analytics patterns it relies on are highly transferable. Whether you’re in fintech, education, manufacturing, or SaaS, the tactics used in retail analytics—segmentation, forecasting, personalization, optimization—are universally applicable. Retail’s dynamic environment—marked by real-time customer behavior, high transaction volumes, and …
Read more40 Retail Analytics Use Cases Every Data Leader Should Know