Insights on data preparation, taxonomy standardization, and making enterprise AI actually work in production.
Your infrastructure choice won't save you from bad data. Here's why most enterprise AI projects fail regardless of platform - and what actually matters.
Read articleDatabricks is exceptional infrastructure for AI systems. But it assumes your data is already clean, structured, and semantically coherent. Here's what happens when that assumption fails.
Read articleThe demo works perfectly. The board approves funding. Six months later, the project is quietly shelved. Here's why RAG implementations fail - and the hidden cost of skipping data preparation.
Read articleTraditional oil & gas companies are acquiring renewable portfolios at unprecedented scale. But they can't integrate what they can't classify. Here's why energy sector M&A fails at the data layer.
Read articleAssociates struggle to find precedents. Email attachments never reach knowledge bases. M&A teams recreate data room structures. AI tools underperform. Here's why informal taxonomies create friction.
Read article70-80% of banking AI projects fail due to taxonomy chaos across 30+ years of systems. Basel III stress tests, Customer 360, and risk reporting all break at the same point.
Read articleTraditional integration: 127,000 incompatible SKUs, 24-month timeline, £15M stranded inventory. FireCherry's systematic approach: 16 weeks using superior taxonomy technology.
Read articleMulti-property hotel groups maintain thousands of spreadsheets because taxonomies don't align. Portfolio analytics impossible. Executive dashboards 3 weeks stale. GenAI projects fail. Here's the solution.
Read articleMulti-format operators run convenience stores, supermarkets, forecourts, and wholesale. But incompatible product taxonomies make cross-format analytics impossible. CFOs can't get answers in real-time.
Read articleRegional hire company acquires competitor with 20+ depots. Several months later can't answer: "Do we have a scissor lift available?" 25,000+ pieces coded differently. Equipment taxonomy chaos destroys M&A value.
Read article15 ships with unified shore-side booking systems. But onboard operational data lives in ship-specific formats. Guest spending, service metrics, inventory consumption trapped in silos. Shore-side can't analyze what drives onboard revenue.
Read articleFinancial data provider serves 30,000+ clients with market data and analytics. Grown through M&A. CFO asks: "What's customer churn by product line?" Answer: "Can't calculate reliably-taxonomies don't align across acquired platforms."
Read articleInsurance broker completes 50+ acquisitions in a year. Six months later, CFO asks: "What's profitability by line of business?" Finance responds: "Can't calculate reliably." Different policy taxonomies, claims coding, commission structures across acquired firms.
Read articlePE firm acquires software platform for £200M. Investment thesis: 5-7 bolt-ons, exit at 16x in five years. After three add-ons, synergy realization stalls at 50% of plan. Customer data uses four ID schemes. Product taxonomies don't align. Platform CEO can't answer: "Which customers should we cross-sell?"
Read articleGraph databases answer "how is data connected?" FireCherry answers "what does this data mean, who defined that meaning, and how does it safely evolve?" Graphs store meaning. FireCherry governs it.
Read articleGlobal freight forwarder acquires $14B competitor. Deal promises $800M in synergies. Eighteen months later: only $360M realized (45% of target). Customer data uses incompatible ID schemes. Service taxonomies don't align. Finance can't calculate true customer profitability.
Read articleMost organizations rely on informal classification systems that exist only in institutional knowledge and undocumented Excel files. Here's why that breaks enterprise AI - and what formal taxonomies look like.
Read articleYou bought a world-class engine. Snowflake Cortex provides exceptional AI infrastructure. But engines need refined fuel, and your organizational data isn't AI-ready yet.
Read articleBigQuery processes petabytes serverlessly. Gemini integration brings AI directly to your data warehouse. But scale doesn't fix semantic problems, and SQL won't clean your taxonomies.
Read articleYour ML engineers are your most expensive resource, and they hate data plumbing work. Here's the cost arbitrage that makes outsourcing data preparation a strategic advantage.
Read articleIoT sensors + messy taxonomies = failed digital transformation. Energy operations need standardized asset classifications before AI can optimize anything.
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