All Posts

Probabilistic record linkage still matters because identity data is messy and match decisions carry real financial and compliance risk. This article explains the intuition behind Fellegi–Sunter and Bayesian record linkage, shows how they control false merges and splits across noisy customer and product records, and points to modern tools and books that help you put these ideas into practice.

Pairwise matching treats each comparison as a one-off. A persistent knowledge graph turns product mentions, manufacturers, model numbers, attributes, and price bins into typed nodes and relations. Matching becomes neighborhood comparison: multi-hop paths (convergent evidence) can beat any single similarity score.

False entity merges don’t just dirty data. They distort inventory, pricing, and forecasts, then every model and report built on top. Learned sparse retrieval improves recall, but it can still treat records like unordered tokens. This post adds token-to-token attention as a structural check so near-duplicates pass and lookalikes fail, with a trail you can audit.

Entity resolution breaks when exact matching is too brittle and dense vectors blur identities. This post introduces SPLADE, a learned sparse retrieval model that keeps inverted indexes and token-level explainability while adding transformer-powered expansion and reweighting. We walk through where SPLADE beats BM25 and dense search, where it can fail on SKUs and over-expansion, and how to run it in Postgres/ParadeDB for large-scale product, customer, or patient identity.

Entity resolution struggles when systems must choose between the rigid precision of BM25 and the fuzzy flexibility of Vector Search. Part 4 reveals why simple linear weighting fails and introduces Reciprocal Rank Fusion (RRF) as the superior alternative. We explore the architectural shift to Hybrid Search, demonstrating how to merge rank positions rather than raw scores using Spring Boot and ParadeDB.

BM25 looks old on paper, but it still decides which records are worth comparing when identifiers can’t afford to be “almost” right. This post walks through the TF‑IDF roots of BM25, how k1 and b shape the scoring curve, and why Lucene, Elasticsearch, and OpenSearch still rely on it. You’ll see how term statistics, not embeddings, keep product codes, SKUs, and customer records anchored during entity resolution.

Solution architects aren’t just “technical.” They translate business intent into systems that scale, integrate, and stay secure—preventing costly silos and rework. In this post, I break down what solution architects actually do, why the role is highly valued, and how enterprise solution architects align AI, data, and platforms to long-term strategy. You’ll also get practical ways to leverage architects for measurable outcomes.

Exact match is easy. Similarity is hard. This post climbs the ladder of structures that make vector lookups fast: linked lists (slow scans), skip lists (express lanes), small-world graphs, and HNSW. Then it shows how pgvector brings HNSW into PostgreSQL so entity resolution can happen where your records already live.

Healthcare providers track the same patient under five name variations. Retailers can't tell when the same SKU is under two different codes. CPG companies buy demand data showing one product with three different names across channels. Supply chains have suppliers that are actually the same company. Every week. Same problem. Different domain. Your data doesn't know what it's describing.

While most organizations chase the latest AI trends, data leaders are building something different: reliable foundations. This isn’t about deploying more agents faster—it’s about assets with lineage, harmonization rules, and semantic definitions that make every AI decision trustworthy. Discover why speed without discipline turns into liability, and how to fund the running back while the quarterback steals the headlines.

Your pricing math is broken. The shift to Agentic AI isn't just a trend; it's a mathematical certainty that will bankrupt you. When precision turns your 10,000-unit sale into a 5-unit transaction, revenue collapses. The fix isn't a bet on outcomes. It's pricing the real work: the expensive LLM infrastructure that translates vague intent into a clean answer. Stop selling the data; start selling the intelligence.

Data is your competitive advantage—but only if it tells a clear story. In this post, I share what transforms business intelligence from overwhelming to actionable: quality data, the right metrics, intuitive tools, skilled teams, and a culture that values evidence. Learn the practical steps to audit your setup, eliminate noise, automate workflows, and embed analytics into daily decisions. Because sustainable growth isn't about chasing trends—it's about refining what matters.

For a decade, the data industry monetized a mistake: we sold the haystack. Customers paid for volume because they couldn't easily find the needle. Agentic AI changes the math. By replacing linear workflows with non-linear intent, AI drives transaction costs to zero—and destroys volume-based revenue models. This series uses econometrics, calculus, and microeconomic theory to engineer a new pricing framework. The assembly line is closing. It is time to price for outcomes.

Minimalist Innovation LLC shows how growth-minded businesses can cut through complexity by focusing on what truly drives sustainable results. Their method centers on refining existing systems—leveraging data, purposeful AI, and minimalist principles to create lasting, streamlined solutions. The approach enables founders and executives to replace clutter and inefficiencies with simple, effective practices, fostering clarity and resilience in scaling organizations.

Early adopters of Agentic AI are driving 6-10% revenue growth. This isn't yesterday's automation (RPA). It's a new class of AI that makes decisions and achieves goals, not just completes tasks. A recent Google Cloud study confirms 88% of early adopters are already seeing ROI. This is how you move from efficiency to profit.

Persuading teams to change is inefficient. Real adoption occurs when the new way of working is simply better. Instead of fighting resistance, build a new model that makes the old one obsolete. Our SHIFT methodology engineers change by designing superior tools and processes that your teams willingly adopt. This is change by design, not by force.

In today's landscape, anticipating the future is a necessity. Predictive analytics insights act as a radar, transforming historical data into a forecast of future outcomes, helping leaders move from guesswork to data-driven decisions. By harnessing this power, companies can unlock new operational efficiencies, predict customer churn, optimize inventory, and drive sustainable growth. This article explores how to use these insights, which tools to choose, and how to build a dat

Your business should fix problems before your customers do. I once saw a company where a missed algorithm alert turned into a customer crisis. The gap between their technical metrics and business reality was costing them money and reputation. We stopped the chaos by building a proactive process to find failures before they happened. Today, Agentic AI can automate this entire feedback loop, turning reactive fire drills into a self-improving operation that protects your bottom

You’ve spent millions on analytics, yet every critical question is a six-week research project. The problem isn’t your data; it’s the hidden "translation tax" you pay on every query. An Agentic Mesh, built on a Headless BI architecture, eliminates this tax. It stops the endless data movement and empowers your team to get verified answers to complex business questions in minutes, not months. This isn't magic; it's modern data architecture. Stop translating. Start asking.

Some customers inevitably churn—and when they do, your growth targets slip through your fingers. This post reveals the precise number of new customers you need each month to outpace natural decay, and shows you how to allocate a fixed acquisition budget across channels using Mixed-Integer Linear Programming. You’ll learn to optimize spend, boost retention, and turn growth planning into a transparent, data-driven advantage for your SaaS or subscription business.