Blogs to share thoughts, experiences, and knowledge.

Overview In every industry today—finance, retail, government, tech, even small business—the real insight often isn’t found in dashboards or formal reports. It lives out in the open: in news headlines, discussion threads, product reviews, and social media conversations. If you can gather that information and understand the tone behind it, you get a clearer picture of what people are thinking long before the trend becomes obvious. That’s the value of combining web scraping  wit

Overview This solution reads SAP’s built-in DVM statistics (from /SDF/DVM_* tables) using ABAP, exports the data to CSV, and feeds it into a Python analytics engine for: Predicting table growth (using Linear Regression) Detecting anomalies (via Z-Score) Recommending archiving or retention actions Visualizing everything in a Streamlit dashboard PART 1 — ABAP Extractor (ZDVM_EXPORT) Purpose Export SAP DVM statistics (table sizes, growth) from Solution Manager or connected syste

Extending SAP Joule with prescriptive intelligence. When SAP Joule  appeared, it gave supply-chain teams a new kind of visibility — the ability to talk  to their data.A planner can simply ask: “Which materials in US01 haven’t moved for 180 days?”“What’s the stock value by age band?” and Joule answers instantly from live SAP tables.That’s powerful — but it’s only the first step. AIS-IO™ (Inventory Optimizer)  picks up where Joule leaves off.It adds predictive insight and opti

When SAP Joule  arrived, it gave enterprise users something that had been missing for years — a natural conversation with SAP. For the first time, finance teams could simply ask: “What’s our DSO this quarter?” “Which customers are behind on payments?” and Joule would respond — instantly, contextually, and conversationally — using real SAP data. That’s a leap forward.But after working with Joule, I began to notice something deeper. Joule can tell you what happened. But it does