Systems | Development | Analytics | API | Testing

It's 9 PM on Day 5 of the month-end close. Your controller is still at their desk, squinting at the intercompany reconciliation spreadsheet that refuses to balance. The German subsidiary's numbers came in late (again). The currency conversion formulas broke when someone accidentally sorted column B. And tomorrow morning, the CFO needs consolidated financials for the board meeting.

It starts innocuously. Marketing signs up for a new analytics platform. IT renews that enterprise license even though only 40% of seats are used. HR adds another recruiter. Travel spending creeps back to pre-pandemic levels despite hybrid work. Then one day: SG&A as percentage of revenue climbed from 22% to 28% over three years. Your margins are compressed. The board is asking questions.

Here's a statistic that should concern every CFO: according to 2024 research from Accenture, finance teams spend 85% of their time on data triage-gathering, validating, and reconciling numbers. Only 15% of their time goes to the strategic work they were actually hired to do. If that sounds familiar, you're not alone. The reality is that most CFOs today can't confidently answer a deceptively simple question: "Where did this number come from?".

You don’t wake up caring about badges. You care about data that’s actually extractable, pipelines that don’t fall over, and teams that aren’t stuck copy-pasting CSVs. Still, it’s good when the market validates that. In the Europe Grid Report for Data Extraction | Winter 2026, Keboola is a Leader on the Mid-Market Europe Regional Grid for Data Extraction.

At this year’s Big Data London, Keboola CEO Pavel Doležal sat down with data expert Christina Stathopoulos on EM360Tech’s Don't Panic, It’s Just Data podcast to explore what’s next in enterprise data automation. The conversation centered on a quiet but powerful shift in how AI, particularly large language models (LLMs), is changing the way organizations interact with their data platforms - not just by querying them, but by acting on them.

By 2026, all-in-one data platforms will dominate because they deliver faster time-to-value, built-in governance, and AI copilots that actually work. Orchestration-only tools remain powerful for engineering-heavy teams, but most organizations will move to managed platforms that reduce incidents, simplify compliance, and accelerate insight delivery.

Large Language Models (LLMs) are transforming how organizations interact with their data infrastructure, offering unprecedented capabilities for both technical and business users. However, this transformation brings unique opportunities and challenges that vary significantly based on user personas, security requirements, and implementation approaches. This writeup explores these dimensions through the lens of practical implementation using tools like Keboola MCP and various client interfaces.

Joe Reis, author of Fundamentals of Data Engineering, known for practical education and his YouTube content — formerly CEO/co-founder of Ternary Data — is reviewing our Keboola MCP (Model Context Protocol) Server with Claude to list Shopify his tables, run exploratory analysis, export data to BigQuery, and generate a star schema. And you can do the same in minutes! We will go through everything here from one-click setup to best propmts to use MCP with.

If you’re in procurement, finance, or operations at a mid-sized ecommerce or retail company, tariffs aren’t just a political talking point - they’re a silent force eating into your profitability. And the real problem isn’t always the tariffs themselves. It’s the lag between policy changes and when your data catches up. If any of it sounds familiar, we’d love to hear your point of view in the short survey below.

When we officially launched our Model Context Protocol (MCP) server integration on June 12, 2025, we weren't just adding another feature - we were fundamentally changing how data engineers interact with their tools. One month later, the transformation has exceeded our wildest expectations.