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The Multi-Entity CFO's Financial Intelligence Guide

Your finance team wastes 14 days every month on manual consolidation. Your close stretches to 15-25 days. And you're one key person departing from chaos. The brutal math: Your "free" Excel process actually costs $850K+ annually in wasted time, errors, and missed opportunities. Meanwhile, leading multi-entity CFOs just cut their close time by 80% and freed their teams for strategic work instead of reconciliation hell.

From Excel Hell to AI-Powered Finance: A CEO's Journey to Data-Driven Decision Making

"We were wasting too much time debating the accuracy of numbers as opposed to using that time to make decisions." That's how Satty Saha, Group CEO of CreditInfo-a credit bureau operating across 30+ countries-described the moment he realized his organization had a data problem. Not the kind of data problem you'd expect from a company whose business is data and analytics. An internal data problem.

The Month-End Close That Never Ends (Until Now)

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.

The Silent Profit Killer: Why Overhead Costs Creep Up (And How to Stop Them)

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.

The 85% Problem: Why Your Finance Team Spends Most of Their Time Not Doing Finance

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?".

Keboola named a Leader on the G2 Mid-Market Europe Grid for Data Extraction

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.

Context Is the New Code: Inside Keboola's Vision for Agent-Powered Data Workflows

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.

Top Data & Business Intelligence Platforms 2026

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.

Opportunities And Challenges When Using LLMs In The Data Space

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.

Follow Along: Joe Reis Reviews Keboola MCP Server

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.