Data teams today are under pressure. They’re expected to move fast, answer better questions, and support decisions in real time. But most teams still spend hours on repetitive tasks: writing queries, fixing formulas, preparing reports, and explaining numbers again and again. Microsoft Copilot for Data Analysis is designed to change... More details
Nearly a month after Google launched its flagship model, Gemini 3, OpenAI’s announcement of GPT-5.2 has reshaped the competitive landscape of advanced artificial intelligence models. The race has moved beyond merely improving accuracy or boosting benchmark scores, shifting instead toward redefining development priorities and steering technological investments—at a time when... More details
The market size for AI agents reached $7.6 billion in 2025, up from approximately $5.4 billion in 2024. Experts expect this market to grow at a compound annual rate of 45.8% through 2030. At this pace, the market is projected to exceed $47.1 billion by the end of the decade... More details
If we’re being honest, Microsoft is leading one of the biggest shifts happening in data analysis today. This comes from its strong technical advantage and the fact that its tools are part of everyday life for millions of people. Recent reports show that more than 1.1 billion users rely on... More details
If you put low-grade fuel in your car, it won’t break down immediately. But over time, you’ll notice weaker performance, more breakdowns, and damage that might become impossible to fix. The issue isn’t the engine it’s the material that feeds it. The same is true for data. When an organization... More details
For a long time, companies, AI professionals, and data analysts focused heavily on prompt engineering the skill of writing precise instructions that lead to accurate and relevant outputs. And for a while, it seemed like improving the prompt alone was enough to get high-quality results. But as AI tools began... More details
Recent reports show that most of the time spent by data teams is not on analysis itself, but on repetitive manual tasks moving data between systems, checking data quality, and validating records one by one. Studies indicate that about 94% of organizations still rely on processes that could be automated,... More details
Data roles in the Middle East are changing fast. Companies are not just hiring analysts to “build reports” anymore. They want people who can work with messy data, explain results, and support real decisions. This shift is backed by data. Saudi Arabia, the UAE, and the wider GCC are investing... More details
Most companies want to be “data-driven,” but many fall into the same traps. These mistakes waste time, distort insights, and slow down decision-making. Here are the ten most common issues — and how to fix each one in a simple and practical way. 1. Collecting Too Much Data Without a... More details
Before you can build dashboards, run models, or make predictions, you need to understand your data. And the first step in that process is descriptive statistics. Descriptive statistics help you summarise large datasets into a few simple numbers. They show you what’s normal, what’s unusual, and what might need attention.... More details
Databases inside companies — no matter how big or small — always contain errors, duplicates, and missing values. These issues may look minor, but they can distort your analysis and lead to wrong decisions, even if you use advanced analytics tools. Numbers show how serious the problem is. A Gartner... More details
Teams waste hours every week on work that should be automated. Not creative work. Not decision-making. Just the repetitive stuff — copying data, sending follow-ups, moving files, collecting responses, and chasing approvals. Microsoft Power Automate solves this problem with simple automations (called flows) that handle these tasks for you. You... More details
