The other night, I was watching Law & Order SVU when a scene cut to Morales, the analyst—which was funny, because that’s my name too—briefing the team on critical data he’d pulled from a suspect’s USB key. It was an Excel spreadsheet. But Morales didn’t just dump a bunch of analyst jargon on them. No,…
Category: Data Analytics
From understanding consumer behaviour to predicting market trends, how can we transform the endless stream of data that surrounds us into actionable knowledge.
Financial Analysis To-Go: No Fluff, Just Liquidity Ratios
Ok, you’re in HQ, coffee in one hand, phone buzzing in the other. The Quarterly Business Review (QBR) is in full swing, and the execs are flashing financial results on the big screen live. No time to blink. Your boss leans in: “So… we need to brief the team after the QBR. Can we pay…
How MarTech & RevOps Achieve Operational Excellence
When you start working as a data analyst, you expect your role to be clear-cut—transform raw data into actionable insights, build dashboards, and uncover trends that guide decision-making. What you don’t expect is just how closely you’ll end up working with MarTech and RevOps—until, little by little, you find yourself doing their job too. At…
My All Hands Rosetta Stone : Defining B2B Core Business Objectives & Missions
I’ve attended numerous Town Halls, All Hands meetings, Annual Strategy Summits and Quarterly Business Reviews in large corporations. The purpose of these meetings was to update and align the entire company on high-level goals, performance, and strategic priorities. The problem is that each meeting was usually outlining their own version of the four pillars of…
My Favorite Data Science Tech Stack Recipes
I specialize in optimizing customer acquisition, engagement strategies, and pricing models in highly competitive, fast-paced industries like Tech and Finance. In these environments, data-driven decision-making isn’t just an advantage—it’s a necessity. To stay ahead, businesses must rely on robust analytics frameworks that seamlessly integrate data extraction, transformation, modeling, and visualization to drive strategic insights. Over…
Exploring Essential KPIs and Tactics in Data Analytics
In the dynamic world of data analysis, a Data Analyst plays a pivotal role in steering a company towards success. The primary goal of a Data Analyst is to provide actionable insights based on data to improve performance. His responsibilities span a broad spectrum, encompassing performance assessment, future performance forecasting, target development, and the shaping…
Mastering the Art of Data Engineering: A Step-by-Step Guide to Building Robust Data Systems
A Data Engineer (DE) is responsible for building a robust data environment by developping and maintening scalable databases, data pipelines and architectures. He focuses on the infrastructure and mechanics of data handling, ensuring that data is properly collected, stored, processed and made accessible for various analytical and operational needs. By enabling efficient data analysis, the…
Data Science Tech Stack Series: Data Management Systems
As data scientists, one of the most important elements of our tech stack is the system we choose to connect to our working environment in order to manage our data. A Data Management System is responsible for managing and organizing large volumes of data throughout the organization lifecycle. It encompasses not only the software but…
Data Science Tech Stack Series: Data Storage
Data storage forms the foundation for storing, managing, and processing data. In this post, we will explore two different approaches for storing data including on-premises storage solutions (1) and cloud-based storage solutions (2). 1. On-Premises Storage On-premises storage, also known as local storage, refers to storing data on servers located within an organization’s premises. Users…
Data Science Tech Stack Series: Working Environments
When performing Data Science, it is crucial to choose the right working environment. Our goal is to optimize our workflow efficiency during the various stages of data wrangling, coding, collaboration, and results interpretation. In this article, we will explore three types of environments with a progressive set of features: code editors (1), integrated development environments…