During my time supporting quarterly marketing and sales planning, I was responsible for overseeing and validating targets and budget allocation, program strategies, and then ensuring their successful implementation along the quarter. A process known as building and executing the Playbook in the tech industry. To effectively support this mission, I had to develop a 360°…
Category: Data Analytics
From measuring performance to predicting outcomes, how can we design the models, metrics, and methods that turn raw data into business advantage. This section explores the analytical engine behind decisions like scoring systems, financial diagnostics, clustering methods, predictive models, and the architecture that makes it all run.
Crack your Case Like an FBI Analyst: Build a Bulletproof Analysis
The other night, I was watching Law & Order SVU when a scene cut to Morales, the analyst. It was funny, because that’s my name too … Well the analyst was 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…
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…
My Go-To Tech Stack Recipes for Building Practical Data Science Solutions
In competitive industries like Tech SaaS and Finance, data isn’t just an asset, it’s the engine behind every strategic move. As a Data & Business Intelligence (BI) Analyst, I specialize in designing scalable, automated, and stakeholder-facing analytics workflows that transform complex data into business insight. A strong analytics workflow seamlessly integrates data extraction, transformation, modeling,…
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…
Data Science Tech Stack Series: Operating Systems and Interfaces
To start our Data Science journey, we need to select the appropriate hardware and Operating System (1) to manage it as well as the software on the computer. Then, we are offered with two ways to interact with the computer commands, a Command-Line Interface (2) or a Graphical User Interface (3). 1. Operating Systems (OS)…