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…
Author: Marine Morales
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 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,…
Defining Your Analytical Role: Planning vs. Running the Business
In every organization, success hinges on the ability to balance visionary strategic planning with effective day-to-day execution. This article aims to clearly delineate the two critical pillars of organizational success: business planning and business execution, represented respectively by decision-makers [1] and contributors [2]. Decision-makers shape the future by defining the vision, mission, and strategy, while…
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…
Decoding the Business Analyst’s Journey: From Identifying Needs to Delivering Solutions
The Business Analyst’s role is to bridge the gap between business needs and solutions. The Business Analyst (BA) adds value by identifying needs _ problems and opportunities _ and by finding solutions for the organization. It involves understanding how different changes (in process, people, systems or technology) can improve efficiency, productivity, or profitability. Business Analysis…
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…