From financial institutions to healthcare providers, manufacturers, government agencies, and media companies, every organization is generating more data than ever before. Embracing data is key to staying on top of the competition, pivoting business models, finding new opportunities, and ultimately growing a business. To uncover patterns and stories from large data sets, companies need Data Analysts.
The cornerstone of their role is to provide insights that help businesses make informed decisions. But how do they get there?
We interviewed a few Data Analysts who transitioned to this role after graduating from Le Wagon’s bootcamps. We asked them what their job consists of, what tools they typically use, and how they’re making an impact on their companies.
Defining the question
Performing data analysis starts with a question – for instance, how can we retain more customers? or How can we improve the conversion rate on our website?
Defining this question involves a deep understanding of the needs and demands of the business, but also a process to keep track of metrics and KPIs. To set the right objectives, data analysts usually work with management and other teams like Sales, Marketing, or Product.
RELATED: How To Thrive In The Data Science Industry
Samuel Rasetti is a Data Analyst at Ludia – a video game company. He’s in charge of measuring the impact of new features and updates on players. For instance, he “looks at the effect on players’ retention and behavior, or whether a new feature has generated more revenue”.
Robyn Korolnek is Lead, BI & Analytics at Psycho Bunny – a clothing brand. In her day-to-day, she “runs data analysis to answer questions such as What percentage of customers are coming back? or What type of products do new customers tend to buy?”.
Collecting, cleaning, and preparing the data
Once Data Analysts have a clear objective in mind, they determine the type of data they need to analyze. The format of the data can be quantitative (ex: website pageviews, sales volume, or customer churn rate) or qualitative (ex: client testimonials or emails), and come from various sources such as CRMs, surveys, or social media platforms.
To automate data collection, data analysts can use ETL (Extract, Load, Transform) tools, APIs, and no-code / low-code platforms.
Once collected, the data has to be cleaned and prepared for analysis. This involves removing duplicates, filling in missing values, and formatting the data. Data Analysts spend a fair amount of their time cleaning data!
The next step is to analyze the data using statistical methods and software tools.
To identify patterns, trends, and relationships, data analysts can use different types of analysis, including text, diagnostic, predictive, or prescriptive analysis.
Then, to manage and retrieve information from databases, a lot of Data Analysts rely on SQL (Structured Query Language). This tool allows them to store and combine data from multiple tables.
For higher-level data manipulation and transformation, Python is the most popular programming language used by data workers. For instance, it can be used to perform regression tests and time-series analysis. Data Analysts typically use libraries such as NumPy, Pandas, and Matplotlib to do numerical computer, data manipulation, and generate visualizations.
“While working, I mainly use SQL and Python and my job will almost always involve manipulating tables containing millions of rows and dozens (if not hundreds) of columns. I can’t tell you why but for some reason this makes me really happy,” shared Cyril, Data Analyst at N26.
Visualizing and presenting data
Once most of the analytical work is done, it’s time to communicate findings to non-technical stakeholders. This is a key part of the data analyst role that requires communication skills and the ability to explain complex concepts in a digestible way.
“I like that data analysis bridges the tech side with the rest of the company, and that you need excellent communication skills to be successful.” – Robyn
To this end, data analysts build a story around their findings. This means putting their insights into context and showing how this relates to the question they first asked. Using visual aids (charts, graphs, tables) helps to turn dry analysis into clear, interactive, and accessible supports. Building reports and dashboards can be done with tools like Tableau and Power BI.
Based on their findings, data analysts are expected to make actionable recommendations to help their organizations achieve their goals.
All these steps make the role of a Data Analyst stimulating and central to the success of a business. According to Cyril the best part about his job is “the variety of tasks and tools used. It goes from creating transformation models and query optimization to data visualization and generating interactive dashboards.”
Curious to learn more about a data career? Take a look at Le Wagon’s programs in Data Analytics and Data Science.
Ines Alvergne is the Marketing Lead at Le Wagon.