Data management

Data analysis

Careful evaluation of your data reveals innovative insights into your organization, generating business intelligence that sets you apart.
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We help you maximize your decision-making process

Effective decision-making is at the heart of every successful business. Using cutting-edge technologies, precise analytics and in-depth expertise, we transform your raw data into valuable, actionable insights.

Our data-centric approach helps you identify hidden opportunities, understand complex trends and make informed decisions that propel your business forward. Whether it’s corporate strategy, investment or operations, our mission is to give you the clarity and confidence to maximize the impact of every decision you make.

Why analyze your data with experts?

Quick results

The experience we’ve built up over the years gives us the ability to handle the most powerful analysis tools with agility, facilitating in-depth, fast-track studies.

Periodic follow-up

Regular support to help you achieve your goals, while empowering your team to push you further.

Accuracy and bias correction

We are able to guarantee a high level of accuracy in data interpretation, while taking care to minimize potential biases introduced by human or technological factors.

Steps to successful data analysis


Defining objectives

Identifying clear, measurable objectives helps direct analysis efforts throughout the process. They can evolve in line with discoveries made as new knowledge is acquired.

  • Specific and Measurable : Objectives must be clear and well-defined, with key metrics to assess performance.
  • Achievable : Your goals are realistic and you have the resources to achieve them.
  • Relevant: Objectives should be aligned with the overall mission of your company or organization.
  • Temporally defined : There should be a clear timetable for achieving your goals to ensure timely progress.

Data collection and cleansing

We collect all your current data sources and study their structure. This step enables us to understand the extent of the information available. If this information is of poor quality or insufficient to meet your objectives, a data strategy will be essential before continuing with the analysis.

  • Study the structure of current data
  • Select and cleanse relevant sources
  • Set up a data lake to centralize the information to be processed

Iterative analysis

Once the objectives have been defined and the data centralized, our experts can begin their analysis. With fast, flexible processing tools, we can quickly identify the most relevant insights and develop appropriate statistical strategies.

  • Exploratory analyses to identify trends, relationships and anomalies
  • Data modeling involving the use of statistical models, machine learning or other methods to predict trends, make inferences, etc.
  • Drawing conclusions from the results of the analysis. This could include identifying trends, making recommendations, etc.

Communication of results and follow-up

Periodically, we present the results to you in a clear and comprehensible way. This may also involve creating data visualizations to facilitate understanding.

Data analysis is an iterative process. The results of each analysis may lead to new questions or changes in objectives, which may necessitate starting the process all over again.

  • Presentation of results achieved
  • Re-evaluation of objectives
  • Presentation of current data limitations (if applicable)
  • Proposal for additional relevant data collection (if applicable)

Questions and answers

François Lévesque

François Lévesque


(514) 916-3026

Our analyses are meticulously carried out by a team of skilled experts who use two of the industry’s most powerful programming languages: Python and R. These tools are renowned for their ability to facilitate the manipulation and analysis of voluminous data.

Python, with its robust libraries such as Pandas and NumPy, enables our experts to rapidly process massive quantities of data. It also offers visualization capabilities through Matplotlib and Seaborn, making analysis results more comprehensible.

R, for its part, is particularly appreciated for its potential in statistical analysis and predictive modeling. It has a variety of packages such as ggplot2 for data visualization and dplyr for data manipulation.

Together, these tools help our experts to quickly examine data sources, derive accurate statistics and efficiently spot outliers. In this way, they enable us to make decisions based on reliable and relevant information. Together, they offer a comprehensive, accurate and fast analytical approach, essential to meeting today’s data challenges.

The scope and duration of analyses can vary considerably, depending on a number of factors such as your precise objectives, the nature of your questions and the complexity of the data available to you. For example, data analysis to answer a simple question, such as analyzing sales trends from a single data source, can be carried out relatively quickly, often in less than a week.

On the other hand, when faced with a more complex set of questions requiring the exploration and correlation of multiple, heterogeneous data sources, the time required can stretch over several months. These more ambitious analysis projects may involve integrating diverse datasets, processing unstructured data, applying advanced analysis techniques and resolving data quality issues.

It’s crucial to understand that the time required to achieve accurate and meaningful results is highly dependent on the complexity of the task at hand. That said, our commitment is to deliver the most accurate results in the most efficient timeframe, whatever the scale of the analytical challenge.

Data confidentiality is a top priority for us. We have put in place several mechanisms to guarantee the security and confidentiality of the information you entrust to us.

First and foremost, all data is transmitted via secure, encrypted connections. We also use state-of-the-art firewalls and intrusion detection systems to prevent any unauthorized attempts to access our systems.

Data is stored in highly secure data centers, where physical access is strictly controlled. In addition, data is backed up regularly to prevent any possible loss.

In terms of data management, only those employees who need access to certain information to carry out their work are granted access.

Finally, we regularly train our staff in best practices in data confidentiality and security. We also have rigorous policies in place to ensure that all data is managed appropriately and securely.

We are committed to respecting and protecting the confidentiality of our customers’ data at every stage of the process.

The best way to determine whether your organization has enough data to meet its objectives is to start by clearly defining those objectives. Then, examine your existing data to see if it can meet these objectives. For example, if you’re looking to understand customer behavior, your data should cover relevant aspects such as transactions, engagement and returns. If your existing data is insufficient, you should consider collecting additional data. A professional assessment by data analysts can also be very useful.