What we do
Specialized in data processing and analysis.
We help businesses extract value from their own data.
Identification and collection
Identify relevant data sources to address specific client needs. Once the data sources are identified, we proceed with extracting raw data from these sources. This may involve using APIs (Application Programming Interfaces), database queries, web scraping, artificial intelligence, or other methods to collect the necessary data.
Cleaning and preprocessing
Raw data may contain errors, duplicates, missing values, or other inconsistencies. Therefore, we apply data cleaning techniques to eliminate anomalies and inconsistencies. This may include removing duplicates, filling in missing values, normalizing data formats, etc.
The goal is to ensure that the data is reliable, consistent, and ready for analysis.
Data transformation
Once the data is cleaned, it may require transformation to prepare it for analysis. This step may involve converting unstructured data into structured data, aggregating data, creating derived variables, etc.
Data transformation organizes the data in a way that facilitates further analysis.
We use efficient database management systems to ensure data integrity and availability when needed.
We prioritize data security and confidentiality. This includes implementing robust security measures to protect data against unauthorized access, loss, or leaks. This may involve data encryption, firewall usage, access rights management, etc.
Analysis and documentation
A plethora of tools are implemented in the data analysis process.
Our tools are specifically deployed based on the needs of each project and team preferences. Tool selection is based on reliability, flexibility, performance, and compatibility with existing technologies and infrastructures.
To ensure traceability and understanding of the data, we document metadata associated with the collected data. This includes information about the data source, collection period, extraction methods, applied transformations, etc.
This metadata is essential to ensure the quality and consistency of data used in subsequent analyses.
Presentation of results
Here are some commonly used methods to present results:
Interactive dashboards
Creation of interactive dashboards using data visualization tools such as Excel, Power BI, QlikView, or Python data visualization libraries like Matplotlib or Plotly. These dashboards allow clients to explore results, interact with charts, filter data, and discover relevant information autonomously.
Analytical reports
Generation of detailed analytical reports presenting the processing results in a structured manner. These reports may include statistical analyses, graphical visualizations, recommendations, and key insights derived from the data. Reports are typically presented as PDF documents or PowerPoint presentations.
Oral presentations
Organizing video meetings, teleconferences, or in-person presentations where data experts explain the processing results in detail. These presentations may include charts, slides, interactive demonstrations, and discussions to help the client understand and interpret the results thoroughly.
Graphical visualizations
Processing results are often presented in the form of graphical visualizations such as bar charts, line graphs, heatmaps, scatter plots, pie charts, etc. These visualizations help illustrate trends, patterns, and relationships among the data in a visually engaging manner.
Summaries and key points
Key results and main conclusions of the processing are summarized in a concise and easily understandable manner for the client. This may include information such as key performance indicators, significant changes, identified opportunities, or challenges encountered.
The presentation of results is tailored to the client's profile, specific needs, and knowledge in data analysis. We ensure to use clear, non-technical language while providing sufficient context and explanations for the client to make informed decisions based on the presented results.