Data Analytics Services

We provide our clients with end-to-end solutions, combining Data Mining, Analytics, and Artificial Intelligence, with the aim of guiding organizations towards an efficient use of their data that creates value and enables strategic decisions.

Our Data Analytics Unit delivers services in Data Science, Data Visualization, and Artificial Intelligence, supporting organizations in making better decisions, predicting business outcomes, and building a smarter company. We implement effective methodologies to analyze and model business events.

Big Data Analytics

“Big Data” refers to large volumes of data present within data lakes/databases – both relational and non relational – which represent an invaluable source of value for the entity that owns them, but unfortunately, they are difficult to analyze effectively. In fact, the term Big Data Analytics usually encompasses both the problem of information overload and the set of analytical tools used to manage this huge flow of data and transform it into a productive and usable source of information.

Although Big Data is defined in terms of size, the measure is the system’s ability to perform data analysis. For this reason, the perception of Big Data can vary depending on the perspective from which the problem is approached. For example, they can be seen as “sets of data whose sizes exceed the capacit of typical database software tools to capture, store, manage, and analyze,” or as “sets of data that are so large (from terabytes to exabytes) and complex (from sensor data to social media data) that they require advanced and unique storage, management, analysis, and visualization technologies.” These definitions show that when thinking about Big Data, it is more important to focus on how they are analyzed rather than how many terabytes of space they fill.

Other definitions focus more directly on the data itself. To be classified as Big Data, data must possess the three V’s: Volume, Variety, and Velocity. Big Data is not only large but also diverse; it is available in many formats and can be organized in a structured or unstructured manner. Velocity refers to the rate of generation over time.
One of the reasons why increasingly large data archives are created is that this allows data to be generated much more quickly, thus increasing the potential for analysis.

Since Big Data is not only large but also of various types and growing rapidly, many technologies and analytical techniques, founded on fundamental pillars, are needed to try to extract relevant information and build expertise and analytical capability in a short amount of time.

Data Analytics

Data Analytics: pillars

Data Analytics: techniques

  • Variable Correlation
  • Data mining
  • Cluster analysis 
  • Machine Learning 
  • Text analytics 
  • Crowdsourcing 
  • Data Classification
  • Network Analysis 
  • Predictive Regression Models
  • Time Series Analysis

Experienced consultants work closely with the Client to:

  • Understand specific needs;
  • Analyze the organization’s databases and data lakes;
  • Obtain varying degrees of simplified, accessible, and valuable information;
  • Develop customized data platforms with visual representations;
  • Create statistical and machine learning models capable of harnessing the potential within the data to predict fraud events or cyber attacks.


Data Governance

Optimization and streamlining of fundamental data management processes to facilitate the development of robust and accurate analytical and strategic solutions.

We support the Client in the four main areas of data governance:

Data Integration

This involves gathering data from various sources, refining, transforming, and consolidating it into a singular, structured point through business processes such as ETL (Extraction, Transformation, and Loading), Data Quality, data replication, and virtualization. This aims to provide the Client with a clearer and more comprehensive overview.

Data Architecture

This entails defining a secure and flexible data architecture that precisely describes what data is stored, where, how, and why, while also clearly outlining models, policies, and rules regarding data governance. This supports accurate and high-quality analysis.

Data Warehousing

Functions of data warehousing and design of extraction/loading processes into databases/data lakes, to ensure data 'cleansing' and quality prior to the analysis process, through appropriate construction and automation of transfers.

Data Management/Engineering

Activities of extraction, cleansing, transformation, storage, and maintenance of data so that they can be analyzed for informed business decision-making. We support the Client in defining a strategic plan to integrate, centralize, and protect data in order to:

  • Define a practical value-oriented data governance framework guided by business, to support data quality, security, modeling, and integration;
  • Make data and analyses easily accessible to a wide range of business users.

Data Visualization and Reporting

Implementation of tools for data visualization, using visual elements such as diagrams, charts, and maps, to handle large volumes of data. Data Visualization makes data more accessible and comprehensible, allowing for trend analysis, anomaly detection, model development, and decision-making. Customized reporting dashboards complete the process, converting data into user-friendly documents that allow the Client to effectively understand and explore the data in an intuitive manner. Data visualization offers numerous business advantages, including:


Data tells a story, embedded within its historical trends. Through visual elements, it's possible to make the data story legible.


Information is shared in an accessible and easily understandable manner for a variety of recipients.

Relationship Visualization

It's easier to identify relationships and patterns within a dataset when information is presented in a graph or chart.


More representable and readily accessible data means greater opportunities for exploration, collaboration, and making better decisions.

Data Analysis

Data analysis activities aimed at providing transparency and visibility, offering insights to support business decisions, to identify deficiencies, risks, areas of opportunity, and business growth. Properly analyzing data allows for leveraging their full potential and comparing them in order to:

  • Discover hidden trends;
  • Develop skills in identifying deterministic rules and underlying behavioral patterns in the analyses performed;
  • Find implicit correlations in the data;
  • Gather high-level information;
  • Provide a valid starting point for the development of advanced analytics, charts, and predictive models.

Advanced Analytics

Application of the most advanced technologies in the field of Machine Learning and AI to support the Client with information and business processes optimization, transforming data into knowledge and technology into intelligence. Our AI/ML modeling services are designed to help our clients leverage the opportunities offered by machine learning to solve modeling and classification problems based on a target event.

We build algorithms of Supervised/Unsupervised ML types that learn and make inferences or predictions on Big Data to improve the predictive capacity of the models they are built upon as more information becomes available.

The most common techniques we use are:

  1. Deep learning;
  2. Text mining;
  3. Pattern matching;
  4. Sentiment analysis;
  5. Cluster analysis;
  6. Neural networks;
  7. Linear regressions;
  8. Random forest;
  9. Gradient boosting.

Technology / Data Analytics

Technologies to transform data into complete and detailed information aimed at improving the business.

How can we help? Let us know!