Data & Business

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We carry out projects and services when the data is the protagonist


We help our clients make better decisions based on the information obtained from the data. We design and build analytical and BI systems. We implement infrastructure and technology to be able to tackle projects with large volumes of data.

We provide 360 solutions, to all the needs of an organization focused on the study and analysis of its data

We develop predictive solutions based on our clients’ historical data. We automate processes and create wizards to delegate certain tasks to automatic processes, helping to optimize and improve processes.


  • BI: Design & modelling
    We design scalable information models, identifying our client’s needs with a global vision of the business

    Conceptual, logical and physical design
    Data Warehouse, Data Marts, Data Lakes

  • BI: Data analysis & Integration
    Our service is based on an exhaustive knowledge of the business and the data, understanding in this way how the data passes through each layer and is transformed in its life cycle until it is presented and provides real value

    Bus matrix
    Data mapping
    Data traceability framework

  • BI: Display and reports
    We offer suitable and agile alternatives with an intuitive design and adapted to the needs of our clients

    Dashboard definition
    Dynamic reports
    Traditional reports

  • Big Data: Infrastructure design Data Driven
    We adapt the technological components to the needs of data exploitation. Implementation of on-premise, Cloud or hybrid infrastructure

  • Big Data: IoT & Industry 4.0
    We help implement the components needed to collect large volumes of data on an ongoing basis. We develop near-real-time analytics.

  • Big Data: Distributed programming
    We take advantage of the capacity of a Big Data environment to quickly process large volumes of data.

    MapReduce, HDFS, Cloudera

  • IA: Evaluate your data
    We tell you what the reality of your data is, how to improve its quality and capture procedures in order to prepare your business for the application of advanced analytical techniques.

    Evaluation and state of maturity of the data.

  • IA: Identify use cases
    We understand your business and we are able to identify use cases that can add value to your business. Understanding of the business and underlying data to provide more information.

  • IA: Building personalized solutions
    We apply the Artificial Intelligence techniques that best suit the problem to be solved and develop specific solutions that make your business evolve.

    Forecasting, recommendation and prospecting.

  • RPA: We improve processes
    After more than 25 years in the industry, we are able to implement efficient office processes, eliminating non-value-added tasks from the workflow.

    Continuous improvement, efficiency, increased productivity

  • RPA: We automate tasks
    We minimize repetitive and low-skilled work in people’s daily lives. We allow personal and professional growth. We dedicate people to high-level tasks.

  • RPA: We improve times, avoid mistakes
    We mitigate human error in highly repetitive processes by implementing assistants who perform these types of tasks. We take advantage of the processing speed in tasks to improve times and increase production.


We bets on CRISP-DM: Cross-Industry Standard Process for Data Mining, as the methodology to design, develop and govern the projects in which data is the protagonist.

This methodology is based on the data cycle and on a knowledge of the business that allows us to offer results in accordance with the needs of our clients. It is not only a methodology to be applied only by the Project Managers: the whole team works in a coordinated way, knowing the importance of each of the phases of the process.

Phase design

  • 1

    Business compression

    This initial stage focuses on understanding the objectives and requirements of the project from a business or functional perspective and then converting this knowledge into a definition of the problem and a preliminary plan designed to achieve the objectives.

  • 2

    Data compression

    The data understanding stage begins with initial data collection and continues with activities to become familiar with the data, identify data quality problems, uncover early ideas about the data, or detect interesting subsets to form hypotheses about hidden information.

  • 3

    Data preparation

    The data preparation stage covers all activities to build the final data set (data that will be fed to the modelling tool from the initial raw data. The data preparation tasks will probably have to be performed several times and not in the prescribed order. Such tasks include: the selection of tables, records and attributes, as well as the transformation and cleaning of data for the modelling tools. In the case of information systems definition, the methodology is oriented to the transformation of operational data into Dimension and Fact tables, being fully compatible with the most extended approaches in the realization of BI projects such as Inmon and Kimball approaches.

  • 4


    In this stage, several modelling techniques and algorithms are selected and applied, and their parameters are calibrated to obtain an optimal value. Usually, there are several techniques for the same type of problem. Some techniques have specific requirements on the form of the data, its distribution, nature and even typology. Therefore, going back to the data preparation phase is often necessary.

  • 5


    At this stage of the project we already have a model created (or models) that appears to be of high quality, from a data analysis perspective. Before proceeding to the final deployment of the model, it is important to further evaluate the model and review the steps executed to build it, to ensure that we achieve the business objectives set. A key objective is to determine if there are any major business issues that have not been sufficiently addressed. At the end of this phase, a decision must be made as to the result obtained and whether it is the expected result that meets the specification, or the need initially stated.

  • 6


    The creation of the model is usually not the end of the project. Even if the purpose of the model is to increase awareness of the data, the knowledge gained should be organized and presented in a way that the user can use. Depending on the requirements, the implementation phase can be as simple as generating a complex report or implementing a repeatable data mining or even machine learning process. In many cases it will be the user, not the data analyst, who will carry out the implementation steps. However, even if the analyst does not carry out the implementation effort, it is important for the end user to understand what actions must be taken in order to make use of the models created.

Would you like to grow your business? Let us know. We are willing to help you.