What we offer

Improve your decisions

WHAT DO WE DO?

We make decisions

Leadership is making decisions and being able to have an impact. In these moments of blurred lines, more than ever it is necessary that those decisions are adequate. To decide is to act on questions. At Decidata, we help you to find the central issue and we put powerful analytical tools in reach so that you always have the best choices.

Advanced analytics

The development of the internet of things, the emergence of big data, the eruption of intelligent sensors and the growth of the number of devices connected to the internet have contributed decisively to creating a new context in which organizations interact with increasing volumes of data. The advanced analytics allows you to transform this data into business intelligence and to generate a value proposition based on your structured and optimized exploitation

While the traditional analytical tools of business intelligence focus on examining historical data to describe performance of past operations, advanced analytics use sophisticated analysis techniques that allow the discovery of hidden patters, the definition of scenarios in real time and the prediction of future behaviors. Combined with a profound knowledge of the business operation, therefore, the advanced analytics provide the organizations with a competitive advantage and allow a continuous improvement process, focused on the definition of strategies and the optimal decision making.

Machine learning

Machine Learning is an area of study related with artificial intelligence and the statistics that are allowed, from a set of data, to create a study of predictive models that are being fed and improved with new information and developing a true learning.

With these techniques and through analysis and study of previous patterns, we are able to predict future behaviors and anticipate increasing the profits and improving competitiveness.

Some applications of machine learning are, in many cases, the predicative maintenance, predicting customer leakage, the reduction of energy expenditures, delinquency prediction, fraud protection, the advanced segmentation of clients or the optimization of processes.

HOW DO WE DO IT?

01

Find the question

We help our clients to translate a business problem to a data science problem. Through our own methodology we dissect the problem and narrow its reach to maximize the impact of our models.

02

Define the sources of information

Once the objective to achieve is defined, we identify the sources of information to incorporate in the model, and they can be internal or external to the company. In this sense, we work hand in hand with the client to select the origins of the data, as well as the variables to incorporate into the set.

03

Construction of the dataset.

From the information received we define the crossover variables and the logical union scheme; We homogenize the different sources and automate the creation of the Set.

Also, we build new synthetic variables necessary to provide the model with a more complete reality to be analyzed

04

Analysis of the information

As soon as the Data set is created, we define and generate the analytical schemes and the visualizations that allow the client, periodically, to understand what is happening to their organization.

05

Construction of the model

With a clear specialization in machine learning, we design different strategies and models that we make compete between them to select one that demonstrates a better performance. We utilize open or black box algorithms, prioritizing the knowledge or the prediction according to the case study.

06

Commissioning

Once the preliminary results are presented, we give the company the responsibility of implementing and monitoring the model.

07

Improvement process

with an algorithm already tested and validated, we define the mechanisms of measurement and improve the model. Our objective is to equip the client with the tools and competence to understand the model and to be capable, independently, to make the necessary adjustments to adapt the algorithm to changing situations.

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