Data Science focused
on results: Data Agility

We believe that Data is the most valuable asset a company can have.

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Icone Data Agility

Data Agility

Data Agility is our Data Science and Machine Learning service for more agile and prioritized business decisions, with risk and cost reduction​

We bring to Data Science projects the same agile and lean approach as digital product development projects: short delivery cycles, prioritization by value, and focus on the business process (and not on tools). ​

Icone Data Agility

Data Analytics

Data Analytics Sprint is a data initiatives' accelerator. Our data science team is able to identify, validate, and speed up business opportunities that can be leveraged through data analysis, in a short time and with low investment. From 2 to 4 weeks, we structure your primary needs and create an evolution plan based on Data-Driven methodologies.

Data Sprint

Short delivery cycles, prioritization by value and focus on the business process, not the tools. The sprints deliverables will be the business indicators and software artifacts that generate significant gains for the customer. The learning generated during the process will be used in the following project cycles. The result is always different and better than the initial plan.

Infographic with the following information: Step 1, business understanding​ is the moment to understand the problem to be solved, which allows determining which data will be used to solve the issue.​ Step 2 is data understanding​, which is the phase of the process where the available data is analyzed to understand the potential of it solving the problem at hand.​ Step 3 is data preparation, the phase where the data is normalized so the exploratory analyses and predictive models creation can begin. Step 4, modelling, the phase that the results begin to appear. The moment to extract knowledge from the exploratory analyzes and improve the predictive models created earlier to automate parts of the business. Step 5 is evaluation, the goal of this phase is to validate (evaluate) the results and ensure that following the outlined path will bring the expected business results. The final step, deployment, is the phase where the created solution is deployed in the production environments.

1

Business Understanding

The moment to understand the problem to be solved, which allows determining which data will be used to solve the issue.

2

Data Understanding

The phase of the process where the available data is analyzed to understand the potential of it solving the problem at hand.

3

Data Preparation

The Phase where the data is normalized so the exploratory analyses and predictive models creation can begin.

4

Modelling

The phase that the results begin to appear. The moment to extract knowledge from the exploratory analyzes and improve the predictive models created earlier to automate parts of the business.

5

Evaluation

The goal of this phase is to validate (evaluate) the results and ensure that following the outlined path will bring the expected business results.

6

Deployment

The phase where the created solution is deployed in the production environments.