SERVICES

BIG DATA END TO END ANALYTICS

Do you realize that you are doing analysis every day?

FROM INSIGHTS TO ACTION

Descriptive and predictive analysis of products, customer, competition, promotions and channel data mix that can accelerate your business in real time.

Our End-to-End Analytics solution is designed to equip the client with supervised management models that use Artificial Intelligence genetic algorithms to build insight in priority dimensions for the business.

Our data model is interactive and evolves constantly, accommodating changes in new analytical behaviors



ANALYSIS IS KEY FOR GROWTH AND CREATING ACOMPETITIVE ADVANTAGE

Business users demands are changing, the management model should always come with speed and tailor-made analysis, data quality and governance with report generators.

How and what new data sources are being created that are necessary and relevant to you?

Is your analysis aligned with the maturity of your users' analysis?

Are you combining data and analysis to drive a new vision?

Are you building a data analysis platform that allows end-to-end continuous growth?

Is your offer optimized based on the data?

Do you use the generated Insights to accelerate the business and make decisions in real time?


WE USE 2 TYPES OF ANALYSIS:

DESCRIPTIVE

We use descriptive analysis for data analysis that helps to describe, show or summarize the data in a meaningful way so that the patterns come directly from the data.

PREDICTIVE

We use predictive analysis for data mining that is related to the prediction of future trends and probabilities.

The key element of our predictive analysis is our machine learning recommendation engine, we transform variables that can measure and predict future behavior.




ANALYTICS LIFECYCLE



1

BUSINESS UNDERSTANDING

  • Information collection about the problem
  • Define the goal of solving the problem
  • Definition of the expected production
  • Definition of the hypothesis
  • Definition of the analysis methodology
  • Measure the value of the business

2

DATA UNDERSTANDING

  • Define variables to support hypotheses
  • Cleaning and transformation of data
  • Create longitudinal data / trend data
  • Provide additional data if necessary
  • Build Smart analytical data

3

DATA MINING & MODELING

  • Definition of the objective variable
  • Data division for training and validation of the model
  • Definition of the analysis time frame for training and validation
  • Correlation analysis and selection of variables
  • Selection of the right data mining algorithm
  • Perform validation by measuring the accuracy, sensitivity and elevation of the model
  • Data mining and modeling is an iterative process

4

MODEL INTERPRETATION

  • Describe the importance of each variable
  • Visualize the general model by creating a decision tree, for example:
  • Define the business action based on the result of the model

5

MODEL OPERATIONALIZATION

  • Determine the model's scoring period
  • Integrate the result of the model with the execution system (campaign system, CRM, etc.)
  • Create operational processes that are timely, consistent and efficient

6

MODEL MONITORING

  • Create a monitoring process for model evaluation
  • Evaluate the model based on the actual result
  • Monitor and evaluate the business impact

Our data model is interactive and evolves constantly accommodating changes in new analytical behaviors.