Predictive Analytics
Take advantage of opportunities by using what you know from your past and present data to anticipate the future
If your predictive analytics capabilities are falling short, it's likely due to one of the following common obstacles:
Uncertainty about how future events will affect your business
Does not identify risks and opportunities for your business based on data
Difficulty associating the patterns that the data shows with your business questions
Your team doesn’t have enough skills relative to predictive analytics
Lack of anticipation of the expected results of business processes
Does not simulate and execute different scenarios of sudden market changes
DAECO predictive analytics allows you to solve these obstacles
Get reliable and actionable forecasts with DAECO predictive analytics services to get the best from the business data collected.
The 8 stages of our
predictive analytics mode
Identificar/formular el problema
To obtain the expected results from predictive analytics modeling, it is essential to identify the business objectives/problems, scope of work, expected results, and data sets that will be used in the project.
Data preparation
Before developing predictive models, analysts collect data from multiple data sources, clean it, and consolidate it for analysis. They are combined and stored in data warehouses.
Data exploration
Analysts access the data and determine how they want to organize it and check how many cases are available in the data sets, what variables are included, the missing values in the variables, and their chances of meeting business objectives through the data sets. .
Transform and select
Relevant data is selected, retrieved and correctly mapped from one format to another, typically from the size of a source system to a format that is cleaned, validated and ready for use. It is also known as an ETL (Extract/Transform/Load) process.
Build the model
It's time to choose a predictive analytics model that's right for your task. Five common models are: classification model, clustering, forecasting, outlier model, and time series model.
Model validation
Typically, data scientists create several predictive analytics models and then select the best one based on its performance during model creation.
Model deployment
The newly developed predictive model must be put into production so that it can deliver results.
Evaluate/monitor results
The model is monitored to ensure that it provides the expected results and is revised when necessary. Subject matter experts and company managers usually participate in the evaluation of the process.
Use predictive analytics to get you started on the path to data-driven strategy formulation and decision making
With DAECO's predictive analytics solution
- Estime future results, discover risks and opportunities for your company.
- Use your data to drive your business growth.
- Transform data insights into a distinctive competitive advantage.
- Discover the opportunities hidden in your data and create smarter strategies.
- Simulate and execute different scenarios of sudden market changes.
- Forecasts the expected results of business processes.
- Make more informed business decisions.
- Optimize processes, generate efficiency and increase performance in your operations.
Empower your business with enhanced analytics capabilities to unlock the true value of your business data and drive decision-makingnes.
