Predictive Analytics
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
Seize opportunities using past and present knowledge to predict what might happen in the future
DAECO's analytical predictive model allows you to resolve these obstacles
Get hold of reliable and purposeful forecasts with DAECO Predictive analysis services to derive the best of your collected business data.
The 8 stages in our
Predictive Analytics Model
Identify/Formulate Problem
To get the expected results from predictive analytics modeling, it is essential to identify the business objectives/problems, the scope of work, expected outcomes, and data sets to be used in the project.
Data Preparation
Before the development of predictive analytic models, Analysts collect data from multiple data sources, clean the data, and consolidating the data for analysis. It’s combined and stored in data warehouses.
Data Exploration
Next, Analysts access the data and determine how they want to organize it and check how many cases are available in datasets, what variables are included, missing values of the variables and their possibilities to meet business objectives through the datasets.
Transform & Select
Relevant data is selected, retrieved, and mapped correctly from one format to another, usually from the size of a source system into cleansed, validated, and ready-to-use form. It, also known as ETL (Extract/Transform/Load) process.
Build Model
It’s time to choose a predictive analytics model that’s suitable for your task. Five common models are: classification model, clustering, forecast, outliers model and time series model.
Validate model
Usually, Data scientists often build multiple predictive analytics models and then select the best one based on their performance while building models.
Deploy model
The newly developed predictive model needs to be put into production so it can deliver results.
Evaluate/Monitor Results
The model is monitored to make sure it’s providing the expected results and revised as required. Typically, domain experts, business managers are involved in evaluating the process.
Use predictive analytics to get you started on the path to data informed strategy formulation and decision making
With DAECO's Predictive
Analytical solution you can:
Predict future outcomes, uncover risks & opportunities for your business
Use your data to power business growth
Transform data insights into distinctive competitive advantage
Discover the hidden opportunities in your data and create smarter strategies
Simulate and run different scenarios of sudden market changes
Forecast expected outcomes of business processes
Make more informed business decisions
Optimize processes, generate efficiency and increase performance in your operations
