With its comprehensive suite of advanced analytics capabilities, the SAP Predictive Analytics platform equips businesses with the tools they need to transform raw data into valuable knowledge, extract actionable insights, and anticipate future trends in their data.
A comprehensive data mining solution like SAP Predictive Analytics helps users improve accessibility and visibility for their most important company data and connect multiple different data sources in a single, intuitive user interface.
Keep reading to learn about SAP Predictive Analytics, its key features and components, and how it can help business users improve the statistical analysis of their most important data, as well as where our team of SAP consultants can come in to help.
What is SAP Predictive Analytics?
SAP Predictive Analytics is a data mining and statistical analysis solution designed to help build predictive models and analyze data more efficiently, making it easier for users, including data scientists, business analysts, and more, to discover hidden insights and predict future events for better outcomes in the long run.
Understanding the Two User Interfaces
With Predictive Analytics in SAP, businesses are given access to a comprehensive suite of advanced analytics tools and capabilities, enabling them to make informed decisions, predict future trends, and optimize their operations.
And, by combining the strength of Automated and Expert Analytics interfaces, this solution empowers users to navigate complex data landscapes and uncover patterns and correlations in the data, leading to enhanced efficiency, accuracy, and strategic decision-making.
1) Automated Analytics
This user friendly interface includes the following modules…
- Modeler: Helps users create data management models, including regression, classification, and clustering, and export them in different formats to easily apply them across production environments.
- Data Manager: Leverages semantic tools to facilitate better data preparation and mining.
- Social: Extracts data and uses structural relational data to improve data visualization, create frequent path analyses, and improve core prediction capabilities.
- Predictive Factory: Allows customers to automate predictive modeling and management functions like detecting model deviations and applying models to a new data set.
2) Expert Analytics
This interface provides capabilities that enable users to…
- Leverage data visualization tools, such as cluster charts and parallel coordinates to improve the accuracy and efficiency of your data analysis functions
- Perform data analysis, including time series forecasting, trend analysis, and segmentation analysis, for better visibility and accuracy across data sets
- Use predictive algorithms and in-memory data mining capabilities to handle large volumes of data in a more efficient and effective manner
Key Steps in the Predictive Analysis Process
- Define objectives: Before your team can build predictive models for your most important data, you must first outline the core objectives of project, including scope, budget, timeline, and the type of data and deliverables required.
- Collect data: This step requires users to gather all the necessary data, including current and historical data from multiple different sources, to build comprehensive data sets and promote better results throughout the rest of the process.
- Prepare data: Once you have collected the required data for the project, your team must complete data preparation functions, including cleaning, preparing, and integrating data among the data set. This step also includes removing outliers and identify missing information for better future outcomes.
- Build models: This step involves leveraging predictive modeling tools to build data models and test them to ensure proper functionality and accuracy and reduce the risk for error across data sets.
- Deploy models: Once the model has been constructed and tested, your team must deploy the model and put it into production, including accurate predictions and reports to automate decision-making processes across your SAP system.
- Refine models: After your data model is live in your production environment, it must be regularly monitored to review performance, refine processes, and ensure predictive models are functioning as needed and expected.
How Can We Help?
Whether you need help implementing the SAP Predictive Analytics solution for the first time, an extra hand discovering hidden insights and relationships between data sets, or additional support optimizing analysis functions across your SAP system, Surety Systems is here to help.
Our team of senior-level SAP consultants has the knowledge and skills needed to help you manage your predictive models and optimize your SAP Predictive Analytics functions.
Getting Started with Us
Interested in learning where our team of SAP consultants can fit in your organization?
Ready to improve predictive and statistical analysis, data mining, and data management functions with SAP Predictive Analysis tools, but don’t know where to begin?
Contact us today for more information about our SAP consulting services!