Why Data Prep Is Key for AI
Garbage In, Garbage Out: Ensuring High-Quality Data for Your AI ModelsArtificial Intelligence and Machine Learning models are only as good as the data they are trained on. In the realm of GRC and MSP, this means that accurate risk predictions, effective compliance automation, and insightful operational analytics depend heavily on well-prepared data. Raw data from sources like Azure Advisor, security logs, and GRC tools is often noisy, inconsistent, and incomplete.
Discuss Your AI Data NeedsComprehensive Data Preparation for Peak AI Performance
Ensuring Accuracy and Consistency
Our data cleansing processes identify and correct errors, inconsistencies, and missing values in your datasets. We validate data against predefined rules and external sources to ensure its accuracy and reliability, forming a clean base for AI model training.
- Handling missing data through imputation or removal.
- Identifying and correcting outliers and erroneous entries.
- Standardizing formats and resolving inconsistencies.
- Validating data integrity for GRC and security use cases.
Structuring Data for Optimal AI Input
Raw data often needs to be transformed and normalized to be suitable for AI algorithms. We convert data into appropriate formats, scale numerical values, and encode categorical variables to create a consistent and optimized dataset for machine learning models.
- Normalization and standardization of numerical features.
- One-hot encoding and label encoding for categorical data.
- Aggregation and disaggregation of data as needed.
- Creating structured datasets from unstructured text or logs.
Extracting Meaningful Signals for AI
Feature engineering is crucial for enhancing the predictive power of AI models. Our data scientists create new, relevant features from existing data and select the most impactful features to improve model accuracy and interpretability for GRC risk prediction and MSP optimization.
- Creating interaction terms and polynomial features.
- Dimensionality reduction techniques (e.g., PCA).
- Selecting the most relevant features for specific AI tasks.
- Time-series feature creation for trend analysis.
