[
SAP Bus... Reviews
](https://www.g2.com/products/sap-business-data-cloud/reviews)

[
SAP Bus... Reviews
](https://www.g2.com/products/sap-business-data-cloud/reviews)

# SAP Business Data Cloud Features

##### 
## Model Development (5)

Language Support

Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript

Drag and Drop

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Pre-Built Algorithms

Provides users with pre-built algorithms for simpler model development

Model Training

Supplies large data sets for training individual models

Feature Engineering

Transforms raw data into features that better represent the underlying problem to the predictive models

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##### 
## Machine/Deep Learning Services (6)

Computer Vision

Offers image recognition services

Natural Language Processing

Offers natural language processing services

Natural Language Generation

Offers natural language generation services

Artificial Neural Networks

Offers artificial neural networks for users

Natural Language Understanding

Offers natural language understanding services

Deep Learning

Provides deep learning capabilities

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##### 
## Deployment (15)

Managed Service

Manages the intelligent application for the user, reducing the need of infrastructure

Application

Allows users to insert machine learning into operating applications

Scalability

Provides easily scaled machine learning applications and infrastructure

Language Flexibility

Allows users to input models built in a variety of languages.

Framework Flexibility

Allows users to choose the framework or workbench of their preference.

Versioning

Records versioning as models are iterated upon.

Ease of Deployment

Provides a way to quickly and efficiently deploy machine learning models.

Scalability

Offers a way to scale the use of machine learning models across an enterprise.

On-Premise

Provides On-Premise deployment options.

Cloud

Provides Cloud deployment options (private or public cloud, hybrid cloud).

Language Flexibility

Allows users to input models built in a variety of languages.

Framework Flexibility

Allows users to choose the framework or workbench of their preference.

Versioning

Records versioning as models are iterated upon.

Ease of Deployment

Provides a way to quickly and efficiently deploy machine learning models.

Scalability

Offers a way to scale the use of machine learning models across an enterprise.

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##### 
## Administration (4)

Data Modelling

Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Recommendations

Analyzes data to find and recommend the highest value customer segmentations.

Workflow Management

Tools to create and adjust workflows to ensure consistency.

Dashboards and Visualizations

Presents information and analytics in a digestible, intuitive, and visually appealing way.

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##### 
## Compliance (4)

Sensitive Data Compliance

Supports compliance with PII, GDPR, HIPPA, PCI, and other regulatory standards.

Training and Guidelines

Provides guidelines or training related to sensitive data compliance requirements,

Policy Enforcement

Allows administrators to set policies for security and data governance

Compliance Monitoring

Monitors data quality and send alerts based on violations or misuse

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##### 
## Data Quality (3)

Data Preparation

Curates collected data for big data analytics solutions to analyze, manipulate, and model

Data Distribution

Facilitates the disseminating of collected big data throughout parallel computing clusters

Data Unification

Compile data from across all systems so that users can view relevant information easily.

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##### 
## Management (16)

Cataloging

Records and organizes all machine learning models that have been deployed across the business.

Monitoring

Tracks the performance and accuracy of machine learning models.

Governing

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Model Registry

Allows users to manage model artifacts and tracks which models are deployed in production.

Cloud Cost Analytics

Provide cost management insights and analytics regarding cloud usage.

Cloud Security

Manage vulnerability, access, and protection for multiple clouds.

Cloud Resource Management

Manually or automatically manage cloud resource usage and scaling.

Cloud Backup and Recovery

Back up cloud resources, and offer recovery capabilities for outages and crashes.

Business Glossary

Lets users build a glossary of business terms, vocabulary and definitions across multiple tools.

Data Discovery

Provides a built-in integrated data catalog that allows users to easily locate data across multiple sources.

Data Profililng

Monitors and cleanses data with the help of business rules and analytical algorithms.

Reporting and Visualization

Visualize data flows and lineage that demonstrates compliance with reports and dashboards through a single console.

Data Lineage

Provides an automated data lineage functionality which provides visibility over the entire data movement journey from data origination to destination.

Cataloging

Records and organizes all machine learning models that have been deployed across the business.

Monitoring

Tracks the performance and accuracy of machine learning models.

Governing

Provisions users based on authorization to both deploy and iterate upon machine learning models.

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##### 
## Functionality (3)

Cloud Consolidation

Consolidate viewing and management of multiple clouds in one solution.

Cloud Orchestration

Provide cloud orchestration for setup, configuration, and management.

Cloud Optimization

Optimize cloud performance and integration with disparate systems.

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##### 
## System (1)

Data Ingestion & Wrangling

Gives user ability to import a variety of data sources for immediate use

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##### 
## Data Management (6)

Data Integration

Consolidates, Cleanses and Normalizes data from multiple disparate sources.

Data Compression

Helps save storage capacity and improves query performance.

Data Quality

Eliminates data inconsistency and duplications ensuring data integrity.

Built-In Data Analytics

SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.

In-Database Machine Learning

Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.

Data Lake Analytics

Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS

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##### 
## Integration (3)

AI/ ML Integration

Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.

BI Tool Integration

Integrates with BI Tools to transform data into Actionable Insights.

Data lake Integration

Provides speed in data processing and capturing unstructured, semi-structured and streaming data.

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##### 
## Performance (1)

Scalability

Manages huge volumes of data, upscale or downscale as per demand.

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##### 
## Security (5)

Data Governance

Policies, procedures and standards to manage and access data.

Data Security

Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion

Access Control

Authenticates and authorizes individuals to access the data they are allowed to see and use.

Roles Management

Helps identify and manage the roles of owners and stewards of data.

Compliance Management

Helps adhere to data privacy regulations and norms.

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##### 
## Maintainence (2)

Data Quality Management

Defines, validates, and monitors business rules to safeguard master data readiness.

Policy Management

Allows users to create and review data policies to make them consistent across the organization.

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##### 
## Operations (3)

Metrics

Control model usage and performance in production

Infrastructure management

Deploy mission-critical ML applications where and when you need them

Collaboration

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.

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##### 
## Generative AI (9)

AI Text Generation

Allows users to generate text based on a text prompt.

AI Text Summarization

Condenses long documents or text into a brief summary.

AI Text Generation

Allows users to generate text based on a text prompt.

AI Text Summarization

Condenses long documents or text into a brief summary.

AI Text Generation

Allows users to generate text based on a text prompt.

AI Text Summarization

Condenses long documents or text into a brief summary.

AI Text-to-Image

Provides the ability to generate images from a text prompt.

AI Text Generation

Allows users to generate text based on a text prompt.

AI Text Summarization

Condenses long documents or text into a brief summary.

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##### 
## Agentic AI - Data Science and Machine Learning Platforms (7)

Autonomous Task Execution

Capability to perform complex tasks without constant human input

Multi-step Planning

Ability to break down and plan multi-step processes

Cross-system Integration

Works across multiple software systems or databases

Adaptive Learning

Improves performance based on feedback and experience

Natural Language Interaction

Engages in human-like conversation for task delegation

Proactive Assistance

Anticipates needs and offers suggestions without prompting

Decision Making

Makes informed choices based on available data and objectives

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##### Categories on G2

[
Data Science and Machine Learning Platforms
](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[
Data Governance
](https://www.g2.com/categories/data-governance-tools)[
MLOps Platforms
](https://www.g2.com/categories/mlops-platforms)

[
Data Warehouse
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Cloud Management Platforms
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