Plotly Dash Enterprise Features

Based on 12 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Reports interface for standard and self-service reports is intuitive and easy to use.

Requires a minimal number of steps/clicks to answer business question.

Based on 19 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Offers a variety of attractive graph and chart formats.

Score cards visually track KPI's.

Based on 18 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Provides business users an interface to easily design, refine and collaborate on their dashboards

Based on 12 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Using formulas based on existing data elements, users can create and calculate new field values.

Based on 17 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Based on 15 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Users can drill down and explore data to discover new insights.

As reported in 11 Plotly Dash Enterprise reviews. Ability to search global data set to find and discover data.

Ability for users to share data and reports they have built within the BI tool and outside the tool through other collaboration platforms.

Tool automatically suggests data types, schemas and hierarchies.

Analyze current and historical trends to make predictions about future events.

Based on 14 Plotly Dash Enterprise reviews and verified by the G2 Product R&D team. Communicate complex information clearly and effectively through advanced graphical techniques.

Ability to handle large, complex, and/or siloed data sets.

Ability to (re)structure data in a manner that allows extracting insights fast and accurate.

Provides business users an interface to easily design and refine their dashboards and reports. (What You See Is What You Get)

Application Programming Interface - Specification for how the application communicates with other software. API's typically enable integration of data, logic, objects, etc with other software applications.

As reported in 11 Plotly Dash Enterprise reviews. Supports a variety of scripting environments

Mines data from databases and prepares data for analysis

Applies statistical algorithms to selected data

Based on 11 Plotly Dash Enterprise reviews. Analyzes both structured and unstructured data

Interacts with data to prepare it for visualizations and models 11 reviewers of Plotly Dash Enterprise have provided feedback on this feature.

Offers modeling capabilities

Creates data visualizations or graphs

As reported in 10 Plotly Dash Enterprise reviews. Generates reports of data performance

Unifies information on a singular platform

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

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

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

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

Offers image recognition services

Offers natural language processing services

Offers natural language generation services

Offers artificial neural networks for users

Offers natural language understanding services

Provides deep learning capabilities

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

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

Facilitates analysis of high-volume, real-time data.

Allows user to query data through query languages like SQL.

Aligns processing and distribution workflows on top of Apache Hadoop

Aligns processing and distribution workflows on top of Apache Spark

Integrates data from multiple external databases.

Facilitates the dissemination of collected big data throughout parallel computing clusters.

Processes data and represents interpretations in a variety of graphic formats.

Strings together specific functions and datasets to automate analytics iterations.

Isolates certain datasets and facilitates management of data access.

Allows big data tool to run and record data within external applications.

Use notebooks for tasks such as creating dashboards with predefined, scheduled queries and visualizations

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

Ability to connect the analytics platform with a wide range of connector options for common data sources, including popular enterprise applications.

Connects to enterprise data governance software, or provides integrated data governance features to avoid misuse of data

Using formulas based on existing data elements, users can create and calculate new field values

Business users have the ability to filter data in a report based on predefined or automodeled parameters.

Allows the user to combine data from multiple sources into a functioning dataset.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Provides the ability to generate images from a text prompt.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Enables non-technical users to build dashboards through intuitive, drag-and-drop interfaces

Enables automated report generation and scheduled delivery to stakeholders

Allows dashboards and analytics to be embedded into external apps with branding flexibility

Supports integration with major data sources like cloud data warehouses, SQL/NoSQL databases, and SaaS applications

Efficiently processes large datasets with minimal lag and ensures high performance under load

Maintains performance and uptime during high traffic from multiple users or teams

Supports semantic data layers, role-based access controls, and metadata governance

Integrates with Jupyter, Python, or R for custom analytics and modeling

Provides native tools for statistical analysis, forecasting, and trend prediction

Uses AI to generate textual summaries, key takeaways, and data stories from dashboards

Allows users to query data and build reports using conversational or plain language

Detects and notifies users about KPI anomalies or significant metric changes in real time

Recommends next questions, analyses, or exploration paths using autonomous AI agents

Learns from historical user interactions to improve and personalize query results over time

Tailors dashboard views and suggestions based on user roles, access levels, and past behavior

Supports AI-driven exploration via prompts or multi-turn conversations for iterative querying