Databoards are the intuitive interface where end users can interact with their data via DataGPTʼs Chat AI Analyst), Data Navigator, and Dynamic Benchmarking modes

See appendix for schema definition

Admins can view and edit the schema configuration including adding new metrics and dimensions and adding additional context for the LLM specific to your business and use case.

Add a Databoard into your own internal environments for end user interaction via an iframe.

Admins can add or remove users from their Account, modify the schemas and access all databoards within the Account.

DataGPT Premium and Enterprise plans provide logical multi-tenant Teams. DataGPT databoards can be partitioned into multiple tenant-specific environments called Teams. With Teams, each tenantʼs data is isolated and protected with access control, and is invisible to the other tenants that share the same DataGPT account

An intuitive interface that gives you visibility into your team's activity - sessions, prompts, and more - so you can monitor usage and provide effective support.

This means you use the exact metric, dimension or segment name that is used in the schema, which the AI analyst surfaces via the autofill feature, to ensure clear alignment with the AI

Apply a filter providing the Analyst direction on what subset of the data you want to ask questions about. Bolean enables you to filter in by any segment(s), or filter out by any segment(s).

For example, you could filter the data by Segment, 'Country: USAʼ, and the AI Analyst will recognize as you ask new questions that you are asking only in the context of the US data.

Each day the AI Analyst will provide a daily summary within the Chat Mode interface highlighting the key metric performance for up to 10 selected metrics.

Each day the AI Analyst will provide a daily summary within the Chat Mode interface either highlighting the key drivers of up to 5 key metrics or highlighting relevant data anomalies.

A section in each response that lets you access the underlying data used to generate it - especially helpful for responses with many segments - offering pagination, sorting, and CSV upload for easier navigation an analysis.

The AI Analyst will automatically send email notifications to users of a Databoard, providing analysis based on pre-configured prompts

The Al analyst will seamlessly process the assigned task along with the sequence of analytical functions in a single pass, delivering a clear and concise report.

Asks what metrics or dimensions have been configured in the schema. Ask how the configured metrics are defined and calculated in the schema.

Get the basic value of your metrics. Example: "What is our total sales today?"

Ask about how metrics perform across different segments or combinations of segments.

Ex: "How is our Revenue from VIP members in the UK performing across product categories?"

Expand your analysis possibilities by building upon other defined metrics in your databoard.

Ex: Calculate my conversion rate (won opportunities / total opportunities)

Ask about patterns in your data over time.

Ex: "Whatʼs the trend in Page Views over the last 30 days?"

Compare metrics from one time period to another.

Ex: "What was our On Time Delivery Rate this Month vs Last Month?"

Understand relationships between different metrics.

Ex: "Does an increase in marketing spend lead to higher sales?"

Understand the impact of specific segments on a metric.

Ex: "Which product category has the biggest impact on our total sales?"

Identify key factors affecting your metrics and suggest actions. Ex: "What are the main drivers of our customer churn?"

Get extensive statistical information about the metric time series: variance standard deviation, percentiles, min/max values and time series rolling average for a given period "What are the standard deviation and average values of our daily sales?

Questions about segments can be displayed as Line chart, Bar Chart, Stacked chart. Questions about metrics can be displayed as Times Series.

Compare the performance of any two segments side by side regardless of start date.

Ex: How did Marketing Campaign 1 that started on January 1, compare to Marketing Campaign 2 that started on February 18th over a 30 day period? Or Why did House of Dragons Season 1 have more Viewership in the first 30 days than Season 2?

Configure your AI analyst to monitor for important changes in your metrics at any level of drilldowns in your data. These could be based on thresholds, relative change, and top/bottom segments. Ex: "Are there any anomalies in our Revenue this month?"

Adjust your comparison period (CP) and observation period (OP) and your data navigator will update all calculations in real-time.

Everyday Data Navigator displays our Metricʼs performance including current value, change in value, and % change

Key Drivers Analysis is automatically triggered daily and displayed in the 'Key driverʼ tab for within each Metric card

Generate reports with a simple click by selecting a metric and dimensions or segments. For example select the metric 'Revenueʼ and Dimension 'Product Categoryʼ to see the revenue generated across each product category

Sort Reports by % Change, Value, Change in Value, or Impact

Drill Down into any combination of segments for instant data exploration and multi-dimensional reports.

Data Navigator includes several automatically generated visuals. Time series visuals are included for each metric's performance. Line, bar, and stacked charts are automatically generated for each segment.

Use the Boolean Filter on the Data Navigator to filter the entire navigator by any Segment(s) to focus on what matters most to you. Bolean enables you to filter in by any segments), or filter out by any segments)

Copy and Share a hotlink to any location in the Data Navigator with another user

Construct your Data Model in the DataGPT Schema. An easy way to think about schemas is when you have distinct use cases. Each use case will often signal a unique schema.
*See appendix for full schema definition

DataGPT will access only the data you make available, either by providing access to a single View or providing access to multiple tables that you want to make available to DataGPT.

Add you metric definition and and metric calculations in the Schema for the Al analyst to use in its analysis.

Add the dimensions within the view or table that are relevant to the analysis.

Add any synonyms that are especially unique to how your business talks about their data, or add clarity for the LLM by adding labels to metrics or segments with similar sounding names to help it understand when to use each respective datapoint.

Add Instructions for the LLM to follow when you have specific steps you want the Analyst to take when answering particular questions that might be unique to your business

The Customer Data Storage Layer is the only layer that contains storage of customer data. This is stored in an encrypted and shared secure S3 bucket in AWS US-East-2

The Customer Data Storage Layer is the only layer that contains storage of customer data. This is stored in an encrypted and shared secure S3 bucket in AWS US-East-2, but can be located in a specific AWS Region upon request

The Customer Data Storage Layer is the only layer that contains storage of customer data. This is stored in an encrypted and secure S3 bucket and EFS which can be located in a customer specific VPC in AWS us-east-2 region or other regions. If you require Regions outside the US East -2 region it may impact your pricing.

Work Hours including M-F 9am ET to 6pm ET

*additional onboardings can be included for an add-on fee of $3,000 per new schema configuration

The User training sessions are provided once during the onboarding process and are not recurring annually.

Direct access to senior support staff for more complex issues and advanced troubleshooting