The Path to Greater Insights
Agreement
- Sign in Agreement
Data Extraction
- Data Schemas
- Guidelines + Mapping
- Data Extraction Methods
- RobobAI template
- SQL queries
- Data may require
- Normalization
- Cleansing and Validation
- Agreggation
Platform Delivery
- RobobAI QA check
- Implement data into RobobAI platform
- Treasury dashboards
- Actionable Intelligence Report (AIR)
- Other custom reporting
Data Schemas
Quick start
Get Started with
3 data points
With just 3 points of your supplier data, we can provide AI-driven
dashboards and start pinpointing opportunities to release liquidity.
This quick start option caters to organizations struggling to collate a quality data set and need support building a business
case.
We recommend Data Schema 1, where possible, due to the instant benefit of an 8-page Actionable Intelligence Report and a faster
route to leveraging your date to drive value.
This quick start option caters to organizations struggling to collate a quality data set and need support building a business
case.
We recommend Data Schema 1, where possible, due to the instant benefit of an 8-page Actionable Intelligence Report and a faster
route to leveraging your date to drive value.
More data drives more insight
We support 3 main data schemas
Supplier level data
(14 fields of aggregated data)
Basic supplier data is used to enable basic Supplier Insights linked to Card payment Optimizations.
You can:
-
Visualize spend by current payment method and terms
-
Identify opportunities to transition spending from check or electronic payment to card.
Invoice and payment data
(38 fields of line level data)
Additional invoice header level data provides additional targeted insights associated with invoice count / size.
You can:
-
Optimize working capital benefits for buyers and suppliers e.g. highlight early and late payments
-
Refine card opportunity analysis with actual payment time date
Commodity data
(61 fields of line level data)
Additional invoice or PO line data enables classification/categorization of spend data for advanced card optimization, treasury insights, and more.
You can:-
Increase granularity of insights by classifying invoice line items by spend category
-
Rationalize suppliers and streamline spend based upon supplier payment terms
Data extraction removes the barrier to spend analytics. It automates the process of data consolidation from multiple sources. No integration is needed. It's a stand-alone, read-only, push process.
The Data Extraction Overview presentation includes more details such as:
A background to Spend data
Example Source systems
Extraction Process
Data Mapping
Data Extraction guidelines
A background to Spend data
Example Source systems
Extraction Process
Data Mapping
Data Extraction guidelines
Data extraction methods
Data extraction methods refer to the techniques used to collect and move data from different sources. There are several methods for extracting data, and the choice of method depends on the source of data. Here are some common data extraction methods
Standard Reports from ERP
Many modern ERPs provide APIs that allow you to programmatically access and extract data. These APIs might expose endpoints for retrieving spend data, which you can then integrate into your extraction process
BI / Reporting
Some ERP systems offer built-in reporting tools that allow you to generate reports specifically for spending data. You can design and
run reports that extract the necessary information
SQL
Write SQL queries to extract spend-
related data from the ERP databases.
Click below for specific queries:
Whitelisting
Some organizations restrict access to external websites. If you are experiencing some challenges accessing the Treasury Intelligence platform from your organization some
Internet access rules may need to be configured to allow access.
A process called 'Whitelisting' can be run, typically by a Network Operations team. Detailed information is provided in the presentation below.
Support