DataSelf Analytics for Acumatica
The following is a summary of the available BI, report, and dashboard templates. Please contact DataSelf to discuss how these templates and information could apply to your business and needs.
Data Warehouse Solution
AI-based Templates
Power BI Templates
Tableau Templates
Microsoft Excel Templates
These templates include Excel workbooks with pre-configured data feeds from your MS SQL Server data warehouse and/or ETL+ CSV exports. Most MS Excel workbooks will require some level of adjustment before they provide value. The data feeds will easily bring the latest data warehouse data into Excel, from where users can slice and dice their data and build new reports.
DataSelf Analytics Deployment
DFT+ Data Modeling
Data Models gather and transform raw data into fast and ready-to-be-reported information. They work as the single version of the truth defined in the DataSelf Server-side components (ETL+, data warehouse, Power BI models, and/or Tableau data sources). All reports, dashboards, and KPIs derive information from their data models. Changing the data model will automatically reflect the new rule across all reports and dashboards. For instance, a simple change to the Gross Profit data model calculation to absorb freight, and next time, reports and dashboards will automatically show GP with freight absorbed.
What’s more, our DFT+ modeling is source agnostic! Click here to learn more.
DFT+ Star Schema Example
The following exemplifies how the Sales Invoice DFT+ tables are linked in DataSelf’s out-of-the-box templates in a star schema arrangement. This model works in reporting tools such as Power BI, Tableau, and Excel.
DFT+ Galaxy Schema Example
The following exemplifies how DFT+ tables are linked in DataSelf’s out-of-the-box templates in a galaxy schema arrangement. This model works in reporting tools such as Power BI, Tableau, and Excel.
A key benefit of a galaxy schema is easy reporting across multiple fact tables in a single report. Click the image to zoom in.
DataSelf vs Other Reporting Options
DataSelf vs DIY Data Engineering Platforms
Data engineering and data warehouse/data lake platforms offer flexibility and scale, but can require technical teams, custom development, and ongoing maintenance.
DataSelf helps mid-market organizations accelerate such platforms and analytics with prebuilt connectors, automated governed modeling, KPI templates, and BI & AI-ready data structures.
Capabilities | Snowflake, MS Fabric, Databricks, Fivetran | DataSelf |
|---|---|---|
SMB & mid-market focused | Not typical | Yes |
Requires dedicated data engineers | Often | Often unnecessary |
ETL/ELT (extract, transform, load) | Large enterprise focus | ETL+ (SMB focus) |
Prebuilt ERP pipelines | Limited | |
Automated governed modeling | Custom initiative | DFT+ (Customizable) |
KPI templates | Minimal | KPI+ (industry-specific, best practice 8,000+ KPI library) |
Single version of the truth | Custom initiative | DFT+ (Customizable) |
Star & galaxy schemas | Custom initiative | DFT+ (Customizable) |
AI-assisted modeling & governance | Large enterprise focus | |
Fabric Power BI / Tableau / AI ready | Custom initiative | |
Time to deploy | Minutes to hours | |
Time to value | Months | |
Architecture options | Primarily cloud-centric | |
Business model | Platform (DIY) | |
Pricing model | Unpredictable |
Keywords: out-of-the-box templates, mappings, ETL+, data warehouse, data lake, DFT+, KPI+, AI+,ERP, CRM, reports, dashboards, KPIs, Claude, ChatGPT, MCP Server, Microsoft Fabric, One Lake, Velixo, Tangerine Software, ZAP, Phocas, Solver BI 360, Domo, Conversight.