Ecoservity · Independent Benchmark · February 2024
Three options. One real project. An Asia-based specialty apparel manufacturer needed SAP TCODEs, reports, and tables live in Power BI and Excel. Here is what the numbers looked like across CDATA, SAP manual extraction, and People@Work.
CDATA and SAP SE16N extract the database tables underneath an SAP object. People@Work delivers the SAP object itself: the named, business-ready result that your analyst actually needs. That distinction is where the entire cost gap lives.
CDATA extracts the underlying database tables. SE16N downloads them to Excel manually. In both cases, the analyst receives raw relational data with SAP table names (MKPF, MSEG, MARA...) and must reconstruct the business logic that SAP already computed.
People@Work streams the SAP TCODE output directly into Power BI or Excel, complete with report variant, business column names, and live data. No joins. No reconstruction. No ABAP knowledge required. The same result your SAP user sees on screen, now live in your analytics tool.
The customer evaluated all three. Each can technically move SAP data into Power BI. Only one delivers it in a form that is immediately usable without downstream reconstruction work.
CDATA's SAP connector for Excel and Power BI extracts underlying database tables. Raw table data arrives in Power BI and must be transformed and joined to build reports.
SE16N for tables and SE38 for programs allow SAP users to export data to Excel directly. Each export is manual, static, and must be refreshed by logging into SAP and re-downloading.
People@Work connects Power BI and Excel to live SAP data via a certified REST API. TCODEs, reports, tables, and views stream directly: named and structured in business language.
An Asia-based specialty apparel manufacturer requested a cost and time comparison across all three options. Five real SAP objects were used, covering the mix of TCODEs, reports, tables, and views that represent a typical analytics project.
The cost comparison runs across every project phase for all three options. The Build phase again is decisive: $2.44M for both CDATA and SAP manual, versus $1,760 for People@Work, because People@Work does not need to reconstruct what SAP already computed.
| Phase | CDATA connector | SAP SE16N / SE38 | People@Work | $ saved | % saved |
|---|---|---|---|---|---|
| 1 · Plan | $4,800 | $4,800 | $1,600 | $3,200 | 67% |
| 2 · Setup | $4,160 | $8,800 | $1,440 | $7,360 | 84% |
| 3 · Build | $2,435,920 | $2,435,920 | $1,760 | $2,434,160 | 100% |
| 4 · Go-live | $16,416 | $16,416 | $6,400 | $10,016 | 61% |
| 5 · Operations | $193,205 | $181,650 | $134,076 | $47,574 | 26% |
| Total | $2,654,501 | $2,647,586 | $145,276 | $2,502,310 | 95% |
| Phase | CDATA | SAP manual | People@Work | Wks saved | % |
|---|---|---|---|---|---|
| 1 · Plan | 4 wks | 4 wks | 2 wks | 2 wks | 50% |
| 2 · Setup | 6 wks | 9 wks | 2 wks | 7 wks | 80% |
| 3 · Build | 7 wks | 7 wks | 2 wks | 5 wks | 73% |
| 4 · Go-live | 4 wks | 4 wks | 4 wks | 0 | 0% |
| Total | 21 wks | 24 wks | 10 wks | 14 wks | 59% |
Both CDATA and SAP SE16N/SE38 extract raw database tables. Neither can deliver MB51 or RM06EL00 as a complete business object. Both therefore require the same Build effort: engineering time to reconstruct the SAP business logic from underlying tables in Power BI. The extraction method changes. The reconstruction cost does not.
SAP SE38 manual setup (Phase 2) runs 9 weeks versus CDATA's 6, because each report download process must be documented per user, per transaction, per output format. People@Work's setup is 2 weeks regardless of how many users or how complex the SAP objects, because the connector handles the SAP authentication and object discovery automatically.
The cost gap is a capability gap. CDATA and SE16N give you data. People@Work gives you intelligence: the same intelligence SAP has already computed, delivered live into the tools your team already uses.
MB51 accessed live in Power BI shows Material Document List output (Vendor, Material, Quantity, Plant) exactly as a SAP user sees it. No joins. No column mapping. No ABAP knowledge required in Power BI.
RM06EL00 (Purchase Orders list) streams its full output, including calculated fields and business logic, directly into Excel via PowerQuery. No recreation of SAP calculation logic required downstream.
Selection criteria, column layouts, and filter variants from SAP are passed directly to People@Work. The Power BI report reflects the exact SAP variant your business defined, not a generic raw table dump.
Every People@Work query runs under the user's own SAP authorization. If a user cannot see a plant's data in SAP, they cannot see it in Power BI. No custom row-level security to build, maintain, or audit.
Power BI reports and Excel workbooks refresh directly from SAP on demand. No SE16N download. No file save. No email. The same data the SAP user sees in real time, available to the analyst without any SAP login.
LFA1, T001W, and V_TNAPR stream with field-level delta. Power BI receives only the rows that changed, not a full table reload on every refresh. One connector for all SAP object types.
People@Work is certified by both SAP and Microsoft. It installs as a native connector inside Power BI Desktop, as a certified data source in Microsoft Fabric, and as a PowerQuery add-in inside Excel. No new infrastructure. No IT project to configure access.
People@Work appears in Power BI Desktop's Get Data menu. SAP server name, object type, object name, and variant are entered as parameters. Live data loads directly into the data model. No staging file, no scheduled export.
Full and delta SAP data flows into OneLake Fabric Lakehouses and Warehouses via People@Work. Microsoft-recommended architecture for SAP live data in Fabric. Co-published on Azure Marketplace.
SAP TCODEs, reports, and tables connect into Excel via PowerQuery. Refresh refreshes directly from SAP. No SAPGUI required. Business users get live SAP intelligence in the spreadsheet tool they already know.
The numbers above are from five specific SAP objects. The savings scale with complexity: the more TCODEs and ABAP reports in your analytics scope, the larger the Build cost gap becomes. Talk to us about your specific objects.