In the 2025 BARC results, AnalyticsCreator shows the same KPI scores across both Data Product Engineering and Data Warehouse Automation peer groups, while rankings differ by category. The result supports the 2024 pattern: AnalyticsCreator’s strengths in business value, connectivity, support and operational delivery are not limited to one peer-group view.
Part 3 of 5 in our series on six years of BARC KPI data for AnalyticsCreator.
2025 is the first year AnalyticsCreator is scored in two BARC peer groups at once: Data Product Engineering and Data Warehouse Automation. That makes the year a useful stress test.
Data Warehouse Automation is the traditional category AnalyticsCreator has been evaluated against since 2019, as covered in Part 2 of this series. Data Product Engineering is the newer and broader category. If the gains from the 2024 Data Management Survey were a one-year artefact — a favourable peer group, a small sample or a good year — the 2025 two-peer-group view should expose it.
Instead, the scores tell a consistent story. AnalyticsCreator performs strongly across both views, with the clearest differences appearing in ranking position rather than absolute KPI score.
The 2025 comparison uses BARC’s published rank and score within each peer group. In the table below, each KPI is shown as rank / score.
The score shows the underlying KPI value. The rank shows how AnalyticsCreator placed against other products in that peer group. This distinction matters because the same score can result in a different rank depending on the competitive field.
| KPI | 2025 Data Product Engineering | 2025 Data Warehouse Automation |
|---|---|---|
| Business Value | 1 / 9.4 | 1 / 9.4 |
| Customer Satisfaction | 1 / 9.0 | 1 / 9.0 |
| Implementer Support | 1 / 10.0 | 1 / 10.0 |
| Sales Experience | 1 / 10.0 | 1 / 10.0 |
| Connectivity | 1 / 10.0 | 1 / 10.0 |
| Business Benefits | 2 / 9.6 | 2 / 9.6 |
| Project Length | 2 / 9.2 | 2 / 9.2 |
| Project Success | 2 / 9.4 | 2 / 9.4 |
| Vendor / User Support | 2 / 9.2 | 2 / 9.2 |
| Time to Market | 2 / 9.3 | 2 / 9.3 |
| Deployment and Operations | 2 / 9.6 | 1 / 9.6 |
| User Experience | 2 / 8.6 | 1 / 8.6 |
| Technical Foundation | 3 / 7.7 | 1 / 7.7 |
| Platform Reliability | 3 / 7.9 | 1 / 7.9 |
| Competitive Win Rate | 10 / 4.3 | 5 / 4.3 |
| Competitiveness | 12 / 3.7 | 5 / 3.7 |
| Considered for Purchase | 11 / 2.0 | 3 / 2.0 |
Key takeaway: In the supplied 2025 BARC KPI data, AnalyticsCreator’s scores remain identical across both peer groups, while its rankings improve in several technical and operational KPIs within the Data Warehouse Automation peer group.
The most important pattern in the 2025 data is that the absolute KPI scores are identical across both peer groups. Business Value is 9.4 in both. Connectivity is 10.0 in both. Customer Satisfaction is 9.0 in both.
What changes is the rank. Deployment and Operations, User Experience, Technical Foundation and Platform Reliability all place first in the Data Warehouse Automation peer group at the same score that reaches second or third place in the broader Data Product Engineering group.
Read plainly, AnalyticsCreator’s technical and operational strengths are more distinctive within its core Data Warehouse Automation category than in the broader Data Product Engineering category. That is exactly what you would expect from a specialist data warehouse automation application that is still building recognition in a wider field.
This distinction matters for buyers. A lower rank in a broader category does not necessarily mean weaker user feedback. It may mean that the comparison group is wider, the category is less specialised, or the competitive set contains products with different strengths.
The 2025 comparison suggests that the 2024 gains were not a one-year artefact. Business Value, Customer Satisfaction, Connectivity, Implementer Support and Sales Experience all hold first-place rankings in both peer groups.
That consistency is important because the 2024 results shifted the AnalyticsCreator story from pure automation efficiency toward business value and operational maturity. The 2025 results show that this broader pattern remains visible when the category frame changes.
For decision-makers, this reduces one common concern about analyst survey results: whether a strong result depends too much on one peer group. In 2025, the same underlying score pattern appears in both the traditional Data Warehouse Automation view and the broader Data Product Engineering view.
AnalyticsCreator reaches 10.0 out of 10 for Implementer Support, Sales Experience and Connectivity in both 2025 peer groups. These are not simply strong scores. They are ceiling scores.
Implementer Support is especially important for partners and consulting teams delivering complex customer implementations. This KPI is not about whether a product has a useful feature on paper. It reflects whether users and implementers felt supported when delivery risk was real.
Connectivity is also central to the AnalyticsCreator value case. Data warehouse automation only works in practice when source systems, target environments and downstream analytics tools can be connected in a maintainable way. AnalyticsCreator’s metadata-driven approach is designed to support this by extracting metadata from source systems and using it to generate reusable artifacts for analytics delivery.
For a broader product overview, the AnalyticsCreator resources page includes reports, presentations and solution materials. Technical readers can also review Understanding AnalyticsCreator for more detail on the metadata-driven workflow.
Key takeaway: Perfect scores for Implementer Support, Sales Experience and Connectivity suggest that AnalyticsCreator’s 2025 strengths are not limited to automation features; they also include delivery support and practical integration confidence.
Business Value is ranked first with a score of 9.4 in both 2025 peer groups. That is one of the clearest links between the 2024 inflection point and the 2025 two-peer-group comparison.
In Part 2, the 2024 results marked the first major shift from engineering-efficiency rankings toward business-value recognition. The 2025 data supports that shift. Business Value does not disappear when AnalyticsCreator is viewed through the broader Data Product Engineering category.
For buyers, this matters because automation alone is not the business case. The business case is faster, more maintainable and better governed analytics delivery. Metadata-driven development supports that by reducing repetitive engineering work, improving change transparency and making generated artifacts easier to govern over time.
Not every KPI follows the same strong ranking pattern, and the weaker results should be named directly. Competitive Win Rate, Competitiveness and Considered for Purchase are lower in the broader Data Product Engineering peer group than in Data Warehouse Automation.
Competitive Win Rate sits at rank 10 in Data Product Engineering and rank 5 in Data Warehouse Automation. Competitiveness sits at rank 12 in Data Product Engineering and rank 5 in Data Warehouse Automation. Considered for Purchase sits at rank 11 in Data Product Engineering and rank 3 in Data Warehouse Automation.
These KPIs should be read differently from Business Value, Connectivity or Support Quality. They are market-presence KPIs. They measure visibility, shortlist inclusion and head-to-head market position against a wider competitive field, including much larger vendors.
The honest read is straightforward: as a smaller specialist vendor, AnalyticsCreator does not win the awareness or shortlist battle at the same rate that it wins on delivered value once evaluated. That is a real gap, but it is a market-visibility problem rather than the same kind of problem as low product satisfaction or weak business value.
The flip side is in the same data. Considered for Purchase improves from rank 11 in the broader Data Product Engineering peer group to rank 3 in the Data Warehouse Automation peer group. That suggests the gap narrows considerably when buyers are looking in the right category.
The Data Warehouse Automation peer group remains the most precise comparison frame for AnalyticsCreator. Data Product Engineering is useful because it tests the application in a broader category, but Data Warehouse Automation is closer to the work AnalyticsCreator was built to support.
AnalyticsCreator supports metadata-driven modeling, generated SQL and pipelines, historization patterns, lineage visualization, deployment packages and Microsoft-oriented analytics engineering. These capabilities map directly to the delivery problems that Data Warehouse Automation buyers are trying to solve.
This is why the 2025 ranking differences are meaningful. Technical Foundation, Platform Reliability, Deployment and Operations, and User Experience all rank higher in Data Warehouse Automation than in the broader Data Product Engineering view, even with identical scores.
Buyers evaluating Microsoft-centric analytics delivery should therefore read both views together. The broader peer group tests reach. The Data Warehouse Automation peer group tests category fit.
The 2025 BARC pattern is easiest to understand when connected to the AnalyticsCreator capability set. AnalyticsCreator is a metadata-driven design application that helps teams model, generate, deploy and govern analytics artifacts in Microsoft-focused environments.
Those capabilities help explain the strength in Connectivity, Business Value and Deployment and Operations. They also help explain why the Data Warehouse Automation peer group shows stronger rankings for technical and operational KPIs. The application is being assessed closer to its core use case.
To understand the capability base behind these results, review the AnalyticsCreator functions and features. To discuss fit for your data environment, use the AnalyticsCreator demo or pricing request.
The 2025 results answer the main question from Part 2: the 2024 gains were not limited to one survey year. Business Value, Connectivity and the operational KPIs hold at near-ceiling scores across both 2025 peer-group views.
The next test is broader again. In 2026, AnalyticsCreator appears in the Data Fabric context, which tests whether the same consistency extends beyond traditional data warehouse automation into a market category AnalyticsCreator was not originally built around.
For background on the 2025 survey context, BARC’s data management trends write-up explains how the market discussion was evolving that year.
For the source context behind this comparison, review the BARC review of AnalyticsCreator and BARC’s survey trends in data management 2025. For AnalyticsCreator product context, use the AnalyticsCreator resources page, the AnalyticsCreator functions and features and Understanding AnalyticsCreator.
Previous: Part 2 covers 2019–2024 and the shift from engineering automation toward business value.
Next: Part 4 covers 2026 and AnalyticsCreator’s expansion into the Data Fabric peer groups. The next article examines whether the consistency seen in 2025 extends into a newer category with different buyer expectations.
To assess whether AnalyticsCreator fits your Microsoft data and analytics environment, review the AnalyticsCreator functions and features or request a demo or pricing discussion.