Data Management
Data Management in SEI is the strategy and tooling used to access, organize, and report on data across diverse systems and environments. Whether you’re working with a single ERP, consolidating multiple business systems, connecting to cloud‑hosted data, or analyzing high‑volume transactional records, SEI provides proven methods to ensure performance, scalability, and usability in every reporting scenario.
This section serves as your guide to choosing the right data management approach, by mapping common operational scenarios to the tools best equipped to handle them.
Data management tools
| Tool | Description |
|---|---|
| DataSync | SEI's data warehouse and ETL engine used for replication, consolidation, joining, and transformation. Ideal for cloud data, ERP offloading, and preparing datasets for analysis. |
| Data Model Designer | Design, organize, and manage the structures that define your integrated data model. Enables efficient blending of multiple datasets and preparation for reporting or OLAP. |
| OLAP Manager | Build and manage OLAP cubes for high‑performance analytical processing. Essential for large datasets (> 5M rows) and complex multi‑dimensional reporting needs. |
By combining these tools, you can implement the right architecture for your reporting and analytics environment:
- DataSync for securely copying and preparing data.
- Data Model Designer for structuring and integrating.
- OLAP Manager for optimizing speed and scalability at scale.
This section outlines common scenarios, from ERP performance optimization to multiple source integration, and shows which mix of tools gives the best results.
Scenarios
Single data source
For scenarios where you only have one source system.
| Scenario | Volume | Data access | DataSync | OLAP |
|---|---|---|---|---|
| Small volume | < 5M rows | Real-time | No | No |
| Large volume | > 5M rows | Real-time | No | Yes |
Notes:
- Direct ERP access sufficient for small datasets.
- For large datasets, OLAP improves performance & reduces query load.
ERP Performance Challenges
For cases where reporting load impacts ERP responsiveness.
| Scenario | Volume | Data access | DataSync | OLAP |
|---|---|---|---|---|
| Small volume | < 5M rows | Near real-time | Yes | No |
| Large volume | > 5M rows | Near real-time | Yes | Yes |
Notes:
- Replicate ERP data using DataSync to stabilize performance.
- For large datasets, combine replication + OLAP cube for speed.
Cloud data
For scenarios involving cloud-based data sources.
| Scenario | Volume | Data access | DataSync | OLAP |
|---|---|---|---|---|
| Small volume | < 5M rows | Near real-time | Yes | No |
| Large volume | > 5M rows | Near real-time | Yes | Yes |
Notes:
- Real-time direct access is not possible for cloud data.
- Always replicate cloud data via DataSync first.
- OLAP recommended for large datasets to maintain speed.
Multiple data sources
For scenarios with two or more different systems.
| Scenario | Goal | DataSync | OLAP | Transform |
|---|---|---|---|---|
| Independent reporting | No merge | No | Yes | No |
| Unified historical reporting | Merge & unify | Yes | Yes | Yes |
Notes:
- Independent: report individually from each source.
- Unified historical: merge sources (ERP + CRM, etc.) via DataSync, then OLAP if volume is high.
Transformation needs
For complex data joins or format mismatches.
| Scenario | DataSync | OLAP | Transform |
|---|---|---|---|
| Complex joins/mismatched IDs | No | Yes (if large volume) | Yes |
Notes:
- Required when linking datasets with different identifiers (e.g., ERP SKU vs CRM product name).
- DataSync acts as data warehouse to clean/transform before OLAP.