![](https://iqanalytics.io/wp-content/uploads/2021/12/icon-data-warehousing.png)
Even optimizing your data pipeline a little bit can go a long way. A well-designed Data Warehouse serves a valuable role in unifying the conceptual model of your enterprise. Properly archived data is safe for use by your BI tools without further transformation, eliminating duplication of logic throughout your analytics platform. What’s the catch?
Advocates of the Data Warehouse promise to break down information silos, to distill your enterprise to a “single source of truth”, and a slew of other benefits. The fact is, delivering these lofty goals in their fullest entails a daunting effort by expensive specialists. Without sufficient resources and managerial willpower, these endeavors are quickly abandoned.
iQanalytics takes a pragmatic approach. We start small and build only what is necessary, but in a way that is extensible for future needs. The Data Warehousing skillset covers many solutions that can be applied strategically. We can build you an enterprise-wide DW, but you might only need a few ETL processes to complete a useful data pipeline. (Indeed, our favorite BI platforms provide built-in DW capabilities, but using them bears the cost of vendor lock-in…)
If your organization is new to analytics, it helps to rein in the scope. You could focus on key revenue drivers — your sales department, for instance — and bootstrap future expansion from there. This Data Mart approach aims for a manageable scope while maximizing ROI. Success here will drive buy-in from the rest of the organization, as well as pay for it.
Technologies
- Microsoft SQL Server
- Snowflake
- MySQL
- PostgreSQL
- Power BI
- Domo
- Tableau
- SSRS
- C#.NET
- VB.NET
- .NET ASP MVC
- .NET Core
- Javascript
- PHP
- Python
- XML/JSON