Eliminate the gap between your applications and analytics (Part 2 of 2)

In Part one of this series we explored the value of combining Dynamics data with other data sources - activity analysis, closed-loop reporting, generating more profitable customers, and creating consolidated dashboards of KPIs.

Traditionally, blending data has required ETL, which can stall analytics indefinitely. To query or visualize data, BI tools must connect to a data warehouse. And ETL often loads data into many silos, forcing data analysts to either analyze sources discretely or combine them manually -- a tedious and time-consuming task.

Let's look at a real-world example.

Say your enterprise uses a medley of SaaS applications: Dynamics for sales performance and lead assessment, Marketo for marketing metrics, Power BI for reports, ERPs for finance. Even if you have an on-premises warehouse, it's unlikely to serve as a single source of truth. Departments connect to different datasets, each with their own structures. And because no one technology governs the data schemas or records, statistical anarchy prevails. Closed-loop reporting eludes an otherwise reasonable business.

Without automation, one application's schema might persist in the warehouse with a focus exclusively on data associated with lead records. Dynamics data, though, is split across leads, which convert to contacts, which are, in turn, are tied to accounts in one system, users in another. It's a mess.

Automating your data pipeline would connect to all these applications via their APIs and bring data together into an on-demand warehouse with a common data schema. This unified schema relates all the objects - from leads and contacts to accounts and users - so things can finally ...