Wasn’t your data meant to help solve problems, not create new ones?
This article investigates the current challenges and bottlenecks around data integration for analytics, with recommendations on how to resolve these issues with new approaches.
The arrival of cloud computing was accompanied by promises of a revolution in data analytics. Attracted by low storage costs and an explosion of data from various systems and sources, data has been uploaded wholesale to data lakes and sometimes processed into enterprise data warehouses. The potential for business users to exploit these vast amounts of data was an easy sell for IT vision leaders, and budgets were allocated for the creation of new data architectures.
Unfortunately, the new data architecture has begun to cause more problems than it solves for both IT vision leaders and business users alike. Legacy analytics systems built around OLAP cubes, or dated ETL workflows, aren’t able to nimbly tap into this wealth of data and it is causing a backlog of work for IT teams and missed opportunities for the business users who need to access, transform, manipulate, and connect to data to inform critical business decisions.
This article was originally published by Domo, read more.