BI and Analytics With or Without a Data Warehouse

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This week’s guest blog post is by Galia Nedvedovich. Galia is a Marketing Specialist at SiSense – the Big Data Analytics Company. If you haven’t already, check out the real user reviews of SiSense Prism here on IT Central Station.

As defined by Wikipedia, BI and analytics is a set of theories, methodologies, Galia Nprocesses, architectures, and technologies that transform raw data into meaningful and useful information for business purposes, while a data warehouse is a central repository of data which is created by integrating data from one or more disparate sources to serve as a single, clean and accurate business “truth.”

When implemented in conjunction with a data warehouse, BI is centralized under the IT department and therefore enjoys cleaner, more accurate data and the ability to serve as the organization’s single source of truth. When individual departments implement their own BI solutions, without a data warehouse, the data is more likely to be “dirty” and it doesn’t serve as a single source of truth. On the other hand, when the business users control their own data and BI, they can be much more agile and thus able to glean more value from their data, faster.

A new generation of BI technologies has recently made available the best of both worlds. For example, BI solutions based on “in-chip technology” deliver data warehouse scale with in-memory speed at a fraction of the cost of traditional BI solutions.

SiSenseTo learn more about the cutting edge of BI, download this illustrated slide deck, which presents in a clear, easy-to-understand way everything you need to know about the pros and cons of traditional BI architectures, as compared with the latest BI technologies available. Whether or not you have an existing data warehouse, you need to appreciate how the newest BI solutions improve every factor of a BI implementation: data quality, data granularity, scalability, cost, implementation time, technical complexity and more.

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