SaaS Makes Business Intelligence Work for SMBs - Slide 4

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Good BI technology gives a better view of the business. Unprofitable product lines can be scrapped and resources can be moved to more profitable areas of the business. However, where SMBs differ from traditional users is that their markets and business can change and develop over a much shorter timescale. Business users cannot afford to wait weeks for BI experts to have the time to perform technical queries as in a traditional system. The data must be streamed directly from sources such as Excel or Salesforce and be easily manipulated by anyone in real time.

When it comes to acquiring business intelligence (BI) capabilities, many small and medium-sized businesses (SMBs) have traditionally been at a disadvantage. They often cannot justify the large CapEx demanded of them, do not have the IT resources to run an on-premise database and server system, and are not sure that they have the breadth or depth of data to warrant such a system. Often their needs changed and evolved far faster than software could be developed and installed.

However, just as the prediction that there would never be more than a million cars in Britain due to the lack of chauffeurs is now laughable, the emergence of self-service BI has blown these traditional problems out of the water and opened up BI to millions of new companies.

Self-service BI is all about empowering the end user and removing the high barrier to entry, both in terms of CapEx and in-house expertise. The software-as-a-service (SaaS) model not only breaks down this barrier, but also incorporates the flexibility to grow and evolve as smaller companies respond to their data and expand.

In this paper, We Are Cloud, providers of the SaaS business intelligence application Bime, explore the 10 key considerations for SMBs setting up a BI project and explore common mistakes to avoid. For more detail on successful BI and common pitfalls, be sure to see the article, “10 Ways for SMBs to Capture Value from Their Data.”

Related Topics : Business Intelligence Vendors, Data Quality, Data Warehousing, Key Performance Indicators, Knowledge Management

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