In a typical
organization, most databases are built with data keyed in by different
people. For example, a CRM database will be updated by several sales
reps and each of these reps will have his/her own way of entering
data. It can be ‘Road’ for some, while others may prefer ‘Rd’.
Similarly, it can be Drive or Dr, and CA or California. Such variations slowly corrupt the sanity of
the data and can make it unsuitable for reliable analysis & reporting.

The situation is made all the more worse if the data is aggregated
from multiple data sources where each system follows its own
conventions and processes. To make such data uniform and
credible, periodic data cleansing becomes necessary. Only clean,
sanitized data can provide you reliable inferences when analyzed.…

Helena Schwenk of Ovum Research has a nice post analyzing LucidEra’s failure and what it means for the other SAAS BI players. Helena concludes,

 The spotlight is now likely to shine very brightly on other SaaS BI companies (…) While LucidEra’s collapse doesn’t necessarily mark the end of SaaS BI as we know it, it could mark the start of a SaaS shift towards larger BI players whose brand and broad portfolios continue to dominate the market.

What Helena points out will certainly happen. The “M” in SMBs, will now look at the smaller, one-product SaaS BI vendors with more caution – and rightly so. But they don’t have that many places to look at. The solutions offered by larger BI vendors are typically more expensive and complex and that puts businesses in a bind.…