The business intelligence (BI) “boom” of the 1990s was something BIG: big projects designed to give big companies with big budgets a competitive advantage. And while BI delivered big returns for some companies, for others it was nothing but a big headache. An estimated 50% of all data warehousing projects failed to meet their goals. Why? Because many companies underestimated just how big an undertaking BI really was.
Bye-bye to the Old BI:
Business Intelligence for Mid-Sized
Companies
www.oco-inc.com Bye-bye to the old BI: Business Intelligence for Mid-Sized Companies
The business intelligence (BI) "boom" of the 1990s was something BIG: big projects designed to give big companies with big budgets a competitive advantage. And while BI delivered big returns for some companies, for others it was nothing but a big headache. An estimated 50% of all data warehousing projects failed to meet their goals. Why? Because many companies underestimated just how big an undertaking BI really was.
That lesson wasn't lost on mid-sized companies. Lacking the funds and deep IT resources to participate in the first wave of business intelligence, mid-size companies were initially left on the sidelines to watch while big companies increased their competitive arsenal. Most mid-size companies couldn't afford to be early adopters, especially with a 50% failure rate. They waited patiently, biding their time with desktop-based data analysis tools until BI evolved to meet their needs. And with the recent arrival of Software as a Service (SaaS) solutions, it appears their time has finally come.
SaaS evolved from the Application Service Providers (ASPs) and hosted solutions introduced during the Internet boom of the late '90s. But where those technologies failed because of limited flexibility and security, SaaS is succeeding by delivering flexible, secure and-most importantly-robust solutions. The best-known example of SaaS technology to date may be salesforce.com in the customer relationship management (CRM) space. Now, new companies are applying the same successful model to business intelligence. For the mid-size market, SaaS BI is the break they've been waiting for: enterprise wide BI that's easy to use, quick to implement, and delivers a working solution within weeks, not months.
Bye-bye to the old BI
Traditionally, the road to BI has been a long one. Companies had the choice of implementing a BI solution with their own IT department or paying third-party consultants to implement it for them. Both routes required a significant investment in time and money: from gaining executive sponsorship to planning and implementation. Along the way, businesses could expect to face a series of potentially crippling hurdles: data extraction, cleansing and loading of data from all over the enterprise; multivendor hardware and software integration; user training; and, of course, the ongoing support and system upgrades to keep it all running.
Even those companies that successfully completed their data warehouse projects were sometimes in for a bumpy ride:
. Users lacked the detailed analysis they were promised;. "Cleansed" data soon grew muddied with inconsistent and external data sources;. The system became obsolete as the company outgrew the existing functionality;. Complex analysis tools relegated the system to a small group of power users;. Crucial partner/supplier data was missing, limiting supply chain visibility;. It required another project to get additional databases into the data warehouse;
Page 2 www.oco-inc.com. It was time consuming to get more reports scoped and written.
In retrospect, the first wave of BI was doomed to some measure of failure. Businesses who wanted an enterprise wide BI solution were forced to get into the business of becoming a BI solutions builder, a role for which many were unprepared. Their IT departments were suddenly asked to implement or manage the unfamiliar, from aggregating heterogeneous databases to optimizing data schemas for deep drill-down and analysis. It's been estimated that, in the average data warehouse project, 60% of the project time is dedicated to data extraction, transformation and loading (ETL). It's no surprise then that many projects lost vital executive support as months passed with no measurable results.
As a second-generation solution, SaaS BI has learned from the mistakes of the past. These are solutions built from the ground up to work better, deploy quicker, and deliver ROI faster by focusing immediately on pressing business problems. SaaS BI is successful because it takes all of the work out of a data warehouse and still delivers all of the benefits: reliable, deep data analysis. For mid-size companies, on-demand BI represents a great leap forward from end users working off of departmental da... [download for more]
