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By George O'Conor
The On Demand Business Analytics/SaaS model is the wave of the future for small to medium size businesses that do not have large IT budgets and cannot afford to pay the high price of existing in-house analytic reporting tools.
They need solutions that show an immediate ROI and provide an up-to-date and accurate view of their essential business information that can be shared easily by their entire organization.
Many decision-makers are finding that traditional in-house BI solutions cannot provide a single reporting and analysis solution that allows them respond in real-time to changing conditions. Why?
Traditional BI solutions:
• Process data much too slow.
• Only process a fraction of a company’s data.
• Provide a limited view of essential information about daily business operations.
• Encourage silos of department information so the same information is not shared across the entire company.
• Focus on transaction systems and not on data quality. As a result, much of the data is useless, redundant, inconsistent, or inaccurate and top executives find they are unable to trust the data
• Are prohibitively expensive and require long implementation periods of as much as two years
The Power of On Demand Business Analytics
The power of On Demand business analytics is the ability to combine demographic and behavioral data with sales information in real-time to leverage customer, vendor, and supplier relationships. To do this, On Demand analytics employ hyper-optimized automation processes to cleanse, normalize, and integrate huge amounts of disparate data from any source.
Because this solution is an online, On Demand software service rather than a hosted application, decision-makers can make quicker, better-informed decisions at a fraction of the cost of traditional BI solutions.
Initially, data is collected from virtually any source: multiple business systems, operating systems, or hardware and software platforms and sent to the hosted service, where automation processes are employed to cleanse, normalize, and integrate this large amount of disparate data.
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