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Madan Sheina, Ovum lead analyst and author of the report, says that 'quantitative growth in the BI software market has also been accompanied by rapid change, particularly in the way BI systems are built, and what is built.
'Similarly, the expectations of BI customers, has changed, as they demand bigger, faster and cheaper systems. Traditional BI technologies, architectures and processes are now struggling to keep pace. Many fail to address two fundamental business needs: agility and adaptability, which enable organisations to react quickly in today's constantly changing business and regulatory environment,' Sheina says.
Ovum's survey found that as organisations seek greater business agility, BI technologies are evolving for quicker and more nimble analytics, leveraging new tools, technologies and approaches such as in-memory engines, columnar databases, appliances, event stream processing (ESP), data mashups and software-as-a-service (SaaS) platforms. Ovum believes these technologies will be key components in achieving analytic agility in 2012 and beyond.
The report found that enabling faster business analytics was only part of goal, and Sheina notes: 'Organisations are also looking at new types and sources, such as social media, streaming and mobile data, to deliver broader and deeper business insights. Big Data is challenging BI systems to scale cost-effectively, which will require a rethink of traditional data warehousing and BI architectures. This expanding scale will also put the spotlight on critical data-management issues such as data quality and data governance.'
In addition, the Ovum recommends that enterprises need to rethink traditional approaches and make their BI systems more predictive.'Enterprises must quickly anticipate and react and adapt to business opportunities and threats in their market. Knowing what might happen, as opposed to analysing what has happened, is a potent competitive weapon for business and affords even greater operational agility. To achieve this, BI vendors need to combine data analysis that is focused on historic data with predictive technologies, and to focus on specific business processes,' Sheina concludes.
