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Audit Analytics 101: Bridging the Data Integration and Business Intelligence Gap

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By John Joseph

No matter whether you call it a Big Data problem or not, the biggest challenge for most business intelligence or data integration teams is the great data divide, where data within the enterprise is increasingly fractured and allocated into separate departmental silos that are sometimes not even accessible by the people that need it the most. 

The data gap isn’t new, but it is certainly getting worse.  It’s partly due to the limitations of spreadsheets and traditional business intelligence platforms, which were designed back in the 1980s and 1990s when data wasn’t quite so big or varied or fast moving. But while they haven’t changed much since then, the requirements of that data have changed significantly in just the last few years, with organizations now needing to consider how new data sources, such as machine-generated data, increase the performance demands on analysts and their organizations.  Today when it comes to business performance and analytics, regardless of whether it’s the sales department, customer service organization, or finance group, there is a requirement for greater flexibility, speed, and control.  

These three can wreak havoc on organizations’ ability to govern, optimize, and ensure compliance for key processes that affect revenue flow and risk if they don’t have a foundation architecture that supports that is built to support them.  In the face of such challenges, companies are increasingly emphasizing the need to combine two important capabilities: data discovery and audit analytics.

Data discovery allows users to bring together a wider array of stakeholders to explore diverse data types much more quickly, and at a much lower cost, than traditional means. In doing this, the people who need information to make strategic decisions can get much closer to relevant data. It lets people explore every possibility without going in to the analytics process with preconceived ideas about likely outcomes that can sometimes mask alternate, valuable truths hidden in the data. 

Audit analytics allow businesses to determine if specific processes, such as revenue assurancefraud management or customer service, are working, which ones aren’t, and why performance is deviating from expectations or may not be in compliance with regulations or company initiatives. With this, For example, a data discovery solution monitoring transactions for a financial services company can look for certain patterns that may indicate fraudulent transactions. In another instance, a telecommunications company could monitor the usage patterns of its customers and make service-change recommendations to best suit the customer and avoid churn.

These are but a couple examples of how I’ve heard organizations use audit analytics. Have you heard others?