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A Lighter Load - Continuous Monitoring as a Big Data Alternative


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"Big data" and analytics have become the rage within the executive suite. The promise is immense - harness all the available information within the enterprise, regardless of data model or source, and mine it for insights that can't be seen any other way. In short, senior managers become more effective at business planning, spotting emerging trends and opportunities and anticipating crises because they have the means to see both the metaphorical trees and the forest at the same time.

However, big data technologies don't come without a cost. It takes massive computing power, including powerful database systems and highly specialized software to manage unstructured as well as structured data across the enterprise. It requires dedicated staff to develop, deploy and manage these systems, which impact the breadth of managerial operations.

In addition, big data forces organizations to confront inconsistent data structures, mismatched goals and procedures, integration challenges and a wide range of other issues that must be brought into alignment before it can be successfully deployed. In short, while potentially very powerful, big data is not a trivial commitment.

All of which raises an interesting question - can many of the benefits of big data be gained at significantly lower cost, with a shorter deployment time, faster return on investment and lower overhead?

For many organizations, the answer is yes. And continuous transaction monitoring (CTM) is how they do it. By applying targeted analytics that focus on structured financial transactions in real-time, CTM delivers the sophisticated forensics that achieve many of business aims of big data, including the ability to continuously improve business processes and internal audit controls. However, CTM represents a much faster return on investment and an excellent overall value.

CTM automatically extracts key transaction information from ERP and other financial systems from across the enterprise. This data is kept in its own data warehouse, so that consistent, comprehensive analysis can take place across disparate, incompatible systems and databases. The process takes place on a daily basis with minimal impact on production systems.

Once these apples-to-apples comparisons become possible, CTM applies a wide variety of forensic techniques to each transaction as it is executed. This near-real-time capability means that internal audit staff and senior executives receive critical feedback on the root causes of improper or fraudulent transactions almost immediately, rather than having to wait weeks or months for reconciliation or post-closing audits.

CTM does much more than alert if a transaction violates an audit control. It also builds a body of evidence about why a transaction was flagged. It's this extra level of analysis using CTM's common data model and a consistent, comprehensive set of measurements across the enterprise that sets CTM apart from other solutions. CTM delivers analysis that works both within and across controls. As a result, it uncovers potential problems in business processes that audit-based tools can't see, let alone resolve.

Internal auditors routinely use CTM to construct continuous improvement loops for automated controls. Likewise, business process owners and senior managers use CTM to connect business trends to the causes that underlie unexpected results. In effect, CTM serves a critical bridge between audit and the executive suite, linking governance, planning and performance management into a cohesive continuous improvement loop of its own.

Of course, big data technologies promise many of these same benefits. However, big data operates from a different set of assumptions. First, it is an inherently top-down approach to analytics. CTM delivers many of the same insights into operations, but does so from the bottom up by connecting suspicious transactions with root causes.

Likewise, big data technologies look at all information across the enterprise, and then use the findings to work down to smaller processes that can be improved. CTM assumes that operational insights are typically driven from financial transactions, and can then be extended to broader business processes.

Big data by definition means a significant investment across the enterprise and places a heavy burden on IT. CTM typically places no more load on ERP and other systems than that imposed by adding another user. It's much less expensive to purchase, deploys in as little as 90 days and often delivers a full ROI within a few months of deployment.

Finally, big data also requires deep changes in corporate structure and culture in order to fully realize its benefits. This expertise in seeking, finding and applying data in new ways takes time for any organization to learn how to master. It's not just IT that's affected. Management needs to learn how to take advantage of a tool that cuts so deeply across operations, too. It takes time to gain this institutional experience.

CTM works much more easily within existing IT systems and corporate organizational structures. At the same time, its targeted approach to analytics delivers exactly the type of expertise businesses need in order to migrate to a full big data solution in the future.

Big data, without appropriate structure, insights, actionable information and processes for business improvement, threatens to become just that - big, expensive and data-intensive. While it can deliver big benefits, not every organization is ready to make that investment right now, nor do many organizations have the in-house knowledge to quickly and easily incorporate big data into their daily operations. CTM provides a very cost-effective alternative that delivers much more immediate benefits, with much less disruption to the enterprise.

Better yet, CTM also works cooperatively with big data when an organization decides it's ready to make that move. CTM's targeted approach continues to connect the findings of big data with the financial root causes underneath the trends. As such, it makes the essential connections that senior executives will need to turn the promise of big data into better, more proactive long-term business planning.

About the author:

Patrick Taylor is CEO of Oversight Systems. Oversight Systems' continuous transaction monitoring software enables enterprise organizations to drive cost savings, reduce risk, combat fraud and document regulatory compliance. www.oversightsystems.com


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