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Business Impact Analysis for Disaster Recovery Planning And Beyond

Sean Salleh 11 Sep 2013 Risk and uncertainty

Business impact analysis (BIA) is a stage in the disaster recovery planning process, in which organizations seek to prepare for business interruptions with a plan to surmount them as quickly and effectively as possible. BIA is the process of understanding business functions and analyzing the impact of an interruption upon them. Some vital business functions may not be able to tolerate an interruption of more than a few minutes; others may withstand interruptions of days or even weeks. It all depends. But when modeling the impact of business interruptions, business teams need to go further than just examining each function one by one.

 

Identifying major risks via business impact analysis Image source: enaxisconsulting.com

 

What is the Impact to be Analyzed?

Business impact analysis often means significant research to gather information before thinking about building a BIA model or planning procedures and remedies in the event of disaster. Talking to managers responsible for a particular business function should yield input about the costs to be borne. FEMA suggests a BIA list that includes possibilities of lost or delayed sales and income, increased expenses, regulatory fines, contractual penalties, customer dissatisfaction, and the delay of plans for new business. Business impacts are then assessed for risk in order to define a disaster recovery plan.

What Can You Really Measure?

In the list above, lost income, expenses, fines and penalties may all be measurable; the delay of business plans too. However, the impact of customer dissatisfaction which alone can send a business into bankruptcy is a more difficult matter. In some cases a business may forecast a need to increase its marketing budget three-fold to recover customer confidence. In other cases, a model may try to express the gap between market expectations and organizational performance as a dollar risk amount. Interestingly, if the gap is such that the performance exceeds expectation, then the risk becomes positive and an opportunity to be exploited in the framework of overall enterprise risk management.

Modeling Global Business Impact Analysis

It is frequently observed that enterprises fail definitively because of a combination of mishaps, where each mishap individually is not fatal, but the combination of all of them together is. Although a disaster recovery plan may only set out procedures and solutions to each potential disaster one by one, an organization also needs to know which combinations of disasters could really sink it. With the total number of things that could go wrong, simulation is often the only reasonable way to model possible outcomes. Using importance analysis in Analytica to detect the events that would have the most impact and then combining models using Analytica’s Intelligent Array feature allows modelers to go beyond serial disaster recovery planning to see the bigger picture.

Timing Issues about Impact

A simulation also allows for flexibility in modeling impact according to dates and timing. A major sales outlet that is damaged just before peak shopping season, or production line breakdown just after the launch of a new flagship product could have far greater impact than similar events at other times. Similarly models should also take account of the relative time sensitivity of different functions affected by a disaster. Denial of access to the headquarters of a company could stop both real time customer service and preparation of a strategic marketing campaign, but it’s probably the interruption to customer service that will have the biggest immediate impact and that needs to be modeled accordingly.

If you’d like to know how Analytica, the modeling software from Lumina, can help you manage risks of all sorts, then try a thirty day free evaluation of Analytica to see what it can do for you.

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Sean Salleh

Sean Salleh is a data scientist with experience in guiding marketing strategy from building marketing mix models, forecasting models, scenario planning models, and algorithms. He is passionate about consumer technologies and resource management. He has master's degrees in Operations Research from University of California Irvine and Mathematics from Northeastern University.

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