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To achieve the best results from predictive analytics, prioritize these six top factors, and avoid these five common pitfalls, advises Rado Kotorov, chief innovation officer at business intelligence and data analytics firm Information Builders. (This content originally appeared on Information Management)
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DO -- Make proactive rather than reactive decisions.

The results provide direction based on these likely behaviors. Armed with this information, the organization can capitalize on positive activities and reduce negative ones.
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DO -- Focus on projects that will impact the bottom line by generating profits or reducing costs or risks.

These projects get exposure and approval and enable an organization to capitalize on the effort.
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DO -- Take a broad view of the process and holistic approach to the solution.

What completes the picture is ensuring options are available for deploying the predictive results for action.
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DO -- Perform a thorough collection and exploration of the data.

This enables those who are building the application to get familiar with the information at hand, so they can identify quality issues, glean initial insight, or detect relevant subsets that can be used to form hypotheses suggested by the experts for hidden information.
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DO -- Prepare the data.

Select tables, records, and attributes from various sources across the business. Data must be transformed, merged, aggregated, derived, sampled, and weighed. It is then cleansed and enhanced to optimize results.
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DO -- Select and apply various modeling techniques.

Once the data has been prepared, selecting the right techniques may explain the underlying patterns in data better than others, and therefore, the outcomes of various modeling methods must be compared.
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DON’T -- Fail to focus on a specific business initiative that predictive analytics can enhance.

Focusing on a specific business initiative reduces the chance of “analysis paralysis,” where effort is wasted on trying to fit the analysis findings to an undefined objective.
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DON’T -- Ignore crucial steps, such as data preparation and access, or deployment of results.

When deploying predictive analytics, many companies overlook important steps in the process. One of the most frequently ignored is data preparation and access. In reality, this should be the activity to which the most effort is devoted. In fact, data preparation typically accounts for approximately 60 to 80 percent of the cost of a predictive modeling initiative.
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DON’T -- Spend too much time evaluating models.

Companies often tend to over-evaluate. They add new variables to the models to increase their accuracy, which often requires rebuilding. This delays deployment, and prevents the organization from recognizing the substantial advantages that predictive analytics can offer.
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DON’T -- Invest in tools that yield little or no returns.

When it comes to the computing environment, organizations typically need two systems: one for predictive analytics, and a reporting system to deliver results. This creates additional and unnecessary hardware, support, and maintenance costs. A simpler and more cost-effective approach is to combine these into a single server environment.
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DON’T -- Fail to operationalize findings.

If an application is not built and deployed, the effort devoted to creating a model will do nothing to enhance forward-looking decision-making. The results will remain in a document that few people will refer to in support of their daily activities.