With the inception of predictive analytics the reactive decision making has been proven unsuccessful. Organizations have started to take more proactive approach by using predictive analytics to make critical decisions to uncover a problem or an opportunity. Predictive analytics not only enables the organization to determine the forecast of the problems and opportunities but also keeping the bad alternatives futures from happening. Hence companies can foresee their problems well in advance and neutralize them at a preliminary level.
With the inception of predictive analytics the reactive decision making has been proven unsuccessful. Organizations have started to take more proactive approach by using predictive analytics to make critical decisions to uncover a problem or an opportunity. Predictive analytics not only enables the organization to determine the forecast of the problems and opportunities but also keeping the bad alternatives futures from happening. Hence companies can foresee their problems well in advance and neutralize them at a preliminary level. According to research from IDC, organizations using predictive analytics solutions generate an average return on investment of 145 percent. Regrettably, many companies don’t implement it correctly and fail to achieve these desired results
Common Worst Practices while deploying a Predictive Model
As beneficial as predictive analytics can be to an organization, implementation and deployment projects often fall apart or fail to get underway due to common poor practices, procedures, and decisions, such as:
- Failing to focus on a specific business initiative that predictive analytics can enhance
- Ignoring crucial steps, such as data preparation and access, or deployment of results
- Spending too much time evaluating models
- Investing in tools that yield little or no returns
- Failing to operationalize findings
Failing to Focus on a Specific Business Initiative
Mostly, companies begin building their predictive application with loose goals in mind and trying to discover something critical that they don’t know. Substantially they end up in trying various analytical models and forces developers into a never-ending cycle of definition, evaluation, and fine-tuning. The best approach and successful predictive analytics endeavor for an organization is to define the project objectives and requirements that will satisfy their business needs. Predictive analytics will be more effective when it is used to identify expected cases and to apply insight from specific patterns and trends existing in the data to these new cases.
Ignoring Critical Steps
One of the frequently confronted failures in deploying predictive analytics is ignoring critical steps. Many companies while deploying the predictive model many organizations take major efforts to look only at the important steps and often ignores the data preparation and access process. 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.
Spending Too Much Time on Model Evaluation
Predictive models must be evaluated to determine how accurately they predict patterns. Primarily, they must be measured from a data perspective and then they must be assessed from a business perspective to ensure they will meet end-user expectations and requirements. Accuracy comes at a cost, and companies must decide how precise they need their models to be. Companies often tend to over-evaluate. They add new variables to the models to increase their accuracy, which often requires rebuilding. They test and retest the models endlessly, spending tremendous amounts of time making continuous refinements because they are not quite perfect. This delays deployment, and prevents the organization from recognizing the substantial advantages that predictive analytics can offer.
There is a tradeoff to be made between time to market, usefulness, and accuracy. Companies must sacrifice some precision in order to accelerate deployment. Or they must halt implementation and rollout – and delay the realization of benefits – to achieve higher levels of accuracy. The truth is, if a model is better than the current approach to forward-looking decision-making (and it likely is), then it should be considered ready for deployment. No model will ever be perfect, because shifting business strategies and evolving end-user needs require continuous modifications.
Investing Heavily in Analytic Tools With Little or No Return
There are various common mistakes made when it comes to investing in predictive analytics tools. Companies frequently end up in buying expensive, complex analytical tools that is way too sophisticated for their needs. These solutions not only come with very high price tags, but also they are typically hard to deploy and difficult to use by anyone other than statisticians and experienced analysts. As a result, they likely contain features and functions that will never be used. All of these factors will significantly lessen the ROI of an organization.
Failing to Operationalize
For predictive analytics to succeed, it must be embedded into applications that are leveraged whenever users need to make decisions. 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. However, when a model is incorporated into a dashboard or reporting environment, the results will be readily accessible to end users, whenever they need them. This will help to create an analytics-driven culture across the entire business.
How to avoiding Worst Practices
The worst practices we have highlighted don’t have to derail a predictive analytics initiative. In fact, they can all be easily avoided by:
When planning a predictive application, companies must consider total cost of ownership and anticipated return, to ensure that maximum value is achieved.
Focusing on Bottom-Line Initiatives
Create models that will provide forward-looking intelligence to help solve specific problems (i.e., minimizing customer churn by uncovering the factors that contribute to it) or help to achieve certain goals (i.e., increasing up-sell and cross-sell revenue by understanding what new products customers are most likely to buy).
Guarantee the most accurate possible results by ensuring that disparate data is easily and properly accessed and cleansed before the models are created and applied.
Evaluate the Model, Without Over-Evaluating
The model must be tested to ensure that it provides better decision-making capabilities over current analysis methods. But over-evaluation can delay deployment and hinder ROI. It simply needs to be assessed until it is determined that it will provide value. At that point, it can be implemented. The statistical properties of the finished model are secondary to the value it brings to the business.
Deploying the Results
The insight provided by predictive analysis efforts must be shared with key stakeholders across and beyond the organization. For example, a bank that has predicted which customers are most likely to churn should disseminate that information to all those who interact with those clients, including call center staff and branch personnel. That way, everyone can contribute to correcting the problem and ensure that countermeasures are being implemented.
We will see how to adopt best practices and the key to successfully implementing and using predictive analytics in the next article
About the Author
Shaughn is an industry analyst for business intelligence. For over ten years, he has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Shaughn is the channel expert for BI for the small and Mid-Market segments at ZSL and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. He can be reached at email@example.com. And please visit Shaughn’s blog at zslbiservices.wordpress.com
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