In my previous article we saw what are all the worst approaches followed by organizations while deploying a Predictive analytic project. This article will provide you information on how to deploy successful predictive analytics model.
Successful Predictive Analytics Deployment
Now that we’ve discussed the wrong approach to predictive analytics, let’s look at some of the critical steps that must be taken to ensure its success.
Understanding the Business Need
In my previous article we saw what are all the worst approaches followed by organizations while deploying a Predictive analytic project. This article will provide you information on how to deploy successful predictive analytics model.
Successful Predictive Analytics Deployment
Now that we’ve discussed the wrong approach to predictive analytics, let’s look at some of the critical steps that must be taken to ensure its success.
Understanding the Business Need
As mentioned earlier, it is crucial for companies to identify the drivers behind the predictive analytics project in the early planning stages. Once an organization defines what new information it is trying to uncover, what new facts it wants to learn, or what business initiatives need to be enhanced, it can build models and deploy results accordingly.
Understanding the Data
A thorough collection and exploration of the data should be performed. 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. This also ensures that the available data will address the business objective.
Preparing the Data
To get data ready, IT organizations must 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. These steps may need to be performed multiple times in order to make data truly ready for the modeling tool.
Modeling
Once information has been prepared, various modeling techniques should be selected and applied, and their parameters calibrated to optimal values. Choice of the modeling technique is determined by the underlying data characteristics or by the desired form of the model for scoring. In other words, some techniques may explain the underlying patterns in data better than others, and therefore, the outcomes of various modeling methods must be compared. A decision tree would also be used if it were deemed important to have a set of rules as the scoring model, which is very easy to interpret. Several techniques can be applied to the same scenario to produce results from multiple perspectives.
Evaluation
Thorough assessments should be conducted from two unique perspectives: a technical/data approach often performed by statisticians, and a business approach, which gathers feedback from the business issue owners and end users. This often leads to changes in the model; but while the technical/data evaluation is important, it should not be so stringent that it significantly delays implementation and use of the model. The model’s business value should be the primary test.
Deployment
Deployment, the final step, can mean one of two things: the generation of a single report for analysis, or the implementation of a repeatable data mining or scoring application. The goal here is to create a reusable application that can be used to generate predictions for large volumes of current data. The results are then distributed to front-line workers; in a format they are comfortable with – reports, dashboards, maps, or graphics – to enable proactive decision-making.
Avoiding common worst practices and adopting best ones, are the key to successfully implementing and using predictive analytics. By knowing what pitfalls to avoid, and what important steps need to be taken, companies can accelerate implementation, maximize user adoption, and realize substantial ROI.