In our last segment we reviewed the process of applying a predictive model to your dataset – going from assessment of software needs to selecting the observation period to selecting the key variables (dependent and independent), choosing a methodology, and then building and testing the model. Predictive modeling is the crux of predictive analytics, where all your data preprocessing efforts are brought to focus on extracting insights to prove your business question.
Once you’ve finished identifying your data, collecting and cleansing it, and building and testing a successful predictive model, it might be easy to assume the project is done. But there is one more crucial aspect of predictive analytics we want to discuss and that concerns the deployment stage.
Deploying Your Predictive Model
It’s one thing to have an elegant model providing you with great predictive power that reveals which customers are most likely to respond to your targeted advertising campaign. It’s entirely another thing to ensure that the model gets used in your organization to drive real business value. Figuring out how the predictive model gets served up to the rest of the company is known as the deployment stage. This can range from a simple spreadsheet with results given to one person to a complex system where multiple data streams must be fed into the model. Setting up these data streams will likely involve additional software engineering work.
In order to turn your data into quality, actionable customer insight your stakeholders will need a way to read and interpret the predictive model. This is where a data visualization or business intelligence tool will help immensely. A popular program for this is Tableau, which allows you to query your data mining results with custom SQL for easy data visualization.
So taken together, we can see that making the output of the predictive model available across the organization is an additional time-consuming, but necessary task. As one source well states, “The deployment of a successful model is sometimes more work than building the model itself.” Given the costs and time committed this far, it’s very important to ensure there is follow through at this crucial juncture.
The Main Takeaways
We opened this series with a look at the way Big Data represents an epic technological shift transforming all levels of business and society. Gartner best summarizes the latest trends as follows: “Big data creates a new layer in the economy which is all about information, turning information, or data, into revenue. This will accelerate growth in the global economy and create jobs.” As the heart of Big Data, predictive analytics takes in disparate sources of data and extracts insights to provide accurate forecasts of future outcomes. PA is critical for providing a complete view of customer behaviors and trends. And businesses that learn to capture these insights will increase revenue, cut costs, and stay ahead of the competition.
So let’s pull together the main takeaways that should be included in your predictive analytics strategy going forward.