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A Beginner’s Guide to Predictive Analytics

Effective data usage requires an analytics-minded manager.
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Demand for analysts has grown exponentially as data collection has exploded. As companies hire more and more analysts, the need for analytics-minded managers continues to accelerate.

McKinsey predicted in 2011 that increased use of data would lead to a shortage of analysts. They also noted that the analytics-oriented manager would become 10 times more important than analysts. We may have reached that point.

When it comes to more difficult projects using predictive analytics, it can be difficult for managers to find balance between the intricacies of the work analysts are doing and the end goal. Defining the project into pieces that are more manageable is imperative.

Here are a few steps in the process of predictive analysis:

1. Define your project. Make time to completely understand your final objective. What are the needs and priorities of your organization? What resources are available? Remember that time, money, human resources and quality are all involved in this discussion.

2. Prepare the data. Collect and clean any data needed for your analysis. Assumptions based on flawed data won’t do you any good, so don’t be afraid to make changes to current data collection tools to fit the project.

3. Build a model. Rank your variables by importance, and allocate proportions accordingly. Take the time to calculate algorithms as carefully as possible. These are critical to effective predictions.

4. Validate the model. When testing data, include not only technical measures but also business relevant measures to ensure the model will work with future datasets.

5. Use the model. Present the model’s key insights to the main users, and continue to look for feedback to improve both the model and its presentation. A great model doesn’t provide value if its insights are not easily seen or understood.

Understanding the less technical overview of predictive analytics helps managers conceptualize the entire process without getting lost in the details. The next step is meaningful communication with your analytics team to make these steps actionable in your next predictive analysis project. 

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