1) Define your project. First you need to clearly define the business question you’d like to answer or the problem you’re trying to solve. In other words, what do you want to be able to predict? Being clear on the ideal project outcome will inform your data requirements and allow your predictive model to generate an actionable output.
2) Build the right team. While new tools make it much easier to perform predictive data analytics, you should still consider having these five key players on your team:
- An executive sponsor who will ensure funding and prioritization of the project.
- A line-of-business manager who deeply understands the business problem you’re trying to solve.
- A data wrangler or someone with data management expertise who can clean, prepare, and integrate the data (although some modern analytics and BI tools include data integration capabilities).
- An IT manager to implement the proper analytics infrastructure.
- A data scientist to build, refine and deploy the models (although AutoML tools now allow data analysts to do this).
3) Collect and integrate your data. Now you’re ready to gather the data you need and prepare your dataset. Bring in data representing every factor you can think of to provide a complete view of the situation and make your model more accurate. You’ll probably be bringing in both highly-organized and formatted structured data such as sales history and demographic information, and unstructured data such as social media content, customer service notes, and web logs. Prepping data requires you to do the following:
- Correctly label and format your dataset.
- Ensure data integrity by cleaning up incomplete, missing, or inconsistent data.
- Avoid data leakage and training-serving skew.
- After importing, review your dataset to ensure accuracy.
You’ll be working with big data, and possibly even real time streaming data, so you’ll need to find the right tools. As stated above, cloud data warehouses can now cost effectively bring the storage, power, and speed you need.
4) Develop and validate your model. The next stage involves building, training, evaluating and deploying your predictive model. There are two ways you can go about this. You can find and hire a data scientist to develop a model or you can use an AutoML tool to develop one yourself. Explainable AI techniques and processes will help you understand the rationale behind the output of your model. Also, there are two main types of algorithmic models–classification and regression–which we describe in the next section. These algorithms ultimately place a numerical value, weight, or score on the likelihood of a particular event happening in the future. You’ll need to test and refine your model multiple times to come up with the best performer, the model which generates predictions that meet what you would expect.
5) Deploy your model. Finally, you can put your model to work on your chosen dataset. You can use the results as a one-time or ongoing decision making or you can automate actions by integrating the output into other systems. Ideally, your model should automatically adjust as new data is added over time as this will improve the accuracy of the predictions.
6) Monitor and refine your model. Keep a close eye on the outputs of your model to make sure it continues to provide results you expect. You’ll likely need to tweak the model as new variables emerge. You can also improve your model’s predictions by applying data mining techniques such as clustering, sampling, and decision trees to data collected over time.