Analytics
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Every analyst knows that great analysis starts with the right data. Predictive analytics is no different. To build the best performing models, it is critical to get the data in the best shape before running the analytics and building out the modeling process. That is why we are happy to release the latest in our "cookbook" series, the "7 Steps to Data Blending for Predictive Analytics."
The goal of this cookbook is to show analysts and even data scientists for that matter, that there is an easier way to prepare data for predictive analysis. There is no reason to spend 60-80% of time waiting on the right data to run your analysis. This cookbook gives a step-by-step guide into how data blending in Alteryx can help simplify this process and allow analysts to spend more time on model creation, evaluation, and deployment of models. We realize that many times analysts doing predictive analytics struggle with a variety of tasks like:
- Accessing the right types and systems of data
- Preparing and cleansing data
- Joining multiple datasets
- Delivering a repeatable process for future analysis
- Relying on others to create the dataset they need
It is these tasks that data blending can truly make an impact in processes and outcomes. In addition, we realize that many organizations spend a significant number of hours at an exorbitant cost to build predictive models and deploy them throughout their organization. Alteryx doesn't force you to replace everything you have built, but instead enables you to take advantage of these existing models.
If these are struggles and concerns you deal with in your current analytic environment, then I encourage you to check out this cookbook to learn more about Alteryx and data blending for predictive analytics.
