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The 4 Roadblocks of Data Preparation: Automating Data Prep to Focus More on Data Analysis

May 1, 2016

When they’re moving at the speed of business, data analysts have limited time to spend analyzing data before it goes stale. If analysts are forced to move slowly, they risk arriving too late to capitalize on potentially profitable transactions, investments, customer marketing opportunities, or social media events.

Why should business analysts have to sacrifice precious time for data preparation tasks like accessing, cleansing, normalizing, and blending disparate data sets? Ventana Research reports that companies using predictive analytics spend 40 percent of their time preparing data for analysis and 22 percent accessing the data—the least gratifying parts of the analytical process. Considering that those tasks are not as essential to decision making as building and deploying models, it’s no wonder that companies see them as bottlenecks. Similarly, Blue Hill Research reports that most analysts spend 40 to 60 percent of their time preparing data and whatever time is left over on analysis.

This e-book examines of the four most common roadblocks posed by data preparation, with potential solutions for overcoming them so that business analysts can spend more time on data analysis. Readers will take away insights into overcoming the roadblocks in their own organization.

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