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Data preparation is a crucial part of analytics applications, but it's complicated. Here are seven common challenges Posted on : Jan 24 - 2022

Data preparation is a crucial part of analytics applications, but it's complicated. Here are seven common challenges that can send the data prep process off track.

The rise of self-service BI tools enabled people outside of IT to analyze data and create data visualizations and dashboards on their own. That was terrific when the data was ready for analysis, but it turned out that most of the effort in creating BI applications involved data preparation. It still does -- and numerous challenges complicate the data preparation process.

Increasingly, those challenges are faced by business analysts, data scientists, data engineers and other non-IT users. That's because software vendors have also developed self-service data preparation tools. Those tools enable BI users and data science teams to perform the required data preparation tasks for analytics and data visualization projects. But they don't eliminate data prep's inherent complexities.

Why is effective data preparation important?

In the modern enterprise, an explosion of data is available to analyze and act upon to improve business operations. But the data used in analytics applications is often gathered from various sources, both internal and external. Most likely, it is formatted in different ways and contains errors, typos and other data quality issues. Some of it may be irrelevant to the work at hand.

As a result, the data must be curated to achieve the levels of cleanliness, consistency, completeness, currency and context needed for the planned analytics uses. That makes proper data preparation crucial. Without it, BI and analytics initiatives are unlikely to produce the desired outcomes. View More