Challenges in Data Preparation
Now, we have already understood that data preparation is a critical stage in the analytics process, yet it is fraught with numerous challenges like:
- Lack of or insufficient data profiling:
- Leads to mistakes, errors, and difficulties in data preparation.
- Contributes to poor analytics findings.
- May result in missing or incomplete data.
- Incomplete data:
- Missing values and other issues that must be addressed from the start.
- Can lead to inaccurate analysis if not handled properly.
- Invalid values:
- Caused by spelling problems, typos, or incorrect number input.
- Must be identified and corrected early on for analytical accuracy.
- Lack of standardization in data sets:
- Name and address standardization is essential when combining data sets.
- Different formats and systems may impact how information is received.
- Inconsistencies between enterprise systems:
- Arise due to differences in terminology, special identifiers, and other factors.
- Make data preparation difficult and may lead to errors in analysis.
- Data enrichment challenges:
- Determining what additional information to add requires excellent skills and business analytics knowledge.
- Setting up, maintaining, and improving data preparation processes:
- Necessary to standardize processes and ensure they can be utilized repeatedly.
- Requires ongoing effort to optimize efficiency and effectiveness.
What is Data Preparation?
Raw data may or may not contain errors and inconsistencies. Hence, drawing actionable insights is not straightforward. We have to prepare the data to rescue us from the pitfalls of incomplete, inaccurate, and unstructured data. In this article, we are going to understand data preparation, the process, and the challenges faced during this process.
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