Data Preparation

Data preparation is the transformation of raw data into a form that is more suitable for modeling so that data scientists and analysts can run it through machine learning algorithms to uncover insights or make predictions.

Why is Data Preparation Important?

Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require some amount of preparation before they can yield useful insights. Some datasets have values that are missing, invalid, have inaccuracies or other errors, which are difficult for the algorithm to process.

The algorithm cannot function if data is missing. If the data is incorrect, the algorithm produces less accurate, if not misleading, results. Some datasets simply lack useful business context (for example, poorly defined ID values), necessitating feature enrichment. Good data preparation results in clean, well-curated data, which leads to more practical, accurate model results.

Steps in data preparation process

The process of preparing data includes the following:

1. Data collection:

Relevant data is gathered from operational systems, data warehouses and other data sources. During this step, data professionals and end users gathering data themselves should confirm that the data is a good fit for the objectives of the planned applications.

2. Data discovery and profiling.

The next step is to explore the collected data to understand what it contains and what needs to be done to prepare it for the intended use. Data profiling helps identify patterns, anomalies, inconsistencies, missing data, and other attributes and issues in data sets, so problems can be addressed.

3. Data cleaning.

In this step, the identified data errors are corrected to create complete and accurate data sets that are ready to be processed and analyzed. For example, faulty data is removed or fixed, missing values are filled in, and inconsistent entries are harmonized. Nevertheless, there are general data cleaning operations that can be performed, such as:

• Using statistics to define normal data and identify outliers.
• Identifying columns that have the same value or no variance and removing them.
• Identifying duplicate rows of data and removing them.
• Marking empty values as missing.
• Imputing missing values using statistics or a learned model

4. Data structuring.

At this point, the data needs to be structured, modelled and organized into a unified format that will meet the requirements of the planned use.

5. Data transformation and enrichment.

In connection with structuring data, it often must be transformed to make it consistent and turn it into usable information. Data enrichment and optimization further enhance data sets as needed to produce the desired business insights.

6. Data validation and publishing.

To complete the preparation process, automated routines are run against the data to validate its consistency, completeness and accuracy. The prepared data is then stored in a data warehouse or other repository and made available for use.

A big benefit of instituting an effective data preparation process is that data scientists and other end users can spend less time finding and structuring data and instead focus more on data mining and data analysis. For example, data preparation can be done more quickly, and prepared data can automatically be fed to users for analyses.

Conclusion

In this article, we have seen what data preparation is and the process of preparing data. We also saw reasons why data preparation is important. Thanks for reading.

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