Data preparation

Data preparation: definition and importance for your business

Data preparation is an essential process in virtually every organisation that works with data. It is a fundamental step in data analysis and is often seen as a necessary preparatory measure before applying complex algorithms or data models.

What is data preparation?

Data preparation is the process of cleaning, converting and organising raw data into a format that can be used efficiently. The primary purpose of data preparation is to avoid errors and ensure accurate results in data analysis. This process involves several phases, including data cleansing, data integration, data transformation, data reduction and data compression.

Why is data preparation important?

Effective data preparation enables companies to make informed decisions based on accurate data. Incorrect or incomplete data can lead to misinterpretations and decisions that can negatively impact a company's performance.

The phases of data preparation

1. Data cleansing: In this phase, incomplete, incorrect, poorly formatted or duplicate data is revised or removed.

2. Data integration: Here, data from different sources is merged into a coherent data set.

3. Data transformation: During transformation, data is converted into a suitable format to meet the requirements of the analysis algorithms.

4. Data reduction: Data compression techniques are used to reduce the amount of data required for analysis without compromising the value of the results.

5. Data compression: Finally, the data is further compressed to reduce storage costs and increase analysis speed.

Data preparation tools

There are a variety of tools that can help companies with data preparation. Some of the most popular are Trifacta, Talend, Microsoft Power Query and Informatica. These tools simplify preparation by allowing users to combine, transform and clean data from different sources.

Conclusion

Data preparation is an essential tool for any data-driven company. Effective data preparation enables companies to ensure that they are providing high-quality data for their analyses and thus making better decisions. Without it, many companies would be working with unreliable data, which could lead to incorrect insights and suboptimal decisions. Therefore, every company that relies on data should ensure that it has a robust data preparation process in place.