Good data preparation gives efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. In a sense, data preparation is like washing freshly picked vegetables so long as unwanted elements such as imperfections or dirt are removed. Taking another example, we know that the process of chopping onions to a more satisfactory state will allow for flavors to spread more quickly than would be the case if we were to drop a whole onion into the saucepot. Likewise, transforming data in the data preparation phase is getting data into a state in which it will be easier to work with. To work effectively with data, it must be prepared to address missing or invalid values and remove duplicates towards ensuring that everything is formatted correctly.
What is data preparation?
In simple terms, data collection can be termed as collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis. In more technical terms, it can be termed as the process of gathering, combining, structuring, and organizing data to be used in business intelligence (BI), analytics, and data visualization applications. Data preparation is also referred to as data prep.
Importance of data preparation
Fix errors quickly; it helps catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.
Steps in the data preparation process
The data preparation process starts with finding the correct data. This can come from an existent data catalog or can be added ad-hoc.
Data discovery and profiling
This involves exploring the collected data to understand better what it contains and what needs to be done to prepare it for the planned uses. Data profiling helps recognize patterns, inconsistencies, anomalies, missing data, and other attributes and data sets issues so problems can be addressed.90
Data munging/ data wrangling/data pre-processing
Transforming data is updating the format or value entries to reach a well-defined outcome or make the data simply understood by a wider audience. This has two broad categories;
Data validation and publishing
To finish the preparation process, automated routines are run against the data to validate its consistency, completeness, and accuracy.
Once prepared, the information can be stored or put into a third-party application such as a business intelligence tool, clearing the way for processing and analysis to take place.
Data preparation is a time-consuming activity that can pull you away from more high-value work, especially as the volume of data used in analytics applications continues to grow. However, various software vendors have introduced self-service data preparation tools that automate data preparation methods, enabling users to discover, access, profile, cleanse and transform data in a streamlined and interactive way.