ETL Data Preprocessing and Normalization: 4 Steps to Cleaner, Better Data
In my last blog entry, I highlighted that the analysis of data in its cleanest, most standardized form is a pinnacle in the performance and growth of a business. In this blog entry, I would like to talk about some of the steps you can take to achieve the cleanest form of data.
Real-world data, or data in its earliest stages, can be very dirty. It can be incomplete, inconsistent and filled with errors. One of the most successful ways to safeguard concise data for data analysis is through data preprocessing. Data preprocessing comprises four efficient techniques that, if used correctly, result in cleanly transformed data.
Data preprocessing techniques are as follows:
1. Data cleaning – Eliminates noise and resolves inconsistencies in the data.
2.Data integration – Migrates data from various different sources into one coherent source, such as a data warehouse.
3.Data transformation – Standardizes or normalizes any form of data.
4.Data reduction – Reduces the size of the data by aggregating it.
All these techniques can work together or individually to create a strong data set. I think a huge part of data preprocessing is the transformation aspect. When dealing with raw data, you never know what you’re going to get. Therefore, transforming data through normalization is one of the quickest and most efficient ways to achieve your end goal – clean, useable data.
The rise of ETL
Over the past few years, extract transform and load (ETL) has quickly become one of the most efficient ways to migrate small and large data sets from sources into a data warehouse. Businesses are quickly adopting this process so they can query their data. With ETL, users can migrate heavy loads of data that come from a variety of different source systems. For example, if I want to look at customer data, depending on the design of the data warehouse, I can utilize a single query to derive the customer’s personal information, purchase and order history, and billing information. All this is very useful when trying to track an order, but the processes behind delivering this transformed and standardized data are also vital to the ETL process.
The complete ETL process is very thorough and encompasses a variety of data-standardizing capabilities. In addition, although this process may exclusively deliver clean data, combining this process with normalization will further guarantee the quality of the data.
What is normalization?
Normalization is a technique that is applied to a data set to reduce its redundancy. The primary goal of this technique is to associate similar forms of the same data into a single form of data. It is, in a way, taking specific data such as “Ste.,” “Suite,” “Number,” “No.” or “#” and normalizing it to “Suite” in all instances.
How does normalization work?
The technique can be used in two ways. The first takes similar data and classifies it into its “first normal form,” “second normal form” and “third normal form,” with the first normal form being the tightest association of data form and the other remaining forms being less closely associated.
The other way to use normalization is by taking an attribute from a data set and scaling it down to a small, specified range, such as 1.0 to 1.0 or 0.0 to 1.0. Although this can be achieved in many different ways, I would like to highlight the following three:
3.Normalized by decimal scaling
Since ETL tools, such as Informatica, already possess most of the data preprocessing techniques mentioned earlier, such as data migration and data transformation, it makes following these data cleansing practices much more convenient. Furthermore, such ETL tools allow users to specify the types of transformations they want to perform with their data. These tools also give users a graphic user interface in which they can either write custom code or use the prebuilt aggregate functions.
Data preprocessing through normalization in conjunction with ETL is the most precise way to obtain clean and fast data that would be the most useful for analytics.