What is data cleaning and data transformation?

In this article, we will look at the data preparation steps – data profiling, data source exploration, data cleaning, data transformation.

Creating and consuming data is becoming a way of life. According to a report by IBM, the world produced approximately 2.5 quintillion bytes of data per day in 2017. Most of this data is stored on the internet, making it the largest database on earth. Google, Amazon, Microsoft, and Facebook together store 1,200 petabytes of data (1.2 million terabytes).

But on the other hand, using data comes with risks. The MIT Sloan Management review reports that financial losses due to incorrect and poor-quality data amount to 15% to 25% of a company’s revenue. And according to a 2018 IDC Business Analytical Solutions survey, data scientists spend 73% of their time preparing data for activities such as analytics and forecasting.

To avoid losing time, market share, and potential customers, companies are looking to use data analytics to grow their bottom line and need to have a good understanding of the concepts of data cleaning and transformation.

Often, web scraping produces large  recent mobile phone number data amounts of dirty and unorganized data. Web data integration (WDI) focuses on data quality and control. WDI has built-in Excel-like transformation functions that allow you to normalize data right in your web application. It enables you to extract, prepare, and integrate data in the same environment. This way, you can use your data with a high level of trust and confidence.

What to do before cleaning and transforming data?

Often, analysts want to move on to data cleansing without completing some important tasks. The steps listed below help prepare raw data for transformation, which in turn helps the analyst identify all data elements (but only those elements that he will work with later):

1. Defining business objectives

Knowing your business goals is the first step to properly transforming your data. Well-defined business objectives ensure alignment with corporate strategy, describe customer problems that need to be solved, include new or updated business processes, anticipated costs, and projected return on investment. All of these parameters help determine what data is needed and what is not needed for analysis.

2. Research the data source

A well-developed data model describes possible data sources, such as websites and web pages, to populate the model. Specifically, careful consideration of data sources includes:

• Defining the data needed for business tasks
• Defining what exactly your colleagues expect to see when collecting web data
• Cataloguing possible data sources and data managers
• Understanding the delivery mechanism and frequency of data updates from the source

The value of web data can also increase over time, and it can then be used to analyze time series and trends in the data. This improves your decision-making process and gives you a deeper understanding of how important events, such as celebrity endorsements and testimonials or sales, impact your business.

3. Data profiling

This step is an actual familiarization with the data before it is transformed. Profiling identifies data structure, null records, unwanted data, and potential quality issues. A thorough review of the data can help determine whether a particular source is suitable for further transformation, potential data quality issues, and the number of transformations required for analytics.

The process of defining business objectives, researching the data source, and searching and profiling sources performs an important function of filtering data sources. All these steps will help organize the processing work, and subsequently make this data suitable for use. The next step is data cleaning.

Data Clearing

Only after assessing and profiling the sources can we start cleaning the data. In general, all applications for cleaning, transforming, profiling, discovering data should be considered from the point of view of the data that is collected on the Internet. Each website should be considered as a data source, and we use the terminology from this point of view, we do not consider the traditional ETL (Extract, Transform, Load) approach, managing enterprise data from traditional sources.

General data cleansing guidelines may include (but are not limited to) the following steps:

Pre-cleaning of data ensures accuracy and consistency of data for subsequent processes and analytics, which in turn will increase customer confidence in the data. Idatica assists with data cleaning upon customer request, preparing extracted data by examining, assessing and refining data quality. We also perform data cleaning, normalization and enrichment of data using over 100 spreadsheet functions and formulas.

Data Transformation / Data Manipulation

Data transformation / Data manipulation (from English “data wrangling”, “data munging”) is the practice of transforming raw data into a regular model for a specific business task for subsequent work on them.

This process includes two key components of the web data integration process – data extraction and data preparation. Extraction includes CSS rendering, JavaScript processing, network traffic interpretation, etc. Preparation, in turn, harmonizes the data and ensures quality.

Below are some good practices for data transformation:

The large amount, type, and speed of data available today is a huge opportunity for businesses to improve their revenue, market share, competitive position, and customer relationships. However, a lack of attention to data cleansing or quality can result in bad data, bad decisions, and loss of trust. Thus, the value of traditional web scraping in this regard remains somewhat on the sidelines.