One can imagine the catastrophe of using inaccurate machine learning models in business – accidents, investment losses, and erroneous analysis. However, because the use cases for machine learning algorithms are numerous and can be positively and negatively impactful, a lot relies on the data quality fed into these models.
Before machine learning engineers build machine learning models, the data must undergo a two-step preparation process; data preprocessing and data wrangling. For this article, we’ll look at data wrangling.
Data wrangling refers to the process whereby data undergoes manipulation of raw data into usable and workable formats to make it easier for data scientists and engineers to analyze and build models. It’s no surprise that data scientists and engineers spend over 80% of their time on it. The benefits make the time worth it! That said, the right tools can take care of data wrangling to give that time back for more interesting work.
In this piece, we’ll take a look at data wrangling for machine learning and how data engineers and data scientists can avoid it.
What is Data Wrangling?
Also referred to as data munging, data wrangling is a prerequisite step for machine learning and analytic purposes. It involves reorganizing, mapping, and transforming data from its raw, unstructured form into a more usable format. Data wrangling involves six steps:
Data discovery: Getting familiar with data is the first step. Data engineers and scientists must know the end purpose of the data. Answering and getting acquainted with the data helps inform the subsequent phases of the process.
Data structuring: Data undergoes structuring from the raw, unusable format into a more structured format to fit the intended use case.
Data cleaning: Data cleaning involves algorithms that help remove missing and null values, discard unnecessary and wrong data, giving rise to clean data.
Enrichment of data: This step is optional and occurs when the current data lacks some information and may involve augmenting data from other sources to give rise to a more robust dataset.
Data Validation: This step applies programming checks on data for quality, consistency, accuracy, and authenticity against standardized rules set at the start of the data wrangling process.
Data Publishing: Once the validated data passes checks, data is pushed/published for use in exploratory analysis or reporting.
Data wrangling was born out of need. Because data rarely arrives in neat, usable formats, data and business analysts have had to get used to working with raw, unusable data. Out of that need, they developed ways and processes we now call data wrangling to transform such data into a functional form to draw analysis and identify patterns.
If data analysts are lucky, they have data engineers on staff building ETL pipelines that help pull, transform, and process raw data for them.
Why Data Wrangling Matters in Machine Learning
Data wrangling has become essential for various purposes like data analysis and machine learning.
In cases of analysis and business intelligence operations, data wrangling brings data closer to analysts and data scientists in the following ways:
Data exploration: Data wrangling helps with exploratory data analysis. Data mapping, a crucial part of the data wrangling process, helps establish relationships between data and provides analysts and data scientists with a comprehensive view of their data and how best to use it to draw insights from it.
Grants access to unified, structured, and high-quality data: Data wrangling involves data cleaning and validation, which helps remove noisy data and other unnecessary variables, leading to the production of high-quality data.
Improves data workflows: Automated data wrangling helps create workflows that ensure an organization’s continuous data flow. Data workflows help accelerate analysis and other organizational processes reliant on such data.
For most machine learning processes, data wrangling forms an essential component of data preparation and produces more efficient and accurate machine learning models. Other roles include:
Minimization of data leaks: Data leakage is the most common problem of machine learning projects. It occurs during model building when data outside the training set is used for training the predictive mode, leading to an over optimized and inaccurate machine learning model. If organizations employ this model for business operations, it may present some huge losses for businesses.
Enriches datasets: Data wrangling helps add extra value to existing data sets. Depending on the intended machine model, adding more variables from the existing feature set may help create a robust data set which leads to more efficient ML models.
More efficient use of time: The presence of validated and usable data leaves data scientists more time for analysis and machine modeling.
Data wrangling applied in all processes helps improve the quality of data used for various end-use cases, analysis, or building machine learning models.
What Makes Data Wrangling Different
Data wrangling is confused with other data processes like data cleaning, transformation, and preprocessing, but these terms have significant differences.
Data cleaning (aka data cleansing or data scrubbing) represents a step in the data wrangling process. It involves removing duplicate values and outliers and standardizing measurement units that produce high-quality data for use in analytics or machine learning.
Data preparation for machine learning analysis involves two essential steps: data preprocessing and data wrangling. Data preprocessing occurs first and helps convert raw, unclean data into a usable format. Data preprocessing involves data cleaning, integration, transformation, and reduction. Data wrangling occurs after data preprocessing and is employed when making the machine learning model. It involves cleaning the raw dataset into a format compatible with the machine learning models.
Data transformation is a component of data wrangling and involves tasks like enriching datasets, filling in null values, filtering, or combining data from various sources. In addition, data transformations help convert raw data into compatible formats for a destination system.
An Overview of Data Preparation for ML
When data arrives from different sources, it’s unsuitable and inaccessible and must undergo data preparation for use in machine learning models. Data preparation is essential for ML because specific models only work with particular data. For example, the Random Forest algorithm does not work with null values. Hence, before attempting to create a Random Forest algorithm, the dataset must be manipulated and transformed to rid such values. Two phases exist in the data preparation process:
Data preprocessing: This occurs before building the machine learning model. It is usually performed once and involves combining data sources, aggregating data attributes, normalization, and reduction. Data preprocessing ensures only valid and clean data proceed to the next step. For example, if we intend on using a Random Forest algorithm, null values could be removed or filled manually or automatically in this step.
Data wrangling: This process is carried out during iterative model building to fit the requirements of the machine learning model. Data wrangling helps train and implement better machine learning models.
How Data Wrangling Fits into the ML Data Preparation Process
Data wrangling leads to the production of more efficient machine learning models. In machine learning, the first built model is rarely the best, as data scientists and ML engineers usually revisit the data wrangling process of data preparation and make slight adjustments. This process is an iterative one, and wrangling may occur severally during the design of a model until engineers arrive at a satisfactory and accurate model that fits their use case. Data wrangling here may involve:
The removal of data irrelevant to the analysis.
Creation of a new column by aggregation
Using feature extraction to create a new column, for example identifying sex by extracting prefixes for names like Mr and Miss.
The Importance of Both Data Wrangling and Data Engineering
The quality of insights is highly dependent on the data used for analysis. Data engineering represents systems design that helps build pipelines that collect, store, and analyze enormous datasets for various purposes. A common practice in most data engineering processes is data wrangling which helps ensure the use of high-quality data for operations. Here are some importance of data wrangling and data engineering:
Better data consistency: Most data sources usually involve data from human-inputted sources like user entries and social media. Data wrangling helps organize, clean, and transform these data into a consistent valuable format for making accurate business decisions.
Cost-efficient machine learning processes: With data wrangling in the machine learning preparation process, engineers can build more accurate models, which helps minimize business costs in the long run. For instance, using a dataset containing low-quality data riddled with invalid data creates a lousy model that can be costly to overturn once used for business decisions.
Trusted business insights: Employing data engineering practices like data wrangling ensures the use of quality data for identifying trends and insights.
Better audience targeting: Data culled and organized from various sources gives organizations a clearer picture of their audiences, making it easier to create targeted business ads and campaigns.
In this piece, we discussed how data wrangling is an essential component of data science work and machine learning model development. The two needed processes are data pre-processing and data wrangling. StreamSets helps data scientists get better self-service access to the data they need in the format they need it, so they can spend less of their time data wrangling.
StreamSets smart data pipelines have many supported in-pipeline transformation steps that can ensure that data lands in the format that data scientists and analysts need. Users can design with a fully graphical interface allowing for collaboration between data engineers and data scientists.