Data has become one of the most important assets for businesses across all types of industries. Whether you are into IT, finance, e-commerce, retail, education, hotel management, or any other business, data has become absolutely crucial in understanding how the business is performing, what are the areas of improvement, and how can they scale their business. Today, organizations have several sources from which they can acquire the required data. Some of these sources include internal and external surveys, customer service interactions, social media platforms, etc. However, businesses cannot directly use all the raw data. It needs to be preprocessed before it can be fed into the various systems and analytics programs for use.
In the majority of the cases, the raw data may be incomplete, noisy, and unreliable. Also, with the importance of data growing at an alarming rate, there is a higher risk of gathering incorrect and unreliable data. Another aspect to remember is that these low-quality data will not be able to provide you with accurate details and predictions. Hence, there are some key steps that you need to take in order to make your data ready for use. Whether you are using data preprocessing in machine learning or data preprocessing in data analytics, you must ensure that your input is high-quality data.
In this post, find out all about data preprocessing, why is it important, what are the key characteristics of quality data, the key steps and techniques of data preprocessing, and more.
Table of Contents
Data Preprocessing – Definition
Data Preprocessing can be defined as the process which helps in converting all the raw data into useful and usable data. As mentioned earlier, the initial raw data that is collected from different sources is not of high quality and the majority of the data is often inconsistent, noisy, incomplete, and unreliable. With the help of data preprocessing, all these issues can be resolved, making the data more efficient for use in data analysis and machine learning. When data is used in various machines and systems, it is vital to ensure that the information is clean and structured with a uniform design.
Machines usually read data as 1s and 0s which is why it is easy for them to calculate and analyze data like numbers and percentages, whereas data which are in the form of images and text need to be formatted to make them readable by the machines. When the raw data undergoes data preprocessing, it increases the success of the various machine learning projects and ensures better performance of the AI and machine learning models.
Below are some of the various tools and methods used for data preprocessing.
- Sampling involves selecting a particular subset from the overall data available
- The transformation which transforms the raw data so that it produces a single input
- Denoising is the process of removing all the ‘noise’ from the data
- Imputation which helps in synthesizing data for any missing value
- Normalization is the process of organizing the data in an efficient manner
- Feature extraction is the process of picking up a feature subset that is relevant and significant in a specific framework.
These tools and methods can be applied to different types of data pulled in from various sources.
Quality Data Characteristics
Whether you are looking for data preprocessing in machine learning or data preprocessing in data analytics, one of the key factors that you need to focus on is the quality of the data. The success of your machine learning models has a high dependency on how relevance and comprehensiveness of the training data. Here are some of the top characteristics of quality data.
- Accuracy refers to how accurate the data is. Having outdated, redundant, or data with typos is not considered good quality data.
- Consistency is highly important as having inconsistent data will result in different results for the same questions.
- Completeness of the data is another key characteristic of good, quality data as it ensures that accurate analyses are performed. Having incomplete information or missing values will affect the accuracy of the results.
- Validity of the data refers to the format of the data, whether it is within the specified range, and whether it is the right type or not. Working with invalid data is difficult as they are unorganized and hard to analyze.
- Timeliness also plays a crucial role which is why you need to collect data as soon as the event it is related to takes place. Over a period of time, the dataset becomes less accurate, thus, making it less useful.
Importance of Data Preprocessing
The importance of data preprocessing cannot be stressed enough. From AI development to data analysis to data scientists, all these require data preprocessing in order to provide accurate and strong results. As mentioned earlier, raw data is often messy, noisy, and inconsistent since they are mostly collected by humans, various applications, and business processes. It is not uncommon to find several inconsistencies in these raw data in the form of manual input errors, missing values, duplicate data, multiple names for the same thing, and more. When humans work with these raw data, they are capable of identifying the inconsistencies; however, machine learning models and deep learning algorithms need the data to be preprocessed before use.
When it comes to using various data sets, there is a popular phrase – ‘garbage in, garbage out. It simply means that if your input data is bad, your output data will also be the same, thus, making your data analysis irrelevant. In fact, not using good, preprocessed data in your machine learning algorithms is a sure sign of ‘garbage’ results. Depending upon the various tools and methods you have used for your data gathering and the sources, it is possible that your raw data includes data that is out of the required range, includes incorrect features, missing fields, incorrect images, irrelevant symbols, typos in URL, etc. All these inconsistencies can greatly affect the way your data is analyzed and its subsequent results. This is why it is extremely vital to have your raw data preprocessed so that the data is easier to understand and interpret.
To summarize, data preprocessing is important in order to ensure accurate, complete, and consistent data analysis results.
Data Preprocessing – Key Steps
Below are the five key steps involved in data preprocessing.
1. Data Quality Assessment
Data quality assessment or data profiling refers to the process of reviewing and analyzing the data to understand its quality. The process begins by examining the data and its various characteristics. By assessing the data, you can understand not only its quality but also how relevant and consistent it is for your project. There are several anomalies that you need to look out for, such as mismatched data types where you have the same data type in different formats from different sources, mixed data values where different sources use different descriptors for the same features, and data outliers, and missing data.
2. Data Cleansing
As the name suggests, data cleansing or data cleaning is the process of cleaning data by checking for missing values, removing outliers, rectifying inconsistent data, removing any bad data, improving the quality of the data, etc. This is considered as one of the key data preprocessing steps as it helps in ensuring that your data is ready for use. The data scientists can use various techniques for data cleansing depending upon their data requirements. Some of the issues resolved in data cleaning include:
- Missing Values – This is one of the most common problems while collecting data and can occur for various reasons like a violation of some specific data rules, merging different datasets to create one big dataset, etc. Some of the common ways to resolve this issue include filling in the missing values manually, using standard values like N/A or Unknown to populate the missing field, using algorithms to predict the most probable value, and using a central tendency for the replacing the missing field. If your dataset has more than 50% of the rows/columns with missing values, it is recommended to discard the dataset unless you can populate them using the methods mentioned above.
- Noisy Data – Noisy data refers to data that contains irrelevant data, unnecessary data, duplicate or semi-duplicate data, unwanted information fields, and data that are difficult to group as one dataset. Some of the common ways to resolve the issue of noise include regression through which you can find the variables with high impact, binning which involves sorting data into smaller segments, and clustering which involves grouping data and removing outliers.
3. Data Integration
Data integration is a crucial step since the raw data is collected from multiple sources. Some of the popular ways to integrate data include data consolidation which involves physically bringing the data to one common place for storage and it also makes use of a data warehouse software, and data visualization which offers a unified interface through which you can have a real-time view of data from different sources, and data propagation which is the process of using specific applications to transfer/copy data from location to another.
4. Data Reduction
Analyzing large amounts of data is quite difficult and this is where data reduction comes into the picture. Data reduction is the process of reducing the amount of data, making it easier to analyze, and also reducing the costs of storing it. Even though this data preprocessing step reduces the amount of the data, it does not hinder the quality of the data in any way. So, if you are working with an extremely large dataset, then it is absolutely vital to use popular techniques, such as dimensionality reduction, feature subset selection, numerosity reduction, etc., for data reduction.
5. Data Transformation
Data transformation is the process of ensuring that your data is in the right format. It uses various techniques to transform the data into ideal formats which can be used by the machine learning models. Unstructured data is quite common in raw data which is why this preprocessing step is important as it helps in structuring the data which will best suit your goals. Some of the key strategies used for data transformation include aggregation, normalization, generalization, smoothing, discretization, feature construction, and concept hierarchy generation.
Data Preprocessing Techniques
Data preprocessing consists of two main categories – data cleansing and feature engineering. Both these categories consist of several techniques as described below.
Under data cleansing, we have the below techniques:
- Identifying and Sorting Missing Data
- Reducing noisy data
- Identifying and removing duplicates
Under feature engineering, we have the below techniques:
- Feature scaling or normalization
- Data reduction
- Discretization
- Feature encoding
Conclusion
When it comes to machine learning models, they are capable of performing advanced functions; however, it all depends upon the data input they receive. If you provide them with high-quality, and accurate data, it will provide you with successful results. In order to ensure the success of your data analysis and machine learning models, you need to ensure that your data goes through the key data preprocessing steps. Check out this list of Data Extraction Software and other similar software on SaaSworthy – your one-stop destination to know about more than 40,000 software across 300 different categories!
