Data cleaning is the process of identifying and removing errors from data. Learn more about this vital part of data analysis and preparation.
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Last Updated: June 18, 2024 Published Date: June 3, 2024Table of contents
Data cleaning is an essential step in business intelligence and data analysis because it validates accurate and reliable data. The accuracy of the data is vital to generate relevant information before being used in a data analysis or business intelligence (BI) process. The processed data helps businesses make informed data-driven decisions and improve business operations. Using unvalidated data can lead to inaccurate information that leads to misinformation, which can facilitate bad business decisions and faulty changes to existing processes. Read more: Business Intelligence vs. Data Analytics
Data cleaning is a necessary step that must occur before the data is executed in a data analysis process or business intelligence operation. Data cleaning involves looking for erroneous, inaccurate, or incomplete data that needs to be removed, corrected, or updated. Data cleaning consists of using AI tools and a manual review conducted by specific personnel to remove different types of incorrect or missing before any data can be processed in a business intelligence or data analysis process. Read more: What is Data Analysis? A Guide to the Data Analysis Process
Using bad or poor data in a BI or data analysis process can lead to incorrect analysis, business operation errors, and bad business strategies. Addressing bad data before it’s executed in a data analysis process saves businesses money by reducing the expense of fixing bad data results after the data is processed, including the added cost of interrupting business operations to correct the results of bad data. The cost of fixing poor data increases if it is not corrected in the data cleaning process. Cleaning bad data in the data cleaning process costs approximately one dollar. The cost increases tenfold if not corrected in the data cleaning phase, and if the bad data is processed and used, the cost of correcting a problem resulting from bad data increases to $100. Data can be improperly formatted, contain spelling errors, duplicate records, missing values, integration errors, or outlier information that skews data. These types of data errors must be cleaned through a data cleansing process before data analysis processing. The emerging role of artificial intelligence (AI) and automation tools contribute significantly to identifying and correcting various errors in the data cleaning process, which enhances its overall efficiency. Read more: Best Data Quality Software Guide
Data cleaning or washing is a critical step in the data processing phase because it boosts data consistency, correctness, and usability, making the data valuable after analysis. Ensuring the data is thoroughly cleaned can be challenging for businesses due to the varying formats and standards used. Data can come from different sources, which can be problematic in the data cleansing process. For example, Lexical, grammatical, and misspelling errors can be challenging for businesses to correct, even when using advanced AI tools. Additionally, when integrity constraints are not applied to a data column in a table, the column can accept any value. Embedded analytics data from an application populates a database table, providing the latest information for business uses without the need for querying. However, if an embedded value is populating a data column with no integrity constraints, then the software application populating the data column could populate the data column with incorrect information. This is possible if a software application is updated and the embedded analytics data is incorrectly modified, sending erroneous data to the data column. Outdated data that is not routinely updated can damage a business’s financials or reputation. Data quality issues can cause a company to lose up to 20% of its expected revenues. Without proper data hygiene, the saved data can contain misspellings, punctuation errors, improper parsing, and duplicate records. A lack of standardized naming conventions can also cause a business to lose expected revenues. To combat these data challenges, companies must continuously clean collected data to maintain data integrity and accuracy. Read more: Common Data Quality Issues & How to Solve Them
Machine learning is the primary AI tool for identifying and correcting errors in a dataset. The ML algorithm can handle missing or inconsistent data, remove duplicates, and address outlier data saved in the dataset, provided it has learned to identify these errors during the ML algorithm testing phase by using either the supervised, unsupervised, or reinforcement learning process. The popularity of AI tools makes the data cleaning process more efficient, allowing businesses to focus on other aspects of the data analysis process.
Data washing or cleaning has changed dramatically with the availability of AI tools. The traditional data cleansing method uses an interactive system like a spreadsheet that requires users to define rules and create specific algorithms to enforce the rules. The second method uses a systematic approach to remove duplicate data and data anomalies, ending in a human validation check.
With the challenges of cleaning big data, these traditional methods are impractical. Today, businesses use Extract, Transform, and Load (ETL) tools that extract data from one source and transform it into another form. The transformation step is the data cleaning process that removes errors and inconsistencies and detects missing information. After the transformation process is completed, the data is moved into a target dataset.
The ETL process cleans the data using association rules, which are if-then statements, statistical methods for error detection and established pattern-based data. With the emergence of AI tools, businesses save time with better results, though a human is still required to review the cleansed data.
Artificial Intelligence helps data cleaning by automating and speeding up the data cleansing process. Machine Learning (ML) is a subfield of AI. The ML algorithm uses computational methods to learn from the datasets it processes, and the ML algorithm will gradually improve its performance as it processes more sample datasets presented to the ML algorithm. The more sample data the ML code is exposed to, the better it becomes at identifying anomalies.
The ML algorithm uses supervised learning, which trains the algorithm based on sample input and output datasets labeled by humans. The second option is unsupervised learning, which allows the algorithm to find structure as it processes input datasets without human intervention. Reinforcement learning (RL) is another ML algorithm technique that uses trial and error to teach ML how to make decisions. Machine learning builds a model from sample data that allows the ML algorithm to automate decision-making based on the inputted dataset processed.
After ML algorithms have learned from sample datasets, the algorithm can correct the data using data imputation or interpolation methods to fill in missing values or labels. Imputation replaces missing data with an estimated value, and interpolation estimates the value of a data column by using a statistical method involving the values of other variables to guess the missing values. Both methods are used in ML to substitute missing values in a dataset. Data deduplication and consolidation methods are used to eliminate redundant data in a dataset.
Natural Language Processing (NLP) is another subfield of AI. It analyzes text and speech data. This AI tool can be used on text documents, speech transcripts, social media posts, and customer reviews. Natural Language Processing can extract data using an NLP model that can summarize a text, auto-correct a document, or be used as a virtual assistant.
In addition to the available AI tools used in BI and data analysis, mathematical and statistical equations complement the AI tools. These equations verify the AI results fall within an expected standard deviation. For example, numeric values that fall outside the expected standard deviation can be considered outliers and excluded from the dataset.
Though manual data cleaning processes are still required, they are minimized. Manual data cleaning is needed when a business wants the data to be at least 98% accurate. The manual data cleaning effort focuses on correcting typos, standardizing formats, and removing outdated or duplicate data from the dataset. In business industries like healthcare or finance, manual data cleaning can enhance patient safety or help financial institutions minimize compliance risks. Manual data washing is essential when every record matters, and you want your dataset or database to be as perfect as possible.
A convenient method for ensuring data columns or fields contain valid data is to implement integrity constraints on the database table’s data column that the user must adhere to before the data is saved in a field. The integrity constraint is a set of rules for each data column that ensures the quality of information entered in a database is correct. The constraints include numeric values, alpha characters, a date format, or a field that must be a specific length before the data is saved in the field or data column. However, misspellings can be challenging to identify.
The integrity constraints will minimize some errors found during the data cleansing phase. A quality check performed by a human can validate correct spelling, outdated information, or outlier data still in the database. Quality checks can be routine or done before the data cleaning process occurs.
Data profiling analyzes, examines, and summarizes information about source data to provide an understanding of the data structure, its interrelationships with other data, and data quality issues. This helps companies maintain data quality, reduce errors, and focus on recurring problematic data issues. The summary overview that data profiling provides is an initial step in formulating a data governance framework.
Database normalization is a database design principle that helps you create database tables that are structurally organized to avoid redundancy and maintain the integrity of the database. A well-designed database will contain primary and foreign keys. The primary key is a unique value in a database table. A foreign key is a data column or field associated with a primary key in another table for cross-referencing the two tables.
A well-designed database table will be normalized to first (1NF), second (2NF), and third (3NF) normal forms. There are four, five, and six normal forms, but the third normal form is the furthest we will explore. The first normal form removes data redundancy from the database table.
Figure 1 contains redundant data, so the database table is not normalized to the 1 st NF.
| Stud_ID | L_name | Major | Professor | Office_No |
| 1 | Jones | Info Sys | Perry | 2233 |
| 2 | Smith | Info Sys | Perry | 2233 |
| 3 | Thomas | Info Sys | Perry | 2233 |
| 4 | Hill | Info Sys | Perry | 2233 |
| 5 | Dunes | Info Sys | Perry | 2233 |
Unnormalized database tables cause insertion, deletion, and update anomalies. The insert anomaly will continually populate the table with unnecessary redundant data and overpopulate the database. The deletion anomaly can possibly unnecessarily delete the professor’s information if all the student information is removed. A related database table is lost when student data is deleted, and the database is not normalized to the 1st NF.
The last issue is an update anomaly. If another professor replaces Professor Perry, every record will be updated with the new professor’s information. Data redundancy requires extra space if not normalized, including the problems we just covered with insertions and deletions. To solve this problem, we must create two database tables, as shown in Figure 2.
The primary key is in a red font, and the foreign key uses a green font. The two database tables are now connected with the primary and foreign keys, and any professor information that changes will only require updating the professor table. These two databases are now considered to be in the first normal form.
