Without data cleaning, there will be many errors in how the data is analyzed, which will not only slow down decision-making but also lead to false positive results. In this article, we will explain why AI cleaning is important, the characteristics of high-quality data, and methods for AI data cleaning.
1. What is data cleaning?
Data cleaning is the process of removing irrelevant or inaccurate data and preparing data for analysis.
This is data that can negatively impact models and algorithms by reinforcing erroneous concepts.
Data cleaning involves not only removing large amounts of unnecessary data, but also correcting inaccurate information and reducing duplication in training, validation, and test datasets.
2. Why data cleaning is important for AI
AI cleaning is important for artificial intelligence to ensure accuracy and quality control. If the datasets you use for analysis aren’t clean, you’ll get inaccurate results that are costly to correct.
For example, if you’re trying to decide what parts of your product need improvement, one of the factors you might use is data on how well your sales team has performed over time, but if you’re trying to decide which parts of your product need to be improved, you might If there is insufficient data, it is possible to make incorrect decisions.
Data cleaning should always be part of your data preparation process. That’s because, without data cleaning, your analysis can be fraught with errors, wasting your time and money. However, by using data cleaning tools and techniques, you can avoid these problems and make the best decisions quickly.
As we have said, data cleaning is an important and necessary part of AI to ensure that conclusions are accurate.
AI cleaning has various benefits. Prediction accuracy is one of the most important things. When your data is clean, you can be confident that the machine’s predictions are accurate given your input.
This is especially important in industries such as medicine and science, where inaccurate data can have disastrous consequences. Data cleaning can also improve the efficiency of your analysis by removing irrelevant data values. This will save you money and time in the long run.
Additionally, data cleaning provides a clearer picture of the research. Clean data eliminates data errors and irrelevant information, allowing you to focus on the most important aspects of your research. This is especially important when working with large datasets or using machine learning algorithms. Because irrelevant data leads to inaccurate and misleading predictions.
3. Five characteristics of high-quality data
Data typically has five characteristics to assess its quality:
- validity
- Accuracy
- completeness
- Consistency
- uniformity
Beyond these basic characteristics, data scientists and data engineers use a variety of specific methods to ensure data quality.
validity
Data collection often involves collecting digitally stored documents containing various information about large numbers of people, such as names, phone numbers, addresses, and dates of birth.
Modern data collection methods allow for control over data submitted in digital documents and forms, so validity is considered a property that is easy to maintain.
Below are typical constraints used on forms and documents to ensure data validity.
- Data type constraints: Data type constraints help prevent inconsistencies caused by entering the wrong data type in the wrong field. This constraint is common for fields where the original data consists only of alphanumeric characters or numbers, such as age, phone number, or full name.
- Range Constraints: Range constraints are used in fields where there is already prior information about the available data. For example, date, age, height, etc.
- Unique Constraint: A unique constraint is a constraint that updates automatically every time a participant enters data in a document or form. This type of constraint prevents many participants from entering the same information for parameters that are supposed to be unique. It is often enabled for fields such as username, social security number, and passport number.
- Foreign Key Constraints: Foreign key constraints are useful for fields where the data is restricted to a predetermined set of keys. These fields are often country or state fields, making it easier to understand the range of data that can be provided. Often country or state categories facilitate the diversity of information that can be provided.
- Cross-field validation: Cross-field validation is not a constraint, but a check that ensures that multiple fields in a document correspond to each other. For example, if a participant enters a specific number or amount, that amount acts as a validator that prevents the participant from entering the wrong value.
Accuracy
Accuracy refers to how much of the collected data is feasible and accurate. Because the data contains personal information that only the participants themselves know, it is almost impossible to guarantee complete accuracy. However, observing the feasibility of that data roughly guarantees its accuracy.
For example, location data can be easily matched to see if the location exists or if the postal code matches the location. Similarly, feasibility can also be a solid criterion. For example, no human being is 100 feet tall and weighs 1,000 pounds.
completeness
Completeness refers to the extent to which the entered data exists as a whole.
Missing fields or values cannot be corrected and will result in the deletion of entire rows of data. However, the presence of incomplete data can be properly corrected with the help of appropriate constraints that prevent participants from filling in incomplete information or omitting certain fields.
Consistency
Consistency is how data reacts when compared to other disciplines. Studies are often conducted in which the same participants complete multiple questionnaires and cross-check their consistency. It also involves cross-checking multiple areas by the same participant.
uniformity
Ensuring uniformity of quality data is important for various applications such as data analysis, machine learning, and decision-making processes. Uniform quality data means that the data is collected and recorded consistently and has the same level of accuracy and completeness across all data points. This makes data easier to compare and analyze, reducing errors and inconsistencies that can lead to incorrect conclusions and decision-making. To ensure uniformity of quality data, organizations and individuals must establish clear guidelines and protocols for data collection, recording, and management. Data is regularly reviewed for accuracy, completeness, and consistency, and any problems or discrepancies are promptly addressed. Additionally, using automated tools and techniques such as data quality software can help maintain data quality over time.
4. AI data cleaning method
Step 1: Remove duplicate data
Data duplication frequently occurs during the data collection stage. This typically occurs when you combine data from multiple sources, or when you receive data from a client or multiple departments. All instances of duplicate data should be removed.
You should also remove irrelevant data from your dataset. This is when your data doesn’t fit the problem you’re trying to solve. This allows for efficient analysis.
Step 2: Filter outliers
An outlier is an unusual value in a dataset. Because it is so different from other data points, it can skew your analysis or contradict your assumptions. Removing outliers is a subjective task and depends on what you are trying to analyze. In general, you can improve the performance of your data by removing unnecessary outliers.
You can exclude outliers in the following cases:
- If you know for sure that something is wrong: For example, if you have a good understanding of what range your data should fall within, you can safely remove values outside that range.
- When it is possible to collect data retrospectively: or when it is possible to verify questionable data points.
One thing to keep in mind here is that just because there are outliers doesn’t mean they’re wrong. Sometimes outliers can help prove the theory you’re working on. In such cases, choose to keep the outlier.
Step 3: Fix structural errors
Examples of structural errors include strange naming conventions, typos, and incorrect capitalization. Anything inconsistent will result in a mislabeled category.
A good example of this is when both “N/A” and “Not Applicable” are present. Both should be analyzed as the same category, but they are displayed as separate categories.
Step 4: Fix missing data
Any missing data must be filled in.
Many algorithms do not accept missing values, so you must either remove them or fill in the missing values based on other observations.
Step 5: Validate the data
After properly preparing your data, validate it by answering the following questions:
- Does the data make complete sense?
- Does the data follow the relevant rules for that category or class?
- Does the data prove or disprove your theory?
summary
Data cleaning is an important and necessary process in AI that helps ensure the accuracy of machine predictions. This increases the value of your predictions and provides more reliable conclusions. This is especially important in fields such as medicine and science, where low-quality data can have dangerous consequences. Another benefit of AI cleaning is that it can provide a more complete picture of your research by removing irrelevant information.
