AI Data Preprocessing: Why It Matters and How It Works
Data preprocessing means creating a high‑quality, consistent and structured dataset that AI (artificial intelligence) and ML (machine learning) can learn from.
It does this by taking raw, noisy and inconsistent data from different sources and transforming it into a form that AI algorithms can safely use through error correction, standardized formatting, and numerical conversion of all relevant data types.
Most AI and ML teams spend the majority of their time and energy on data preprocessing relative to the other stages of the project: collecting, preparing, and training. It's imperative for higher levels of model accuracy and greater consistency in model behavior. This results in fewer surprises once models are deployed into production.
The Importance of Data Preprocessing for AI Models
AI models are very susceptible to the type of data they receive during their development. If the quality of data is poor, including inconsistent, biased, and noisy data, it will be immediately accepted by the model and repeated in production.
Clean and consistent data: Through quality data preprocessing, you will increase the likelihood of cleaner, consistent, and more representative data of actual scenarios.
Enhanced computing and storage efficiency: This can be done by eliminating duplicated data, reducing the number of dimensions in the dataset, and normalizing the values within it. This will allow your team to train your models significantly faster with smaller, more rigorously honed training datasets compared to other forms of preprocessing.
As important as data preprocessing is, the process presents its own set of challenges that you must know to truly build accurate, reliable datasets.
Challenges with Raw Data
While handling raw data, it’s important to lookout for the following points to ensure a clean dataset:
- The fields containing valuable information often contain missing values (age, geographical location, as well as timestamp).
- Inconsistencies in format (different formats of dates, as well as the units of measurement).
- Outliers resulting from anomalies due to logging errors or sensor errors (uncommon for data points to have extreme values).
- Redundant data (duplicate records for the same occurrence or event).
- Noisy or corrupted values (typos, random symbols, irrelevant content).
- Biased samples (where certain geographic areas, demographic groups, and/or event types are given less or greater weight than others).
Steps in Data Preprocessing
The general process flow for the data preprocessing pipelines is similar across all data types, even though the specific tool(s) used vary significantly between the different data types.
Understand and Profile Your Data
To begin analyzing your raw data, you must first obtain an understanding of it.
Identify the following:
- Each row and column distribution.
- How many types of data were collected.
- What type(s) of data are missing.
- The maximum and minimum ranges for each data entry type.
- The statistical analysis of the raw data, so that the team can determine which data fields will provide a useful service and which data field(s) will require modifications.
The Cleaning Process
Cleaning processes aim to improve the quality of data records.
The data cleaning process includes the following:
- Removing missing records using imputation of reasonable substitutes.
- Deleting unusable records or using imputation based on the model.
- Removing or capping the values of data that are clearly outliers or extremes that do not apply to the use case.
- Data deduplication (removing duplicate records), which provides a means of identifying duplicate users and events.
- Correcting errors in the format of the data field(s). This may involve correcting malformed email addresses or impossible time stamp fields.
Data Transformed to Model Format
Once the raw data has been cleaned, it will need to be transformed into a format that is compatible with a predictive model(s). This transformation may include:
- The normalization or standardization of numeric data types to share similar data ranges.
- Encoding categorical data types (such as countries or types of devices) in numeric formats, through methods such as One-Hot or Ordinal Encoding.
- Aggregating event-level data into user-level or session-level features.
- Feature building to develop additional features that will provide better insight into the history or identified patterns of behavior.
Integration of Data
Combine data from multiple sources, including:
- Databases
- Log files
- Third Party APIs (API)
- (manual) upload of files.
Through preprocessing, the data will be unified, keys aligned, filters applied (where necessary) to end up with a single coherent set of data (with which) a model can learn.
Reducing And Sampling the Amount of Data Collected
Reducing/obtaining data through dimensionality reduction or (expressed) data sampling will improve the efficiency of training the models. Moreover, it will reduce noise created by data. Dimensionality reduction will also preserve the essence of the patterns contained within the data.
The Development of Training, Testing, and Validation of Data sets
An extremely important aspect of preparing data for machine learning models is splitting the processed data sets into three separate data sets: Training, validation, and test data sets.
Splitting the processed data sets will enable the evaluation of how well the model generalizes unseen data. Further, it will help prevent the model from overfitting the training dataset.
What Does This Mean for Professional Data Collection Service Providers?
The ability to develop AI applications using pre-processed data creates significant commercial value for the clients of an organization that collects and processes data.
An organization's ability to not only collect data but also build value from the data collected through increased quality, consistency, and usability for downstream AI systems is important.
The development of an AI team’s pipeline can be streamlined by providing a model or process for preprocessing the raw data into a high-quality, organized dataset: Less data wrangling allows the AI team to concentrate on creating models and experimenting with them.
By starting with a structured set of data, clients will know what to expect. And they will have a less unpredictable outcome than if the data were a mixture of structured and unstructured datasets.
Furthermore, the use of well-structured datasets at the start of the training process allows for privacy and regulatory compliance.
By implementing anonymization and tokenization through the cleaning and transformation process, there is less risk of violating regulations or experiencing loss of privacy when using data for AI initiatives.
Effective data preprocessing is what turns raw information into reliable training fuel for AI. When your data is clean, structured, and consistent, every stage of model development from training to deployment becomes more predictable and more efficient.
When it comes to building AI solutions, investing in strong preprocessing isn’t optional. It's the foundation that determines how well your models will perform in the real world.
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