Machine Learning (ML) models are highly dependent on suitable data to deliver accurate insights and predictions. The raw data must be preprocessed or prepared using a series of steps to prepare it for Artificial Intelligence (AI) and ML processing.
Why is data preparation important for effective Machine Learning?
Uninformed decision-making hurts a business as time and energy are expended on executing a plan with little chance of success. Machine learning can help make better informed, data-driven decisions. However, machine learning models are only as good as your data. Bad data will skew the predictions the machine learning model produces. Investing in data preparation increases the quality of the data that decision-makers rely on, increasing the probability of a positive outcome.
Data preparation for Machine Learning
The following data preparation processes will improve the data quality used for machine learning.
Data profiling
Understanding source data sets better through data profiling helps to formulate data preparation. Data profiling involves scanning a data source to determine its size, variability, structure, and content. The output from profiling can include identifying duplicate records, binning data values into ranges, and calculating Min, Max, Mean, Median, Mode, Standard Deviation, Sum, and Variance statistics.
Cleansing data
Data profiling will help identify field delimiters, which the data cleansing data process will use to make the data fields and records consistent by standardizing data types and file formats.
Filtering out data
Knowing what questions the data will be used to answer or what correlations the machine learning model is looking for helps determine what data can be discarded to avoid skewing the model. Outlying values and unnecessary data can be removed. Any duplicate records can be deleted.
Transforming data
When data is collected from multiple sources, many fields can be inconsistent. Date formats may vary, number fields can contain currency symbols, and numeric values can differ. Data transformation can correct these inconsistencies. Leading or trailing spaces can be made consistent. Data subject to regulations can be masked or obfuscated to protect customer privacy without impacting the results from the ML model.
Enrichment of data
Data sets can be enriched by adding calculated values, merging related data from multiple sources, and bucketing discrete data values data into ranges. Gaps can also be filled by adding default values, extrapolating, or interpolating field values. Data from internal systems can be combined with external third-party data to add a market context.
Partitioning Machine Learning data
When datasets are too large to be read by a single process, they can be partitioned into sub-sets and placed on different devices for faster ingestion through parallel execution. Partitioning data can be done by hashing values for random distribution or by a key value to distribute slices evenly across partitions.
Data validation
Data validation is often the final step in data preparation and is used to assess the data quality.
Automation of data preparation for Machine Learning
The steps of the data preparation process can be chained into a data pipeline process using a data integration solution that can orchestrate and schedule the individual data preprocessing steps.
The benefits of data preparation for Machine Learning
Some of the benefits of data preprocessing include the following:
- Preprocessed data yields better results from machine learning models.
- Prepared data is better able to support traditional business analytics.
- ML training models can reuse existing data pipelines for faster data preparation.
- Preprocessed data results in improved outcomes that increase agility and competitiveness.
- Preprocessed data is of higher quality, making it more authoritative and trusted.
- Data engineers are more productive as model training times are reduced.
Actian and data preparation
The Actian Data Platform makes it easy to automate data preprocessing using its built-in data integration capabilities. Businesses can proactively preprocess their operational data to be analysis-ready using pipeline automation. Organizations can get full value from their available data assets by making it easy to unify, transform, and orchestrate data pipelines.
Actian DataConnect provides an intelligent, low-code integration platform to address complex use cases with automated, intuitive, and reusable integrations. DataConnect includes a graphical studio for visually designing data pipelines, mapping data fields and data transformations. Data preparation pipelines can be centrally managed, lowering administration costs.
The Actian Vector database makes it easier to analyze high-speed data due to its columnar storage capability that minimizes the need for pre-existing data indexes. Vector supports user-defined functions that can host machine-learning algorithms. Vector processing speeds queries by exploiting multiple CPU caches from a single instruction.
The Actian Data Platform runs on-premises and multiple cloud platforms, including AWS, Azure, and Google Cloud, so you can run your analytics wherever your data resides.