Generative AI

Data Preparation for AI

Digital interface with icons representing the crucial steps of data preparation for AI, highlighting machine learning and automation.

Artificial Intelligence (AI) techniques such as Machine Learning (ML) can deliver predictions and insights using large volumes of data. Data preparation uses a series of processes to ensure that algorithms and models receive high-quality, clean data to maximize the validity of predictions.

Why is data preparation important for effective AI-driven data analysis?

Bad data leads to bad insights. Decisions based on poor data quality are more likely to result in unintended consequences. Data preparation rectifies data errors and omissions that can lead to skewed insights.

Data preparation processes

The following outlines the primary steps in data preparation for AI.

Data profiling

Profiling data sources for AI provides a deeper understanding of the content and structure of a data set. Data profiling reads a source data set to determine data volume, cardinality, structure, and content. Data integration products such as Actian DataConnect identify duplicate records, bin data values into ranges, and calculate statistics such as Min, Max, Mean, Median, Mode, Standard Deviation, Sum, and Variance for each data field.

Data type unification

Data cleansing looks for field delimiters and reformats each field into a suitable data type for every record.

Data reduction

Source data often contains data fields that are not relevant for a particular analysis. Keeping redundant data can slow down the analysis and consume expensive resources. Data reduction filters out fields that are not needed. If unique records are needed, duplicates will be discarded in this step. In addition, data values that lie outside the expected range are removed in this step.

Data transformation

The primary goal of data transformation is to improve the consistency of the data to avoid tripping up an AI-driven analysis. Currency symbols, decimal places, and the use of leading zeros can be inconsistent. If data contains sensitive information such as credit card numbers, account numbers, or social security numbers, applying a mask can obfuscate these fields to comply with regulatory requirements.

Data correction

A source data set can contain erroneous data that was misread, or that contains an out-of-the-ordinary value. In the data correction step, outlying values are removed or corrected.

Data enrichment

Data records with incomplete or missing values can be added by referring to multiple data sources. Default values or extrapolated values can also fill these gaps. Bucketed fields that map discrete values into ranges can be added. For example, age ranges may make more sense to use than individual ages for analysis and reporting.

Data partitioning

Very large data sets can be split into multiple partitions or shards to enable efficient parallel processing. Each subset of data can use dedicated servers to accelerate analysis. Data can be partitioned using a round-robin scheme where each record is allocated to a list of partitions in a circular order. A key field can be selected to direct a record to a bucket that contains records in that value range. A hashing scheme combining values in 2 or more fields can distribute the data evenly across data partitions.

Data validation

Data validation can improve data quality. In this step, data is checked for anomalies that the data preparation steps failed to identify and fix.

Automation of data preparation for Artificial Intelligence

Data preparation steps can be executed in sequence, known as a data pipeline. Data integration solutions can orchestrate individual data preprocessing steps, handle any retry, and report exceptions to keep operating costs under control.

The benefits of data preparation for AI

Some of the benefits of data preparation for AI include:

  • AI analysis yields more accurate insights and business outcomes when operating with prepared data.
  • Prepared data is of higher quality, benefiting traditional business analytics and machine learning.
  • Data preparation scripts are reusable, lowering the time and effort involved in data analysis projects.
  • Data engineers are more productive after they automate their data preparation processes.

Actian and data preparation

The Actian Data Platform makes it easy to automate data preparation thanks to its built-in data integration technology. Businesses can proactively build data pipelines from their operational data, increasing data quality and making it readily usable by business intelligence (BI), AI, and ML analysis.

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 are centrally managed, lowering administration costs.

The Actian warehouse makes it easier to perform high-speed data analysis 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 analytics wherever your data resides.