How to transform any data to trustable data using AI/ML based data preparation?

September 18, 2020 | Data Curation

How to transform any data to trustable data using AI/ML based data preparation?


Trustable data is data that comes from specific and trusted sources and is used according to its intended use, delivered in the format and time frames appropriate to the specific users. Trustable data helps in good decision-making processes. The properties mentioned in the definition above make data trustworthy for good decision making.

Know more about trustable data and its importance

What is data preparation?

Data preparation on the other hand is the process of transforming as well as cleaning raw data before processing and analysis.

The process of data preparation aims at enriching data by formatting it, making corrections, and standardizing data formats.

A good data preparation process allows the data scientist to analyze it efficiently, eliminate errors, and inaccuracies that may occur during data processing and analysis.

Why is data preparation important?

Most artificial intelligence and machine learning algorithms require their data formatted in a very specific way. This means datasets generally require considerable preparation before yielding a useful purpose. Some of the datasets contain values that are inconsistent, missing, invalid, or in some instances, difficult for an algorithm to process. When data is missing, the algorithm is not able to use it. If invalid, the algorithm will produce less accurate or perhaps, misleading results. Some datasets could be relatively clean, but they would need to be adjusted. Many datasets also lack useful business context, therefore the need for feature enrichment. It is considered that a good data preparation process produces clean and well-curated data. Clean data leads to more practical, accurate model results.

What are the AI/ML data preparation steps?

In order to create a successful Artificial Intelligence or a Machine Learning data preparation model, every data scientist must be able to train, test, and validate the model before deployment to production.

For a data scientist to prepare data to go to production, they must follow the following steps;

Data collection

Data collection is the preliminary step that addresses common data challenges, which include;

  • Parsing highly-nested data structures into tabular form, for pattern detection and easier scanning.
  • Searching and identifying data from external repositories

A data scientist will need to make sure that the data preparation model they are considering is capable of combining multiple files into a single input. They also need to have a contingency plan set up so as to overcome any challenge arising from sampling and bias in a data set or the AI/ML model.

Data Profiling

This is the second step after data is collected. Here, the data is assessed on its condition, to identify trends, exceptions, outliers, missing, incorrect and inconsistent information.

Data exploration and profiling is an important step because it identifies any biases in the source data, which informs all the model’s findings.

Biased data could potentially alter your model’s findings, an entire data set or a partial data set.

Data formatting

The third step involves formatting the unbiased data in the best way possible that fits your AI/ML model. For instance, data that is aggregated from different sources and is updated manually may contain inconsistencies. Formatting this data to remove errors, makes it consistent for use in the model.

Data quality improvement

In this step, start by having a methodology for managing mistaken data, missing qualities, outrageous qualities, and exceptions in your data. Self-administration data readiness instruments can help on the off chance that they have insightful offices worked in to help coordinate data credits from divergent datasets to join them astutely.

For consistent factors, make a point to utilize histograms to survey the dissemination of your data and diminish the skewness. Make certain to look at records outside an acknowledged scope of significant worth. This “anomaly” could be a contributing blunder, or it could be a genuine and important outcome that could illuminate future occasions as a copy or comparable qualities could convey a similar data and ought to be disposed of. Likewise, take care before consequently erasing all records with a missing worth, as such a large number of cancellations could slant your data set to no longer reflect true circumstances.

DQLabs, AI-augmented Data Quality Platform

Feature engineering 

Feature engineering is the step that involves the use of domain knowledge in extracting features from raw data through different data mining techniques.

Splitting data into preparing and assessment sets 

The last advance is to part your data into two sets; one for preparing your calculation, and another for assessment purposes. Make certain to choose non-covering subsets of your data for the preparation and assessment sets so as to guarantee legitimate testing. Put resources into devices that give forming and listing of your unique source just as your readied data for contribution to AI calculations, and the ancestry between them. Along these lines, you can follow the result of your expectations back to the information data to refine and enhance your models after some time.

What are the capabilities needed in data preparation tools?

According to the Gartner Research report, Market Guide for Data Preparation Tools, when data preparation tools are implemented, the time and reporting of information discovered during the process can be reduced by half.

Data preparation tools need to have the following capabilities in order to improve data trust;

  • They need to support basic data quality as well as governance features. These tools should be able to integrate with other data preparation tools that support both data governance and data quality criteria.
  • The tools should extract and profile data. Ordinarily, a data preparation tool uses a visual environment that helps users extract interactively, search, sample, and also prepare data assets.
  • The tools should create as well as manage data catalogs and metadata. These tools should be able to create, search metadata, and track various data sources, different data transformations, and every user activity against a particular data source. The tools should keep track of data source attributes, relationships, and data lineage.

The AI & ML Lifecycle

Machine learning is considered to be both an art and a science. Organizations rely on data scientists to find and use all the necessary data so as to develop an AI/ML model. The life cycle for data science projects is made up of the following steps;

  • Find necessary data
  • Analyze the data
  • Validate the data
  • Prepare the data
  • Enrich and transform the data
  • Operationalize a data pipeline
  • Develop and optimize the AI/ML model with an AI/ML tool or engine
  • Operationalize the whole process for reuse purposes

Many ML projects do not progress beyond the first step: creating an accurate data pipeline that can be trusted. Studies show that data scientists as well as data analysts report that 80% of their time is spent on the data preparation stages, rather than data analysis.

Many enterprise data preparation tools that have critical enterprise capabilities help data scientist;

  • Prepare data
  • Find and access trusted data
  • Foster collaboration with data governance and protection
  • Operationalize the data pipeline for reuse as well as automation


A proper and thorough data preparation process, when done at the start of an AI/ML project, leads to a faster, more efficient process to the end. Data preparation steps and processes outlined in this article apply to whatever setup you are using. The processes are outlined to help you get better results.

Want to implement a reliable data preparation tool for your organization?