Select Sidearea

Populate the sidearea with useful widgets. It’s simple to add images, categories, latest post, social media icon links, tag clouds, and more.

Follow Us:

Data Preparation

Data Preparation Process


eep learning and Machine learning are becoming more and more important in today’s Enterprises. During the process of building the analytical model using Deep Learning or Machine Learning the data set is collected from various sources such as a file, database, sensors and much more.

Much of the work around data and analytics is on delivering value from it. This includes dashboards, reports, and other data visualizations used in decision making; models that data scientists create to predict outcomes; or applications that incorporate data, analytics, and models.

What has sometimes been undervalued is all the underlying data operations work, or dataops, that it takes before the data is ready for people to analyze and format into applications to present to end users.

We use dataOps methodology as our guideline in order to work with data and prepare it at its best. Dataops is a relatively new umbrella term for the collection of data management practices with the goal of making users of the data—including executives, data scientists, as well as applications—successful in delivering business value from the data.

In general and in most cases, the collected data cannot be used directly for performing analysis process. Therefore, to solve this problem, Data Preparation is done. It includes two techniques that are listed below

  1. Data Preprocessing
  2. Data Wrangling

In AppiLux we frequently face situations in which the data is there, the operation is there but a simple and usable data source is not yet there, hence we often start with this step.


We work with the organization in order to get a hand of every data source that’s there and will try to work with field experts to understand and analyze the data in its context thoroughly, this helps us to start off with the data preparation tasks.


Data Preparation is an important part of Data Science. It includes two concepts such as Data Cleaning and Feature Engineering. These two are compulsory for achieving better accuracy and performance in the Machine Learning and Deep Learning projects.

One important step in data preparation is the data preprocessing step as can be seen in the image. Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.

Data Preparation steps

Therefore, certain steps are executed to convert the data into a small clean data set. This technique is performed before the execution of Iterative Analysis. The set of steps is known as Data Preprocessing. It includes:

  1. Data Cleaning
  2. Data Integration
  3. Data Transformation
  4. Data Reduction

On the other hand we run data wrangling to better prepare the data for making it an appropriate feed.

Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one ``raw`` data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. A data wrangler is a person who performs these transformation operations.

Data Preprocessing is performed before Data Wrangling. In this case, Data Preprocessing data is prepared exactly after receiving the data from the data source. In this initial transformations, Data Cleaning or any aggregation of data is performed. It is executed once.

On the other hand, Data Wrangling is performed during the iterative analysis and model building. This concept at the time of feature engineering. The conceptual view of the dataset changes as different models is applied to achieve good analytic model.

data preprocessing steps

In this image you can see three steps of data preprocessing and how we technically deal with them.

First we deal with missing data and we use one or a combination of methods stated, then we move on to deal with noisy data which dramatically restraint the performance improvement of the ML or Deep learning model and lastly we deal with the inconsistent data which actually is a continuation of step 2 to finally have a better and more usable data set to work with.

For the sake of simplicity we are not going to cover all the technicality we face here.


ppilux is a multi cloud in mind company so we have to be prepared to use different tools and different techniques in different platforms in order to achieve this important part of ML and deep learning projects.

In Google’s GCP we have the option to set our ML pipeline in different ways and in this pipeline we have several places to handle parts or all of the data preprocessing tasks.

As an example, we may do our preprocessing steps in google GCP using BigQuery SQL like commands which gives us the ability to create new tables of billion rows of data but normalize them for instance and make sure we took care of outliers or noisy data or missing data and the like.

On the other hand we can implement computationally expensive preprocessing operations in Apache Beam, and run them at scale using Cloud Dataflow, which is a fully managed auto scaling service for batch and stream data processing or we can implement data preprocessing and transformation operations in the TensorFlow model itself.

The preprocessing we implement for training the TensorFlow model becomes an integral part of the model when the model is exported and deployed for predictions.

In amazon’s aws platform we have several options to use in order to handle the preprocessing step of our job.

We can go for Amazon’s SageMaker platform which enables developers and data scientists to build, train, tune, and deploy machine learning (ML) models at scale and use it in conjunction with other popular tools like Scikit-Learn to facilitate this step or we can use AWS Glue which is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

Other available options are Amazon EMR, Amazon Redshift, Amazon Relational Database Service, and Amazon Athena.

For the sake of data preprocessing on azure platform we normally use a combination of azure machine learning studio and also our own scripting tools in python or R languages to have a neat and perfect data to work on.

One of the best tools in IBM Watson for data preprocessing is The Smart Data Preprocessing (SDP) engine which is a new analytic component for data preparation. It consists of three separate modules: relevance analysis, relevance and redundancy analysis, and smart metadata (SMD) integration.

Given the data with regular fields, list fields, and map fields, relevance analysis evaluates the associations of input fields with targets, and selects a specified number of fields for subsequent analysis. Meanwhile, it expands list fields and map fields, and extracts the selected fields into regular column-based format.

Due to the efficiency of relevance analysis, it is also used to reduce the large number of fields in wide data to a moderate level where traditional analytics can work.

Start Your AI Path With Us

Create an efficient machine learning pipeline on the cloud with AppiLux team of experts and focus on new opportunities that AI presents you on the fly
Talk To An Expert