Category: Data Cleansing

The Difference Between Data Cleansing and Data Transformation

There is a lot of confusion about the difference between data cleansing (also known as data screening) and data transformation. Both are important to any data analysis, whether or not machine learning is to be used. In this article, we break down the difference between data cleansing and data transformation.

Data Transformation

What is Data Cleansing and Why is it Important?

Data Cleansing, also known as data cleaning or data screening, is the process of preparing data for analysis, statistical modeling, or machine learning algorithms. This is done by deleting or modifying incomplete, incorrect, irrelevant, or inconsistent data. Data cleaning addresses factors such as outliers, noise, missing data, inconsistency, relevance and redundancy. This process is critical for ensuring consistent data quality and realizing accurate, actionable insights. “Garbage in, garbage out”, as they say.

What is Data Transformation?

Data Transformation is the process of transforming your data into a usable format, optimized for your specific business goals. If you plan to use statistical modeling or machine learning algorithms, the type of data transformation needed will vary. If data is to be fed into machine learning algorithms, it will also involve adding “features” in a process called “feature engineering”. 

Data cleansing and data transformation work hand-in-hand and are absolutely essential to any data pipeline. For a detailed article on Data Transformation, see our blog article: Data Transformation: An Executive’s Guide to Affordable AI.

Data Screening and Transformation Process

Once the business goals have been identified, data cleansing is just one of the stages in the data screening and transformation process shown below.

This process leverages both SEMMA and CRISP-DM principles and is detailed below.

Steps In Data Transformation

Data Fusion

Data usually comes from multiple sources, which makes it difficult to analyze without transforming the data into a homogenous format.  This is known as syntactic heterogeneity. 

When data sets for the same domain are developed by different people, this can lead to differences in meaning and interpretation. This is known as semantic heterogeneity. 

Various techniques to address both types of heterogeneity can be used for data fusion.

Data Quality Assessment

Establishing quality criteria for your data involves considering outliers, noise, inconsistency, incompleteness and redundancy within data sets. 

Outliers are results that deviate so far from the rest of the data that its validity is in question. They should be eliminated to avoid skewing the results.

Noise is meaningless data that is irrelevant to the analysis being performed. These data can hinder machine learning and should also be eliminated.

Inconsistency means the data contradicts itself somehow due to mislabeling or mistakes. 

Incompleteness is data with missing values that must be eliminated or filled in by various statistical methods. 

Redundancy is when there are multiple copies of the same data in databases, which can skew results.

Data Selection

Data selection determines what data is used for analysis or modeling and includes an assessment of the data relevance. Lots of data is eliminated in this phase to prevent costly data cleansing and/or feature engineering on irrelevant data.    

Data Cleansing

Data quality is improved, based on criteria set forth in the quality assessment phase. Issues related to outlier, noise, inconsistency, incompleteness and redundancy are fixed. 

Feature Engineering

Feature Engineering uses domain knowledge of the data to extract the most useful features used by algorithms. These are fed into machine learning algorithms to supercharge performance. Feature engineering consists of the creation, transformation, extraction and selection of features from raw data.

Data Screening and Transformation are important steps to ensure the data used for analysis or modeling will produce the most accurate and actionable insights for business or operational decision-making. 

Accelerating your AI and Machine Learning Initiatives

Why dive into an AI or Machine Learning program before making sure you can get the ROI you need?

If you would like to kick-start your Artificial Intelligence or Machine Learning initiative, Cloud App Developers, LLC has created a valuable offering in our Machine Learning Proof of Concept.

Who Would Benefit?

Those who:

  • Have lots of legacy data and want to launch an AI or Machine Learning Program, but don’t know where to start.
  • Need to validate if business goals are achievable through data science, Machine Learning or Deep Learning. 
  • Want to know what is possible with AI and Machine Learning.

What does the program include?

The Cloud App Developers’ Machine Learning Proof of Concept is designed to provide a useful assessment of your data, validate your business goals against available data and models, and to help identify other business goals that might be possible through Machine Learning. Our Machine Learning Consultants will then compile a comprehensive report and present the findings to your stakeholders. A typical program would include the following stages:

Accelerator Program Stages                                       Questions Answered

Review of your Business Goals“What are the business goals you hope to address with AI or Machine Learning?
Assessment of existing data“What is the nature and quality of your existing data?” “Is there any missing data?” “What data preparation is needed?”
Top-Level Validation of Business Goals“Can your existing data support your Business Goals?” “What other goals might be achievable?”
ML Model Recommendations“Which ML Algorithms or Analytical Models are best suited to meet my business goals?”
Data Transformation of subset of data (see below)“How much will it cost to prepare all of my data for modeling?”
Run Models against one business goal and a subset of data“How do I validate how AI and Machine Learning can help me?” “Can I accomplish some goals with Analytical Modeling or do I need sophisticated ML Modeling?”
Generate ML Report with RecommendationsFull Report on Machine Learning Readiness and Goal Validation Includes recommendation and cost estimates for full Data Transformation and ML Program.

Visit our Software Solutioneer Blog for more articles.

Data Transformation – An Executive’s Guide to Affordable AI

Don’t Spend A Fortune To Extract Huge Value From Your Data

Data Transformation can help unlock this value while avoiding the high costs of developing artificial intelligence. By focusing on critical steps in data transformation you can enable powerful data science and machine learning capabilities that will address many business objectives.

Data Transformation
Data Transformation

You can’t walk before you crawl, and you can’t run before you walk. Even if you plan to eventually develop a robust Artificial Intelligence program, the first step on this journey is data transformation of your legacy data. You will be surprised at how much value you can extract along the way.

What is Data Transformation?

Data transformation is the process of transforming your data into a usable format, optimized for your specific business goals. Whether you plan to use data science or machine learning algorithms, the type of data transformation needed will vary. By following these steps, you can transform your data for any type of analysis or modeling you wish to perform.

Steps in Data Transformation

Whether you are planning to use simple statistical modeling or machine learning algorithms, the first step is transforming your existing data into formats that are easily ingested by either type of model. The steps in data transformation are detailed in Fig. 2 below.

Steps in Data Transformation
Fig. 2:  Steps in Data Transformation

Proper data fusion, quality assessment, data selection, and data cleansing will enable powerful business insights, even without machine learning algorithms.

Some of your models may rely upon statistical modeling techniques that do not require feature engineering. However, should you choose to develop and/or deploy machine learning algorithms, expert feature engineering will make them sing!

Feature Engineering – the “Secret Sauce” of Data Transformation

High-performing machine learning algorithms are not possible without proper data preparation and expert feature engineering.  Feature Engineering is the “Secret Sauce” that turns average machine learning (ML) algorithms into High-Performance ML Algorithms.

Feature Engineering is typically a collaboration between two parties:

  • Domain experts who have a deep understanding of the data itself, and
  • Machine learning engineers who are experts at choosing and optimizing machine learning algorithms. 

These two roles are equally important for the success of any machine learning initiative.

What is Feature Engineering?

Feature Engineering uses domain knowledge of the data to extract the most useful features used by algorithms. These are then fed into machine learning algorithms to supercharge performance.  Feature engineering consists of the creation, transformation, extraction, and selection of features from raw data.

Feature engineering uses various data optimization techniques to enhance algorithm performance.

For example, feature engineering might remove irrelevant features, and prioritize the features that are most useful to the models.  The amount of data can also be reduced to a more manageable amount through feature extraction techniques.  All of these are necessary elements of a high-performance machine learning initiative.

Feature Engineering, Data Transformation and Model Selection

Machine Learning Pipeline
Figure 3, Machine Learning Pipeline, Image Source: Oreilly.com

As seen in Fig. 3, features and models work together to produce high-performing machine learning results. Selecting the right algorithms is only half the battle.  In high-performing machine learning programs, model selection and feature engineering complement each other.  Bad feature and/or model selection combinations can negatively affect machine learning performance.  And you can’t have proper feature engineering without data transformation.  It’s all tied together.

Data Transformation Unlocks Affordable Alternatives to A.I.

For companies without huge AI budgets, there are alternatives. Knowing the differences and limitations of Machine Learning and Data Science is key to choosing the right strategy.

Despite tremendous upside and hype, the dirty little secret is that full-blown Artificial Intelligence programs can be extremely costly. Also, they can take years to develop. A.I. requires layers of deep learning algorithms to build the necessary neural networks. This takes a lot of money, time, and effort.  Artificial Intelligence Consulting often fails to take this into consideration. 

For the purposes of this article, we will focus primarily on Data Science and Machine Learning strategies. We will avoid the more costly deep learning solutions required for A.I.  The importance of Data Transformation, as well as Feature Engineering, will be highlighted.

Data Science vs Machine Learning

What is the difference between data science and machine learning and why should you care?

As you kickstart your machine learning program, why not take advantage of the significant benefits of advanced analytic techniques that do not require machine learning algorithms? After all, it will take some time to optimize your machine learning algorithms anyway. A proper data transformation initiative will prepare your data for whichever type of analysis you want to perform.

What is Data Science?

Data Science uses statistical approaches and advanced analytics techniques to extract useful insights from data.  Usually in response to specific requirements from business executives, Data Science uses data analytics, mathematics, and statistics to extract those specific insights. 

Data Science techniques form the core of business intelligence systems that rely partly on humans to spot trends in spreadsheets, charts or graphs. 

Not very sexy, but don’t discount their value.  Even today, companies rely on such methods to drive significant business value, often without machine learning.  Data science case studies can be found to address many important business objectives.

For some of your business objectives, a data science-based business intelligence system may be all you need.  To aid in decision making, Data Analytics Consulting may be helpful to visualize and present the data to stakeholders in your organization.

What is Machine Learning and When Should You Invest in it?

Simply put, Machine Learning is when machines can identify patterns in legacy data and then use those patterns to generate insights or predictions whenever new data is introduced into the machine learning system. 

To decide when to start investing in machine learning, it is helpful to understand the limitations of data science-based Business Intelligence Systems. 

As companies store data in larger quantities, and from more sources, with varying quality levels, data science-based Business Intelligence Systems fail. This is because of the “4 Vs” associated with Big Data:  Volume, Variety, Velocity, and Veracity of data.  At some point, relying upon humans to deal with the 4 Vs becomes untenable.  That’s where introducing Machine Learning begins to make sense. Machines are simply better at handling “Big Data”.

Why Machine Learning?

Machines are far better at dealing with large data sets with disparate sources and varying quality levels. 

A plethora of machine learning algorithms have been developed to handle classification, regression, and clustering tasks for these data sets.    Also, not all business objectives can be accomplished with data science techniques alone. In particular, Unsupervised Learning and Reinforcement Learning algorithms enable powerful insights not possible with standard data science approaches. See Figure 1 below: Data Science vs Machine Learning vs Deep Learning

Data Science vs Machine Learning vs Deep Learning
Figure 1: Data Science vs Machine Learning vs Deep Learning

Accelerating your AI and Machine Learning Initiatives

Why dive into an AI or Machine Learning program before making sure you can get the ROI you need?

If you would like to kick-start your Artificial Intelligence or Machine Learning initiative, Cloud App Developers has created a valuable offering in our Machine Learning Proof of Concept

Who Would Benefit?

Companies would benefit if they:

  • Have lots of legacy data that want to launch an A.I. or Machine Learning Program but don’t know where to start.
  • Need to validate whether if business goals are achievable through data science, Machine Learning or Deep Learning. 
  • Want to find out what else is possible with A.I. and Machine Learning.

What does the program include?

Cloud App Developers’ Machine Learning Proof of Concept is designed to provide a useful assessment of your data, validate your business goals against available data and models, and to identify any other business goals that might be possible through AI or Machine Learning.  Our Machine Learning Consultants will then compile a comprehensive report and present the findings to your stakeholders. A typical program would include the following stages:

Accelerator Program Stages                                       Questions Answered

Review of your Business Goals“What are the business goals you hope to address with A.I. or Machine Learning?
Assessment of existing data“What is the nature and quality of your existing data?”  “Is there any missing data?”  “What data preparation is needed?”
Top-Level Validation of Business Goals“Can your existing data support your Business Goals?” “What other goals might be achievable?”
ML Model Recommendations“Which ML Algorithms or Analytical Models are best suited to meet my business goals?”
Data Transformation of subset of data (see below)“How much will it cost to prepare all of my data for modeling?”
Run Models against one business goal and a subset of data“How do I validate how AI and Machine Learning can help me?” “Can I accomplish some goals with Analytical Modeling or do I need sophisticated ML Modeling?”
Generate ML Report with RecommendationsFull Report on Machine Learning Readiness and Goal Validation Includes recommendation and cost estimates for full Data Transformation and ML Program.

Book a Free Consultation! > Message our Data Scientists to learn more

What is Data Transformation?

Data transformation is the process of transforming your data into a usable format, optimized for your specific business goals. It works hand-in-hand with data cleansing to prepare data for machine learning and data analysis.

Steps in Data Transformation

Steps in Data Transformation include data fusion, data quality assessment, data selection, data cleansing, and feature engineering.

What is data science?

Data Science uses statistical approaches and advanced analytics techniques to extract useful insights from data. Data Science techniques form the core of business intelligence systems that rely partly on humans to spot trends in spreadsheets, charts, or graphs.