Maximizing the Value of Your Data: A Step-by-Step Guide to Data Transformation

Data is the lifeblood of any organization, but raw data on its own is not enough. To unlock its full potential, you need to transform it into valuable insights that can drive decision-making, improve operations, and increase revenue. That’s where data transformation comes in.

Data transformation lets you take your data from chaos to clarity and achieve your goals with the right tools and methods. This article will provide a comprehensive guide to maximizing your data’s value through effective transformation processes and data management.

Here, we’ll introduce you to the different types of data transformation, explain why it’s important, and outline the steps you need to take. We will also share frequently-used methods and tools for data transformation and the common challenges you may encounter.

What is Data Transformation?

Data transformation converts raw data into a form used for analysis and decision-making. The goal of data transformation is to improve the quality, structure, format, and visualization of the data so that it can be more easily understood and utilized by end-users, applications, APIs, and business intelligence tools.

The data transformation process can involve various methods and techniques, such as normalization, aggregation, smoothing, and data mapping, to clean, organize, and prepare the data for further use.

Raw data usually comes from multiple sources in different formats (e.g., CSV files, E xcel sheets, XML format). To make it usable for business intelligence or machine learning tasks, you need to work on the enrichment of data quality and compatibility.

Data transformation usually occurs before any statistical analysis or machine learning algorithms are applied to the data set. Data transformation helps streamline business processes by helping you quickly identify trends in large datasets, automate repetitive tasks, and gain insights about customer preferences and behaviors.

The insights from data transformation can help data analysts make better decisions, optimize their operations, and achieve their business goals.

What Are The Common Types of Data Transformation?

Data scientists use different types of data transformation to make raw data usable. The four main categories are:

Constructive Transformation

Constructive transformation involves adding information to the dataset. This can include replacing missing values, correcting errors, and filling in gaps. For example, you can use constructive transformation to add a customer’s contact information or address if it is missing from your original dataset.

Destructive Transformation

Destructive transformation deletes unnecessary data from the dataset. This helps you focus on the most relevant information and reduce noise in the dataset. Common examples of destructive transformations include removing duplicates, deleting irrelevant columns, and removing any outliers.

Aesthetic Transformation

Aesthetic transformation improves a dataset’s overall look and feels by enhancing the data formats for better visualization. This could include sorting data into columns, adding colors, or changing fonts.

For example, formatting the data for improved data analytics, such as converting date and time data into a standardized format.

Structural Transformation

Finally, data engineers use structural transformation to alter your data’s structure and source systems to make analysis easier. It can also combine multiple data sources into one large source so you can gain deeper insights from it.

Common examples of structural transformations are normalizing data across data pipelines, converting strings to numbers for quantitative analysis, and using pivot tables for better organization.

What Are The Steps in Data Transformation Process?

Data science professionals have developed a series of standardized steps for data transformation, which we will explain in detail below:

Step 1: Data Discovery

The first step of the data transformation process is to identify and collect the relevant datasets through data profiling. This includes understanding your source systems, clarifying what you need from them, and understanding the data structure. Your sources may include data from databases, spreadsheets, or other platforms.

Step 2: Data Mapping

Now, you need to map out how your datasets will interact with each other to create an efficient workflow. Here, analysts decide how individual fields are matched, filtered, joined, and aggregated to create a suitable format for analysis.

Step 3: Extraction

During the extraction phases, analysts move data from its source system into the target system, which may include structured (database) or unstructured (event streaming) sources depending on the needs of the project/application at hand.

Step 4: Generating and Executing Codes

You need to generate codes for each step in your mapping process at this stage using a programming language such as Python or SQL. This code should contain instructions on the transformation process and which datasets are involved at each step.

You can then program the codes in a set schedule to get meaningful data in real-time.

Step 5: Reviewing Outputs

Data analysts must also review the transformed data for accuracy and completeness by comparing it against pre-defined quality metrics or other standards. They should also address discrepancies between the expected and generated output with further coding or manual adjustments.

Step 6: Forwarding to Target Destination

The final step is to forward the transformed data to its target destination. This could involve sending it to a database, file system, cloud data warehouse like Snowflake, or another application for further analysis and usage.

Note that data transformation is a cyclical and ongoing process that requires constant monitoring and adjustment.

Frequently-Used Data Transformation Methods

Now, coming to the more technical side of data transformation, let’s look at the different methods and processes you can use to make your data more meaningful. Here is a list of commonly used approaches:

Aggregation

Aggregation involves combining data from multiple sources into a single dataset to create a unified view of data from disparate systems. For example, you may aggregate sales data by product or region to get an overview of your sales performance.

Smoothing

Smoothing removes noise or meaningless data from a dataset to make the data more manageable and easy to analyze. Data analysts often use this to reduce volatility in time-series data and make trends more visible by making small changes.

Generalization

Generalization involves reducing the level of detail in a dataset. For example, you may generalize customer data by grouping customers into segments based on similar characteristics.

This method involves replacing detailed data points with more general ones. For example, you can generalize a dataset containing personal details by replacing names and addresses with codes.

Discretization

Discretization divides continuous data into a finite number of intervals or categories to make analyzing and interpreting data easier, especially when working with large datasets.

Discretization helps handle continuous attributes in datasets such as age, income, etc. For instance, you can discretize a continuous attribute such as age into three categories- young (18-30 years), middle-aged (31-50 years), and old (> 50 years).

Attribute Construction

Data scientists create new attributes or variables based on existing data through the attribute construction process. This method involves feature engineering, where you can create unique attributes from existing ones by combining multiple fields.

It helps you identify patterns or relationships between different data points that would not be obvious in the raw data. For example, you could construct an attribute for “total sales” by summing up the values of individual transactions over a certain period.

Normalization

You can scale your data through normalization to fit within a specified range and ensure data consistency across different datasets. Normalization also makes comparing other variables easier and helps reduce data redundancy.

Manipulation

Data manipulation, also sometimes referred to as data wrangling, involves changing or formatting data to make it more suitable for further analysis or presentation. This could include converting text into numbers, sorting values, or removing unnecessary information from datasets before you can use them for further processing.

Variable Transformation for Data Normalizing and Smoothing

To effectively deal with data smoothing and normalizing processes, “variable transformation” is a valuable tool. By applying several mathematical functions known as variance stabilizing transformations, you can transform variables and reduce their skewness in the value spread across them.

Common functions include:

• Logarithms,

• Reciprocals,

• Powers,

• Square roots,

• Yeo-Johnson transformation, and

• Box-Cox transformation.

You can use these mathematical functions to reveal linear or additive relationships between target variables and predictors that may have been previously obscured.

Types of Data Transformation Tools

Data transformation tools are essential components of the modern data-driven world. By enabling users to transform and migrate data from disparate sources into a single, centralized repository, companies can quickly derive insights from their collected information.

Many data transformation tools are available today, ranging from essential scripting solutions such as SQL or Python to more advanced low/no-code platforms like Zuar’s Mitto that provide an interactive GUI for collecting and loading data into a warehouse.

Scripting Tools

Scripting tools are designed for technical users and developers familiar with SQL or Python programming. These tools allow you to write scripts, automate the data transformation process, and achieve scalability.

A typical example of such a tool is dbt, which combines the functionalities of SQL and Python to create tables and transformations. The Python Mitto SDK is another helpful tool for scripting and automation, letting you interact with multiple business functions and schedules.

Low/No-Code Tools

On the other hand, these tools offer user-friendly dashboards and are intended for non-technical users who don’t have programming skills. They let you collect data from various sources and load it into a data warehouse using an interactive graphical user interface.

Over the past decade, many low-code solutions and Extract-Transform-Load (ETL tools) have emerged, such as Zuar’s Mitto. Mitto provides data integration like ETL/ELT capabilities and helps with the ETL process and management of data throughout its journey. With options for hosting either on-premise or in the cloud implementation, Mitto has code and no-code options to accommodate different skill levels and preferences.

Why Should You Do Data Transformation?

Data transformation is a crucial part of data analysis and management. It allows you to clean, organize, and standardize your datasets to use in analytics or other business operations effectively. The process offers several benefits, such as:

• Improved usability - Computer and human analysts find it easier to work on transformed and organized data.

• Enrichment of data quality - Data transformation eliminates potential quality issues, preventing misinterpretations of the data.

• More efficient query handling - Standardizing the data and storing it properly help with improved data warehousing and will optimize query speed, decreasing friction to analysis.

• Streamlined data management - Metadata and lineage tracking implemented through transformation make it easier for teams to manage data gathered from different sources.

While data transformation methods have numerous benefits, it’s essential to understand that a few potential drawbacks exist.

Challenges with Data Transformation

The process of transforming raw data into valuable insights and information can present many challenges for organizations, as follows:

• Cost: One of the biggest challenges is the high pricing of data transformation tools and the need for professional expertise.

• Resource demand: Another challenge organizations face is the demanding compute resources required for the data transformation process. The intensity of some on-premises transformation processes can take a toll on other business functions.

• Skill shortage: The shortage of skilled data professionals is another major challenge organizations face regarding data transformation. Data professionals are in high demand and can be difficult to recruit and retain, which can pose a challenge.

• Priority handling: Finally, aligning data transformation activities with the business’s data-related priorities and requirements can be challenging. It’s vital for organizations to properly prioritize their data transformation goals and ensure that their transformation activities align with their overall data strategy. Failure to do so can result in a confusing and ineffective transformation process.

Wrap-Up: Master Data Transformation Process

Whether it’s for better data organization, improved data quality, faster queries, or simplified data management, the benefits of data transformation are clear. However, with any complex process, there are also challenges to be aware of, including high costs, resource constraints, and the need for skilled data professionals.

Despite these challenges, organizations can overcome them by carefully planning their transformation processes. The Python Feature Engineering Cookbook and Feature Engineering for Machine Learning course are two excellent resources to help you get started with mastering data transformation.