What is Data Transformation?
Definition, process and the new generation of tools

By Brian Laleye · March 29, 2022 · 17 min read

This article aims to define data transformation and how every business can use it to easily model and transform their data.

The business world has been transformed with the advent of cloud computing and big data. According to the IDC (International Data Corporation), 175 Zettabytes of data will be generated in 2025 which represents almost 3 times the amount generated in 2021(61 Zettabytes). Data is now considered a company’s most significant asset and its value is priceless. Due to this critical aspect, companies have come to realize that the availability of their data supports their core business activities. And so, it has become essential for them to find the best possible ways to convert their information into true business assets.

You may have heard the term data transformation before, but do you really know what it means? What does it look like? How does it work? What are the different steps involved in it? And most importantly, how does data transformation impact your business and your industry?

Read on to learn everything you need to know about data transformation.

Summary

Various classes of data

Data is available in various forms and it is rarely present in a form that is perfect for processing.

Data comes in various classes:

Structured: the data is present in a field-oriented manner (think Excel sheets or SQL Databases)
Semi-structured: the data is hierarchical but not field orientated (think XML or JSON)
Unstructured: the data is unorganized and not field orientated (think of text files, images, emails, audio/video files, etc

To dig deeper, let’s take a look at how the data is structured:

Rectangular data structures: A rectangular data structure is one where all the rows have the same number of columns, and all the columns have the same number of rows. The simplest example is a spreadsheet. In this case, the rows are the country where visitors of your website came from. Visitors from each country have visited the website each day of the week. This type of data structure is great for analysis because it lends itself to many very common statistical analyses.

Non-rectangular data structures: Often, however, data comes in a more non-rectangular format. It may not have as many rows as columns or vice versa. For instance, imagine you had a list of clients and their contact. Each customer would have a name, but some may not have a phone number listed while others may list several of them. This type of data structure, which is often present in marketing data sets and relational databases, can be difficult to store and analyze using traditional statistical software packages.

The solution to these problems lies in Data transformation.

The data transformation definition

Data transformation is a relatively young field. In the IT business, it started with the move from Transaction Processing to Data Management and Aggregation in the late 70s and early 80s.

It continued with the development of data warehouses and data marts in the 90s, but it was only around 2005 when ETL (extract, transform, load) processes became popular that Data transformation was democratized.

Data transformation is a process used to convert (or map) data from one format or structure into another format or structure.

In computing, data transformation is the process of converting data from one format or structure into another format or structure. Usually, data transformation is performed using extract, transform and load (ETL) techniques. Data transformation is a crucial step in the data integration process because it prepares and normalizes data for further analysis, reporting, and visualization. Data transformation can be performed on any type of dataset, regardless of its original format or designation.

The data transformation process

There are several steps and techniques involved in Data Transformation which can be divided into 3 broad categories:

Data Extraction (Input): this is the first step where data is extracted from various data sources. Some of the common data sources include raw files (.txt, .csv, .xlsx, JSON…), business applications, databases, etc.
Data Cleansing (Modeling): in this step, the data is cleaned to make it consistent and accurate. Data cleansing includes removing inconsistencies in the data, missing values, and duplicates.
Data Integration (Output): here the extracted data is transformed into a format that can be used by downstream applications. These destinations may be SaaS applications, databases, data warehouses, or just GoogleSheets and Excel files.

As discussed earlier, the main goal of Data Transformation is to convert the raw data into a format that can be used for further analysis. The above 3 step process is generally followed for achieving the same.

The most common data transformations

Filtering and subsetting: Filtering refers to selecting only certain rows of data, while subsetting means selecting only certain columns. For example, filtering can be performed on a set of records by specifying that the output must contain all records in which the purchase amount is greater than $100. Subsetting can be performed by specifying that the output must contain only the client’s last name and purchase amount field.

Complete set of boolean operations. x is the left-hand circle, y is the right-hand circle, and the shaded region show which parts each operator select from r4ds

Conversion between character encodings: A character encoding is a code that pairs a set of characters with something else, such as numbers or bytes. Character encoding schemes are necessary because computers only understand numbers; each letter or symbol stored on a computer must be represented as a number. Each character encoding scheme sets its own guidelines for which numbers represent which characters.
Standardization: Standardizing data involves converting it all into a consistent format. For example, if you have addresses in one column, you might be able to separate them by address, city, state, and zip code. That way, you can analyze the data at each of those levels or even import it into a mapping tool and visualize the geographic distribution of your data.
Sentiment analysis: For example, if you were analyzing tweets about your product, sentiment analysis would help you determine whether people are talking positively or negatively about it. You might use sentiment analysis to filter out any tweets that don’t mention your product at all.
Aggregation: Aggregating data means combining several pieces of information into a single row or column. This can be done with pivot tables in Excel or by grouping records in Google Sheets. For example, if you want to see how many customers live in each state, you could group customer addresses by state and then count the number of customers per state

Illustration of frequency encoding from towards data science

For more examples, refer to the section Pipeline(Modeling).

What happens after data transformation?

In this section, we dive into what happens once you have transformed your data.

Data analytics: After data has been transformed it is ready to be fed into the tools that will enable you to analyze it. Analyzing data is key to gaining meaningful insights from your business. For example, analyzing your customer’s purchase history will allow you to make informed decisions about what products you should market or stock in the future.

“Operational Analytics is the use of data, predictive analytics, and business intelligence tools to gain insight into business operations, and to generate real-time actions thanks to activated data”

Data Visualization: the next step after transforming your data is to visualize the results so that they are easily digestible for humans. There are many tools available to visualize your results using charts, graphs, maps, and more. Being able to easily visualize your results will make all of your hard work worthwhile!

Machine learning: Once the data has been transformed into a format that is machine-readable, it can also be used to train machine learning models. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.

How data transformation is used in companies?

“I probably spend more time turning messy source data into something usable than I do on the rest of the data analysis process combined.”

Pete Warden in his Big Data Glossary

Data transformation is necessary because business enterprises will often store data in more than one database to achieve flexibility, recoverability, and cost-effectiveness. For example, a large enterprise may use different databases for different departments: financial information may be stored in a PostgreSQL database, while employee information is stored in a MongoDB database.

As such, a massive number of queries and transformations are made between the databases every day.

Transformation is required in the operation of any enterprise-level business, they are often automated using software tools. For example, when emailing customers invoices, most small businesses don’t have to worry about altering file formats – all they have to do is send an attachment. However, some people use specific software for editing images to create products for sale. Such individuals would probably find new software more useful than old email habits because changing files often results in wasted work and lost sales opportunities. In general, there aren’t very many how-tos out there on data transformation.

Aside from several online tools that claim to simplify the process but actually make it harder, technology blogs offer fragmented advice – some detailing basic instructions while others list separate tools you could use. While these tactics may give readers information needed to complete certain projects, they lack discussion on how important each step is and which resources are best suited for helping you reach your goal with as little effort as possible!

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How does it help in businesses?

The main advantage of data transformation and integration comes in improving data quality and preparing it for analysis. The idea behind taking multiple sources of information and making them consistent is that businesses will be able to trust their analytics more than they would otherwise. And, if you’re a business owner who wants to make better decisions, you’ll want to know as much about your data as possible. If there are any discrepancies, it could severely impact your financial planning and forecasting. With good data preparation processes in place, however, businesses can start gaining more meaningful insights from their decision-making process by trusting those insights even more.

Think of it like an engine. If an engine has high integrity, it should give off fewer errors over time and thus should require less maintenance overall to run properly. Business owners want high integrity data because they want their business engines running smoothly without having to worry about repairs or other disruptive events. Data transformations help achieve just that – consistency, transparency, and accurate results – without jeopardizing how fast or hard employees work with integrated technology systems or when using unconnected apps at work. That’s what makes data transformation so essential for successful businesses today: it provides functionality without compromising speed of operation or company growth potential when executing new initiatives in various departments across a wide range of products and services offered. Yes, companies face risks, but data transformation and data integration solutions also mitigate threats while helping organizations succeed in getting ahead of emerging trends while remaining agile enough to pivot when necessary.

Effective data transformation and integration efforts create business value through clarity (transparency) and understanding (improved accuracy). Both lead to competitive advantage, which is exactly why forward-thinking businesses continue transforming their enterprise applications into a state where data flows seamlessly among various IT platforms. So what does all that mean? Well, building strong relationships within a marketplace builds trust – and ultimately leads to increased revenues.

For B2B businesses specifically, customers want to know that you understand their needs before they buy anything. Being upfront about costs and ensuring payments remain on schedule helps solidify relationships between partners. By providing visibility into supply chains around the world, suppliers show respect for buyer requests regarding pricing fluctuations or delivery deadlines. All these elements build on one another and contribute to delivering positive customer experiences consistently over time. Companies have found success in building long-term partnerships through lean supply chains, although data flow continues to grow exponentially every year due to machine learning and automation investments designed to increase innovation globally.

The challenges of data transformation

The challenges of data transformation are numerous and include:

Data sources: Data transformation often requires that information be obtained from multiple data sources. These sources may exist both inside and outside of an organization, and there may be substantial differences in how these sources store information, how complete their data is, and the reliability of their data. For example, an organization wishing to analyze relationships between customers’ credit card purchases and their current location, income level, and age will need to combine transaction records with customer demographic information.

Timeframes: Data transformation often involves combining information from multiple time periods. Since the source of this information may not have been created at the same time, it may have variations in its structure and quality that complicate the integration process. For example, consider an organization trying to match sales records with details about customer purchase history. The sales records are likely to have been generated by a single system at one.

Cost: It is possible that data transformation will be costly. The price is determined by the infrastructure, software, and tools that are utilized to process data. Licensing, computing resources, and recruiting appropriate employees are all possible expenses.

Openness: Organizations are becoming more and more porous because teams collaborate with many stakeholders (providers, partners, freelancers, internal teams etc.) and it is necessary to speak the same language when teams do not have the same level of maturity to understand the data. For instance, the marketing team should know how to express their need in terms of data to the engineering team.

“Data transformation is not a panacea; it is a vision, a policy, and a mass of initiatives which raise new challenges and issues.”

Julien Lévy, Academic Director of the Innovation & Entrepreneurship Center at Hec Paris
The challenges of data transformation in companies

Data transformation historical tools

Data transformation software is a core component of an ETL (extract, transform and load) pipeline, which is used to migrate data from one source to another.

The best tool for your project depends on what you’re trying to do—spreadsheets may be fine if you just need to manipulate data before inserting it into a database, but they can get unwieldy when managing larger volumes of data. Spreadsheets also tend to have limited support for transformations (e.g., if you have hourly statistics from one day and weekly from another). Scripting languages, like Python or SQL-Lite, let developers use highly expressive syntax; however, both lack built-in abstractions for storing results.

Data transformation tools have been available since the mid-1980s, but they have become much more common in recent years with increased use of open source software tools such as DataWeave (MuleSoft), Talend Open Studio, and Spoon (Pentaho).

The most common type of data transformation tool uses a graphical user interface (GUI) to design and test mappings, which are then often exported to a file that can be run automatically within an integration solution.

These tools need highly skilled data engineers in order to ensure the efficiency of your data transformations.

To increase the efficiency of your data transformation process and ensure the excellent quality of your data, you need to combine the analytical skills of data engineers with enterprise-grade technologies.

Reverse ETL: How data transformation has become accessible

In order to make data transformation accessible, the Reverse ETL tool is the solution. Nowadays, Reverse ETL has become more and more popular.

Reverse ETL is essentially the same as ETL except it moves data in the opposite direction. Data is extracted from a source database, transformed according to business rules, and loaded into another database.

Reverse ETL is a set of methods or processes that sync data from a data warehouse to operational tools like CRM, or any business tool (Slack, Google Sheet, etc).

The idea behind doing it in reverse is that you can consolidate all of your company’s information in one central location, allowing for more efficient analysis and decision-making down the road.

By centralizing your data you are also creating a single source of truth for your organization.

With RestApp, easily model and transform your data with the use of No Code. Empower anyone to clean and transform data from disparate sources without friction. Create, edit and share your data models as a product with your teammates.

If you’re interested in starting with your Data Transformation, check out the RestApp website or book a demo.

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Laurent Mauer

Laurent is the head of engineer at RestApp. He is a multi-disciplinary engineer with experience across many industries, technologies and responsibilities. Laurent is at the heart of our data platform.

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