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What is Data Transformation? + How to Make it Easier

If you're dealing with data in your business, it's important to understand the fundamentals of data transformation. In this article, we'll look at the benefits of data transformation, challenges to avoid, and how you can make the process easier using an ETL tool.

What is Data Transformation?

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Data transformation is a term used to describe the process of changing the format, values, or structure of data. In simple terms, data analysts use data transformation processes to convert raw data into a format that’s much easier to analyze and interpret.

This can mean anything from using basic formulas to remove duplicates, to complex restructuring. The result is being able to present data in a completely different format from its original version. Data transformation is taking place within your business all the time, even if you don't know it. A 2018 study found that companies that invest in marketing technology are top performers. Why else would we be dealing with so many spreadsheets and data points?

Just think of downloading data daily and producing reports through a software interface, or manually exporting data and formatting it. Any form of data manipulation is data transformation. When a company needs to handle big data, it becomes a complex task that requires additional tools and processes. Leveraging the process of data transformation can enhance your business intelligence so you can make better decisions in real-time.  

Why Would You Need to Use Data Transformation?

There are almost endless reasons why you might need to utilize data transformation on some scale. Some of the key reasons companies commonly need to transform data include:

Integrating Data Between Different Systems

It’s common to use more than one IT system within an organization, even dozens. Some of which need to send or receive data to/from one another. To transfer data seamlessly, there’s a good chance it’ll need format changing to be compatible. When you change that data for a different system, software, or program, you’re using data transformation.

Data Profiling

You can scan vast sums of data and develop profiles based on your criteria. These profiles can later serve you in any number of ways later, from data mapping and setting metrics, and beyond.

Presenting Data

When presenting data to other people, the clearer you make the information, the easier it will be for them to understand. Maybe you are data wrangling the sales numbers from across several regions, summarizing large amounts of data, or just formatting it into bite-size bits.

End-users will always prefer one format over another, which is likely different from its original form. Some data transformation will have to take place between the raw data and the end format. Make it relevant and digestible to the person in front of you.

Legibility

Often times, raw data is not legible in its original form. Binary data comes as “0s” and “1s” to us in its raw form. The data quality is low and not very useful to human eyes at that point. We need data transformation to turn binary data into a format that is legible to both computers and humans, depending on how we use it.

A less extreme example could be a massive spreadsheet of data exported from a system that has no formatting, showing as long strings of text. You can use data transformation to break up all this information into separate cells so you can leverage pivot tables and analyze it.

Common Data Transformation Processes

There are a few ways companies commonly transform data. Most revolve around using ETL (Extract, Transform, Load) tools. These tools enable processes to scale and handle large amounts of data quickly and efficiently. It’s also common for companies to try and handle data transformation without specialist tools. This is particularly common for small amounts of data or less complex solutions, and can often suffice.

Data transformation is a personalized process, as very few data export processes are the same. Each method presents its challenges considering the application, costs, and time involved. Here are the three most common methods of transforming data that companies typically use.

Local ETL Tools

Companies that handle a lot of data will often have bespoke ETL tools developed to use in-house. Otherwise, they use third-party ETL tools that they install and train staff to use. This method of data discovery is typically the most expensive. However, it allows a company to develop customized data transformation processes, enabling them to transfer large amounts of data efficiently.

Cloud-Based ETL Tools

Cloud-based ETL tools enable companies to carry out all the same functionality while leveraging the infrastructure and expertise of third-party companies. This will typically be a less expensive option. It may, however, lack custom features used to transform data exactly how it’s needed.

Custom Scripts

For small amounts of data, or solving a single issue between two systems, it’s often possible to write a bit of custom script to enable the data transformation. Computer languages such as Python, SQL, and creating Macros are examples of custom scripts. This enables bespoke data transformation solutions that solve issues.

The main drawbacks are that it’s hard to scale this type of process. It’s not very flexible and prone to running into problems.

The Challenges of Data Transformation

There are huge upsides to data transformation, but like anything, it comes with some challenges. Below are the main drawbacks companies face when it comes to data transformation.

Expense

Expense is the biggest issue companies contend with when transforming data. There’s a fine line between finding an in-house solution that works well and implementing a process that’s able to scale as the data increases.

Depending on the volume and type of data, it can put immense stress on an organization’s infrastructure and resources. ETL tools come with an expense, as does training staff on how to manage data transformation processes.

Complications

Data transformation is often a complicated process. You may need to use the expertise of off-site professionals, or train staff internally. Both options present issues in the logistics of managing these tasks, and the added costs.

From the onset, finding a solution for transforming data is mostly a problem-solving exercise. Few processes are identical, so there is often the need to find a customizable solution. This might be possible with a flexible ETL tool or by developing a custom script.

Time

Cleaning and formatting unstructured data is a time-consuming process. Time is money, so this is a considerable challenge to a company undertaking data transformation tasks. There is time in the way of manpower needed to oversee the processes. And don’t forget the time it takes for machines and software to work through the process.

How to Make Data Transformation Easier and More Efficient

There are a few ways companies can improve the efficiency of their data transformation processes. The best way, in most instances, is by leveraging data transformation tools. ETL (Extract, Transform, Load) tools help with the specific problem of transforming data. Here’s how ETL tools can make data transformation more efficient.

What Are ETL Tools?

ETL tools are software that enables the user to extract data from numerous databases, systems, or applications, then transform that data into the desired format, and finally load it into a single database.

They are particularly useful when dealing with a large or diverse amount of data—enabling users to “pool” together multiple data sources and streamline it in any way they need.

How Do ETL Tools Work?

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As the name suggests, there are three steps to ETL tools: Extract, Transform, and Load. Learn more about them below.

Step 1 - Extract

First, extract all the data from as many sources as you instruct the tool to pick up. These can be from multiple sources, either homogeneous or heterogeneous, and will typically be from databases, XML files, spreadsheets, and so on.

Step 2 - Transform

This is where the primary data crunching takes place. You can set up rules and functions to clean, filter, and rearrange volumes of data. ETL tools take raw, illegible data and turn it into uniform, easy-to-read information.

Step 3 - Load

The final stage is to load the data into a location for you to pick it up. This is called data warehousing, as the information goes into a data warehouse and is stored there.

The best part about ETL tools is that they’re customizable and flexible. Once you have the software and processes in place, you can make changes to either part of the process. A little tweaking can result in the most efficient and adaptable way to transform data at scale.

Conclusion

In this digital age, data transformation has become a vital part of day-to-day operations for businesses of all sizes in one form or another. ETL tools perform data transformation tasks at scale. They save you time and money, and enable you to transform massive amounts of data with accuracy.

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