In 2017, The Economist published an article titled “The world’s most valuable resource is no longer oil, but data.” The article is a landmark in ushering in the data-driven world we’re inhabiting today.
This observation launched an onslaught of “Data Is The New Oil” pieces. Many of these articles take a negative outlook on how businesses are making use of the data they harvest.
The “data-as-oil” metaphor is more apt than it might seem at first glance. Data is only useful when it’s refined. Otherwise, it’s just a hot, sticky, crude bubbling mess.
Data transformation is one technique that business owners and software developers are using to help get data into a usable format. Let’s find out more about data transformation, shall we?
What Is Data Transformation?
Data transformation could be described using the motto of the board game Othello. It only takes a minute to learn, but it could take numerous lifetimes to master.
On a very basic level, data transformation is simply the act of converting data from one format to another. That’s very much an oversimplification. It also overlooks much of what data transformation is capable of, as a result.
Data transformation is an essential component of data warehousing and data wrangling. It’s also an essential component of getting applications to cooperate by allowing data to be moved from one format to another. You might use data transformation to convert JSON to PDF, for instance.
Data transformation is more than simple file conversion, also. It can encompass a wide array of different actions that can be worked upon data. There are also a lot of different reasons someone might want to do so.
The Process of Data Transformation
Now that you have a clearer understanding of what data transformation is, let’s take a look at how it’s achieved.
Step 1: Understanding The Data
Data transformation requires knowing your data. You have to understand the data you’re working with if you hope to make sense of it. You also need to understand what you’re trying to transform your data into.
Understanding your data can be more complicated than it seems at first glance. Data science is becoming more prevalent in the digital world for a reason. Translating raw data into a usable, useful form can be an arcane art unto itself.
As a general rule of thumb, a computer system looks at the file format to interpret the form that data comes in. If a file has a ‘.avi’ extension, your computer is going to interpret that file as a video.
This can cause problems when the data doesn’t line up with the file format. This means that data transformation tools often need to peek under the hood of a file, so to speak. This can be trickier than it sounds, at times.
Step 2: Transforming The Data
Once you have a clearer visualization of the data you’re working with, and what you’d like to do with it, it’s time to start transforming your data in earnest. This can be done in a few ways.
The first method is writing data transformation scripts by hand. This data transformation code is often written in languages like Python or SQL. It’s often undertaken by third-party or in-house developers.
The problem with coding data transformation by hand is its inflexibility. It’s often not rugged or robust. Minute changes to your file or data structures can bring the whole thing toppling down instantly.
Online and cloud-based data transformation solutions help prevent many of these problems. They usually feature some sort of dashboard, for one thing. This means you don’t have to know how to code to make the necessary adjustments.
Drag-and-drop solutions are a major lifesaver, even for data scientists. Even if you know the difference between JSON vs XML, it can be hard to keep all the details straight. That doesn’t even consider all of the new file and data formats that are constantly emerging.
Types Of Data Transformation
Now that we know a bit more how data transformation works, let’s take a look at some of the different types you see most commonly.
Data joining is very common in multichannel data situations. It involves bringing data from multiple disparate sources into one central destination. It’s an essential component of data visualization, comparison, and creating rich data for analytics.
Cleaning data one of the most important steps of making data actionable and understandable. This means removing duplicate entries, for instance. This can be much trickier than simply removing files with the same now, however.
The deduplication process compares incoming data against a record of the files already stored in the database. The duplicate file is deleted but there’s a record made of the transaction.
The same algorithms are used to monitor outgoing data. Not only does this help to eliminate wasted space, it also speeds up file transfers.
Every database is different. This means that the headers and categories are going to vary from spreadsheet to spreadsheet, as well. Getting all of the categories to agree with one another is called ‘key restructuring.’
Imagine you have a customer’s phone number listed as their primary contact method in one database. This column might be called ‘Primary.’ Perhaps you might have their email address listed in a column titled ‘Contact.’
You would run into a problem when you go to merge this file with another spreadsheet where ‘Contact’ was the name for the primary contact information. This could lead to your customer being contacted via email instead of by phone. This could result in some mild irritation, at best, and a lost customer in the worse case scenario,
The world is only going to keep getting more data-driven as the years progress. This means that our databases are going to keep getting bigger and more complex, as well. Implement data transformation into your workflow now and get into the swing of things, before you have to start dealing with petabytes.
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