data transformationThere’s no need to talk about how important data is in today’s work environment. This has been established and we know that no business, big or small, can function well without the presence of reliable and correct data.

Every business has to deal with a lot of data. Take a moment and look at all your available data points, from customer data to business processes, and answer the following questions.

Who has access to your data?

How do you and others use and manage this data?

The success of your business largely depends on how you transform this data into actionable form. 

We know that it can be complicated to handle data. It requires strict management protocols and standards, especially due to the presence of bad clusters of data that can render data void.

While we have tools like WinPure Clean & Match Enterprise to take care of bad data, a better option is to be careful during the selection process so that you do not have to count a lot on matching tools. 

Data management is important but it’s only a part of it. It doesn’t matter how much data you have, it will not benefit your business unless you know how to use it. This is where transformation comes into play.

In this article, we’ll talk about data transformation and why it’s very important for your business.

Related: Data Management Terms Glossary

What is Data Transformation?

Data transformation can be defined as “the mapping of existing data in one location to integrate with one or more destination systems in another. 

The role of destination systems is very important here. They reveal how the data exists in the systems, which can help you realize how to best use it.

If this definition is too complex for you then refer to how Techopedia defines the term:

“Data transformation is the process of converting data or information from one format to another, usually from the format of a source system into the required format of a new destination system.”

Data conversion may sound simple one paper – converting documents – but it may involve converting programs from one language to another so that it can work on different platforms. 

The process consists of two main phases:

Data Transformation involves two key phases:

  • Data Mapping: It involves assigning elements from the system or source back toward the destination in order to capture every transformation that occurs. The process can end up being very complicated especially when the job involves complex transformations such as one-to-many or many-to-one transformation rules.
  • Code Generation: This step involves the creation of the transformation program that can be run on different systems.

Why Use Data Transformation

Let’s talk about why we need data transformation. Companies spend a lot of money on data transformation. Here’s why:

  • Data transformation makes data more organized, which makes it easier to use and comprehend for both computers and humans. This might come as a surprise to some but computers take longer to process complicated data. Plus, the risk of errors or mistakes is also higher when it comes to raw data.
  • Properly validated and formatted data greatly improves its quality and protects software and application from potential risks and landmines like unexpected duplicates, incompatible formats, incorrect indexing, and null values.
  • Data transformation makes it easier to manage and use a variety of data. It facilitates compatibility between different systems, types, and applications. This is of huge benefit as having to use different tools to manage different types of data can make data management a costly and time consuming process. 

Based on this, we can say that data transformation helps save time and money while making the job easier and less risky.

It can process information and reveal results in ways that are easy to understand. Data transformation also helps deliver relevant and accurate communications, drive automation, improve customer satisfaction, and increase conversions. 

Data transformation is very important from a marketing perspective. Most data that businesses collect is used to improve marketing techniques, reach a wider audience, and improve conversions. 

How to Use Data Transformation

A large number of businesses turn to data scientists to help manage the process without even thinking if they really need the help of an expert. 

You may need someone who understands data and can analyze it, but it’s not always a given because there are tools out there that can automate the process.

The purpose of these apps is to allow non-IT individuals to use data. Google Analytics, for example, can answer questions like how many people visit your site, where they live, how much time they spend on your page, and what products they’re interested in.

All this information is presented in a neat manner with the help of graphs and charts that make it easy even for someone with no understanding of data to understand what’s being said.

However, big companies may have no option but to hire data transformation experts who can manage, transform, and explain data so it can be used by other departments.

Data Transformation Challenges

Data transformation is important but it comes with some challenges:

  • It can be quite expensive. We can’t always correctly gauge the cost since it depends on a number of  factors including the size of your business and the tools and software used to process information. Expenses include licensing fees, employee salaries, and computing resources.
  • Data transformation can slow down other operations as it requires several resources. This is why experts suggest to use cloud-based systems as they allow you to scale as needed.
  • Transformation may not always be ‘successful’ especially if there’s a lack of expertise. 
  • Businesses might end up performing transformations that they don’t really need. For example, a business might make changes to the format even when they already have a tool that supports the current format.

The importance of data transformation cannot be neglected. Pay attention to it and enjoy all its benefits.

Written by

Shad Hus

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