Data Transformation Process: Ultimate Guide & Tips

Think about a lock– you need the right key shape to be able to open the door. In the same way, the data transformation process is what makes data the right “shape” (or format) for end use systems. In turn, you can open the door to valuable business insights for better decision making abilities. 

This is especially necessary when it comes to a business’ finances, as it is what informs all decisions thereafter! 

We’re going to look at how data transformation techniques and automation solutions that execute them can be applied to support your business goals.

Coming Up

What is Data Transformation?

What are the Benefits of the Data Transformation Process?

What are the Stages of the Data Transformation Process?

What are the Types of Data Transformation?

What are Techniques of Data Transformation?

What are the Challenges of the Data Transformation Process?

What are the Best Data Transformation Process Tools?

How to Get Started with Data Transformation

Closing Thoughts

What is Data Transformation?

Data transformation is the process of changing data from one format into another, most commonly from one source system into a target/destination system. 

Data transformation can be simple or complex, which is determined by how much work and changes need to be made to the data before it arrives at its final destination. 

Depending on what types of data transformation process you apply, these adjustments can be performed manually, automatically, or a combination of both. 

Naturally, the use of automation software will not only hasten the timeline, but it will also provide greater accuracy as human error is inevitable, especially when handling data. 

What are the Benefits of the Data Transformation Process?

Just like having a key that doesn't match your lock will prove useless, having data without being able to utilize it is as good as not having the data at all. 

With the data transformation process, you can actually make use of the data in your hands to get the answers you seek. 

Data transformation offers an array of benefits, including: 

1. Better Data Management

As organizations scale, the growth of its data follows suit. Data transformation comes along with lineage tracking, which offers a better way to manage data. 

2. Faster Queries

When you are able to standardize and store data in a centralized location, you get the advantage of being able to quicken your query times. This means that you get the results back faster and have less friction when performing data analysis. 

3. Improved Data Quality

Data transformation also improves your data quality by removing redundancies and ensuring that data is up-to-date. As a result, you have less risk of misinterpretation. 

What are the Stages of the Data Transformation Process?

The data transformation process, be it manual or automatic, tends to flow through the same series of steps. 

These stages are as follows: 

1. Data Discovery

Before transformation or reformatting any data, it’s critical to first pinpoint the data that is to be used. This happens through data discovery, or data profiling. At this step, data teams or a data automation tool will identify the raw data that will be transformed. 

2. Data Mapping

Knowing how the data must be formatted is the only way to move forward with the process. In some cases, data analysts will have to manually list out the way the data must be filtered, modified, matched, and aggregated. 

While the manual efforts required a highly-skilled and dedicated staff, an automation software can manage this aspect for you by automatically formatting the data according to the target system’s needs. 

3. Data Extraction

Data is moved from the source to the target system through data extraction. Extraction can either be structured (through databases) or unstructured (event streaming, log files). 

4. Data Execution

After extraction and data loading in the target system, the data has to be transformed into the right format. Think of this like molding the key for the lock. 

It will either be done by analytics engineers who have to write code or by a software solution that will do this tedious work behind-the-scenes. 

5. Data Review

After the main event of the data transformation process takes place, it’s a good idea to review the data to ensure it was properly implemented and stored. 

6. Data Sending

Once all is in good order, it’s time to close out the process by moving the data into its target system, which can be a data warehouse or structured database. 

What are the Types of Data Transformation?

Before moving data into a warehouse, there are different types of data transformation that can be applied. A few of most common types are:

1. Data Mapping

Data mapping connects a field between your initial data source and target end source. For example, it may be through syncing data, time values, unifying capitalizations, etc. 

2. Data Filtering

As the most simple type of data transformation, data filtering chooses small portions of the data set to be used for analysis.

3. Data Deduplication

As the name implies, data deduplication removes any repeat data before moving it to the data warehouse or structured database. 

4. Data Aggregating

Data aggregation pulls together and summarizes data from multiple sources. 

5. Data Validation 

Data validation checks the accuracy of source data before implementing or processing the data. 

What are Techniques of Data Transformation?

With the data transformation process comes data transformation techniques that all help to structure your data and handle data cleansing before moving it to its desired target system. 

Let’s take a look at some common data transformation techniques: 

1. Aggregation

Data aggregation pulls together data from various sources and stores it in a unified format. Data aggregation is particularly useful for businesses that collect mass amounts of data. 

2. Smoothing

Data smoothing removes any irrelevant data from the dataset and helps to identify patterns and trends. 

3. Normalization

Normalization ensures that data stays within a specified range for data mining applications. 

4. Discretization

By creating interval labels, discretization transforms large datasets into smaller categories of information to streamline analysis. 

5. Manipulation

Data manipulation involves changing the data to organize it better. This helps to recognize patterns in the data. 

What are the Challenges of the Data Transformation Process?

The data transformation process is highly valuable, but it does come along with its fair share of challenges, especially when being performed manually. 

1. Errors

One of the most widely experienced hurdles of the data transformation process is errors. Errors can occur because of manual mistakes, flawed data, or corrupt data. Data transformation helps to mitigate errors by ensuring the overall quality of data and interoperability between systems. 

Automation software reduces the risks associated with data errors, as well as improving compliance and internal controls. 

2. Implementation Cost

Data transformation can be expensive, depending on the infrastructure. However, there are no-code solutions that are cost effective and robust to manage data transformation, along with other complex finance functions. 

For example, in finance low-code solutions can help manage your data and execute reconciliation, expense analysis, rebate management, APRA reporting, and more. 

3. Burdensome 

The data transformation process can be cumbersome for your human resources who manually have to do it, as well as for on-premise data warehouses that end up slowing down other operations when handling data transformation tasks at the same time. 

That’s why so many businesses are turning to cloud-based solutions that are easy to scale and quick to act. 

What are the Best Data Transformation Process Tools?

To alleviate the burden and opportunity cost of executing the data transformation process by hand, there are data transformation process tools to choose from. Based on your company’s budgets, strategy, and needs, you can select from four main groups of tools:

1. Open-Source

Open-source tools are typically free and offer code for technical infrastructure, but they require a team to develop, build, and maintain the ETL process. 

2. Enterprise-Grade

Commercial organizations sell enterprise-grade tools for companies that are looking for a ready-to-use and intuitive solution for data process transformation. 

3. Cloud-Based Platforms

Software-as-a-service cloud-based solutions deliver ETL solutions as part of their suite of solutions, enabling scalability, speed, and ease-of-use. 

4. Custom ETL Tools

For businesses that wish to create their own custom ETL tools, extensive coding knowledge and maintenance is required. As expected, this is a costly endeavor. 

When operating any type of company, it is often the finance and accounting team that gets bogged down with the influx of data (which also comes from every other department therein). 

Rather than having to manually sort through information and connect systems by hand, automation software alleviates the burden and automates the data’s centralization. 

How to Get Started with Data Transformation

Data transformation requires a long-term and defined strategy to improve the success of its implementation. To get started, it’s best to:

1. Identify Your Data

Create a list of how your data should ideally look for your target system. 

2. Assess the Differences

See the differences between how your data currently stands (in its raw format) and how it should be for the target system. List what types of data transformation is needed to get it to the end state. 

3. Make the Changes

Select the data transformation tool of your choice to apply the necessary changes. Then, you can use your data for insights! 

Closing Thoughts

The data transformation process is critical if you wish to make use of your data in a timely and efficient manner. When making business decisions, it’s of utmost importance to have finances in order as they dictate what comes next. Finance and accounting teams benefit greatly from automated data transformation processes. 

With the influx of data flowing into your business across sources, an automation tool can streamline your data transformation process, increase data accuracy, remove key person dependencies, and ensure that you adhere to all data compliance regulations.