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What Is Data Transformation? Here’s Everything You Need To Know

Data transformation definition: Data transformation is the process of converting and structuring data into a new format that a business can analyze, process, and use for growth.

It’s no secret that you need data to make informed business decisions. If you want to optimize your marketing strategies, you need data to fuel your insights.

Not all data you collect can initially make sense, though. Enter data transformation.

Think of data like carbon. It may need to undergo high heat and pressure before it can turn into a precious diamond. Similarly, data needs transformation before it becomes valuable information for your business. If you want to know more about data transformation, you’re in the right place.

This blog post will go through these topics:

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What is data transformation?

Data transformation is the process of converting, cleansing, and structuring big data into a new format that a business can use to analyze, process, and aid in decision-making. Data transformation helps companies grow, and companies often use it to convert data so it matches the destination’s format.

For example, let’s say your heavy equipment rental business runs lead-generation campaigns on search and social media. Because your lead data have different sources, their formatting and fields are not the same.

In addition, reports from different marketing channels may have different terms for the same metrics. To combine performance data from both channels, you need data transformation to collate and aggregate them.

A data transformation process can be performed manually, automatically, or a combination of both.

5 benefits of data transformation

Every business needs data to better understand its customers and industry trends. In addition, data can help companies evaluate and streamline their processes.

While every business can collect data, making the data useful is challenging. That’s why data transformation is crucial in making the most out of the data collected.

Here are the five benefits of data transformation:

  1. Effective and efficient data management. It takes time and resources to organize and understand data. Data transformation helps businesses organize data to use them effectively and efficiently.
  2. Data compatibility. Do you want to ensure that different tools and departments can use the data you collected? Data transformation enables data compatibility between different data sets, applications, and platforms.
  3. Data consistency. Does your business gather data from different sources? Chances are, you face the challenge of inconsistent data. Data transformation helps you keep your data from different sources consistent.
  4. Quality data. Data transformation helps improve the quality of the data you collected.
  5. Accurate forecast. Data transformation generates data that you can use as metrics in reports and dashboards. These reports can help you understand buyers’ insights and forecast sales.

4 challenges of data transformation

While data transformation is a critical component for a business’s success in processing its wealth of data, it comes with challenges:

  1. Data transformation is expensive. The cost of a data transformation process depends on the infrastructure and other tools used. Businesses must spend on their data stack, licenses, computing resources, and talent.
  2. Data transformation uses up computational resources. When data transformation occurs in an on-premises data warehouse, it uses a lot of computational resources, thus slowing down other operations. If you use a cloud-based data warehouse, you can avoid this challenge, as the transformations can happen after loading.
  3. Data transformation can have inconsistencies. Issues may arise during data transformation, and they might result in inconsistent and incorrect data. Instead of producing high-quality data that can help businesses with decision-making, they get flawed or corrupted data that are not meaningful for the company.
  4. Businesses may perform data transformations that they don’t need. A company may need data transformation into a specific format it initially needs. Strategies and directions may pivot, though. And the ongoing data transformation processes may need to change.

5 data transformation techniques

You can clean and structure data using different tactics before you store and analyze it. Not every technique works with all types of data. In addition, you may need more than one transformation technique.

Here are five data transformation techniques you can employ:

  1. Data smoothing
  2. Data aggregation
  3. Data normalization
  4. Data discretization
  5. Attribute construction

Let’s go through each one.

1. Data smoothing

Have you ever looked at a bunch of data points that don’t seem to tell you much? To highlight important features in your data set, you need data smoothing.

Data smoothing is the process of removing noise from a data set using algorithms. It helps you see patterns more clearly as it removes out the data outliers.

Data smoothing helps predict trends and can help you with sales or seasonal forecasting.

2. Data aggregation

Data aggregation is a tactic that stores and presents data in a summary format. It is beneficial if you have more than one data source and must compile and analyze the data together.

For example, let’s say you own multiple pet stores in different locations. You can aggregate the sales performance of all your stores, so you have a monthly sales analysis report on your overall revenue.

3. Data normalization

If you want to segment and analyze your data easily, you can use data normalization.

Data normalization is the process of organizing data to have a uniform and standard way of recording them. As a result, it’s easier for you to sort, segment, and analyze your data.

Say you have a web lead form that collects a user’s first and last names. Some users may type their name in capitalized format, while others submit theirs in lowercase. Data normalization can help you standardize the format of the first and last names when you store them in your database or customer relationship management (CRM) software.

4. Data discretization

Data discretization is a data transformation tactic that converts continuous values into a set of intervals, so your data is easier to analyze and group together. This step is especially useful for customer segmentation.

For example, your retail business might need to segment your customers according to age so that you can share products relevant to each age group. You can use an age range for each contact instead of their unique age numbers.

5. Attribute construction

Do you need to create new attributes based on existing data points? Use attribute construction, which is the process of creating new attributes from your existing data.

Attribute construction helps make data mining more efficient. It also enables you to create new data sets your team needs for additional insights.

For example, let’s say your manufacturing business has data about your customers’ transactions. You can use this data transformation tactic to create a new attribute of customer lifetime value.

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Improve your processes and grow your business with data transformation

Data transformation in data mining can help you analyze your processes and ultimately improve your bottom line. If you need help with data transformation, WebFX can help.

By teaming up with WebFX, data transformation is made easy. We provide marketing analytics services to help you improve your processes, strategies, and bottom line. We’ve helped over 2,000+ customers generate $6 billion revenue, and we’re pumped to deliver the same results for your business.

Contact us online or call us at 888-601-5359 to speak with a strategist about our marketing analytics services!

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