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Your Guide to Data Transformation Techniques

Analyzing data can be a mundane but critical task for decision-making, as your data can help you make informed decisions and look at the case from every aspect.

To make your life easier, you need to make sure that the data is properly configured. You don’t want to end up with wrong insights just because the data was not in the appropriate format or there are inconsistencies within your dataset.

In this guide, we’ll go through all the different data transformation techniques you can use to ensure your dataset is clean and ready to be analyzed.

What are data transformation techniques?

Data transformation techniques refer to all the actions that help you transform your raw data into a clean and ready-to-use dataset.

There are different types of data transformation techniques that offer a unique way of transforming your data and there is a chance that you won’t need all of these techniques on every project. Nevertheless, it’s important to understand what each technique can offer so you can choose what’s best for your case. 

In the next section, we will describe each and every one of these data transformation techniques to help you understand what they refer to and what’s the best way to use them.

Different types of data transformation techniques

01 data transformation pipeline

There are 6 basic data transformation techniques that you can use in your analysis project or data pipeline:

  • Data Smoothing
  • Attribution Construction
  • Data Generalization
  • Data Aggregation
  • Data Discretization
  • Data Normalization

Data Smoothing

Smoothing is a technique where you apply an algorithm in order to remove noise from your dataset when trying to identify a trend. Noise can have a bad effect on your data and by eliminating or reducing it you can extract better insights or identify patterns that you wouldn’t see otherwise.

There are 3 algorithm types that help with data smoothing:

  • Clustering: Where you can group similar values together to form a cluster while labeling any value out of the cluster as an outlier.
  • Binning: Using an algorithm for binning will help you split the data into bins and smooth the data value within each bin.
  • Regression: Regression algorithms are used to identify the relation between two dependent attributes and help you predict an attribute based on the value of the other.

Attribution Construction

Attribution construction is one of the most common techniques in data transformation pipelines. Attribution construction or feature construction is the process of creating new features from a set of the existing features/attributes in the dataset.

Imagine working in marketing and trying to analyze the performance of a campaign. You have all the impressions that your campaign generated and the total cost for the given time frame. Instead of trying to compare these two metrics across all of your campaigns, you can construct another metric to calculate the cost per million impressions or CPM.

02 feature extraction

This will make your data mining and analysis process a lot easier, as you’ll be able to compare the campaign performance on a single metric rather than two separate metrics.

Data Generalization

Data generalization refers to the process of transforming low-level attributes into high-level ones by using the concept of hierarchy. Data generalization is applied to categorical data where they have a finite but large number of distinct values.

This is something that we, as people, are already doing without noticing and it helps us get a clearer picture of the data. Let’s say we have 4 categorical attributes in our database:

  1. city
  2. street
  3. country
  4. state/province. 

We can define a hierarchy between these attributes by specifying the total ordering among them at the schema level, for example:

street < city < state/province < country.

Data Aggregation

Data aggregation is possibly one of the most popular techniques in data transformation. When you’re applying data aggregation to your raw data you are essentially storing and presenting data in a summary format.

This is ideal when you want to perform statistical analysis over your data as you might want to aggregate your data over a specific time period and provide statistics such as average, sum, minimum and maximum.

In the above example, we aggregated our daily temperature data so we can see the average temperature for each particular month and be able to find which month has the minimum average temperature in a glance of an eye.

Data Discretization

Data discretization refers to the process of transforming continuous data into a set of data intervals. This is an especially useful technique that can help you make the data easier to study and analyze and improve the efficiency of any applied algorithm.

Imagine having tens of thousands of rows representing people in a survey providing their first name, last name, age, and gender. Age is a numerical attribute that can have a lot of different values. To make our life easier we can divide the range of this continuous attribute into intervals.

Mapping this attribute to a higher-level concept, like youth, middle-aged, and senior, can help a lot with the efficiency of the task and improve the speed of the algorithms applied.

Data Normalization

Last but not least, data normalization is the process of scaling the data to a much smaller range, without losing information in order to help minimize or exclude duplicated data, and improve algorithm efficiency and data extraction performance.  

There are three methods to normalize an attribute:

  • Min-max normalization: Where you perform a linear transformation on the original data.
  • Z-score normalization: In z-score normalization (or zero-mean normalization) you are normalizing the value for attribute A using the mean and standard deviation.
  • Decimal scaling: Where you can normalize the value of attribute A by moving the decimal point in the value.

Normalization methods are frequently used when you have values that skew your dataset and you find it hard to extract valuable insights.

Other ways of transforming data

The techniques we went through in the previous sections are considered to be the standard data transformation techniques used in almost every analytics project.

In addition to the above, there are two other ways you can transform your data to be able to analyze it and extract valuable insights.

Data Integration

Data integration is not a data transformation technique but rather a critical step during the pre-processing phase.

Data integration is the process of combining data from multiple sources creating a unified view of the data. These sources can be:

  • Traditional databases
  • Data warehouses
  • Simple CSV or Excel files
  • Exports from popular tools

Many third-party tools can help with your data integration needs and is one of these tools. With you can extract and combine data from multiple sources, such as Pipedrive, HubSpot, or Salesforce, perform data stitching, and load the data into Google Sheets, Microsoft Excel, or Google BigQuery. Then it’s easy to apply any of the aforementioned data transformation techniques to bring your dataset to the appropriate format. And the best part here is that you can automate exports on a schedule you want!

2. Pipedrive importer Schedule

Data Manipulation

Data manipulation refers to the process of making your data more readable and organized. This can be achieved by changing or altering your raw datasets.

Data manipulation tools can help you identify patterns in your data and apply any data transformation technique (e.g. attribution creation, normalization, or aggregation) in an efficient and easy way.

What technique to use for your data transformation needs

In this article, we went through 6 different data transformation techniques that you can use in your analytics project. These techniques can help you bring your raw data into the appropriate format for quality analysis.

Each of these techniques provides a unique benefit and is applicable in specific circumstances. Before applying a specific technique you will need to think and evaluate what each one can bring to the table and what your raw dataset requires. You can then decide which technique is best for your case study and proceed with the required actions before analyzing your data!

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