Want to organize, merge, standardize, and format large datasets to extract business intelligence? Read this ultimate guide on data transformation in the ETL process.
Companies rarely get data in the format that your business intelligence (BI) tools can utilize. Usually, data connectors and repositories bombard you with raw and unorganized data. You can not extract any pattern from such raw data.
You need a specialized process, such as data transformation, to structure the data to match your business needs. It also unveils the business opportunities that inaccurate datasets hide from your sight.
In this article, we will discuss data transformation from the ground up. After reading, you will grow professional knowledge on this subject and can successfully plan and execute data transformation projects.
What Is Data Transformation?
Essentially, data transformation is a technical step of data processing where you keep the essence and content of the data intact and modify its appearance. Mostly, data scientists perform modifications in the following parameters:
The result is clean data in an organized format. Now, the final format and structure will depend on the BI tool that your business uses. Also, the formatting may vary from department to department as different business sections, like accounts, finance, inventory, sales, etc., have varying structures for input data.
During this data modification, data scientists also apply business rules to data. These rules help business analysts extract patterns from processed data and the leadership team make informed decisions.
Furthermore, data transformation is the phase where you can merge different data models into one centralized database. It helps you to draw comparisons between products, services, sales processes, marketing methods, inventory, company expenditure, and more.
Types of Data Transformation
#1. Data Cleaning
Through this process, people identify incorrect, inaccurate, irrelevant, or incomplete data sets or their components. Afterward, the data can be modified, replaced, or deleted to increase accuracy. It relies on careful analysis so that the resulting data can be used to generate meaningful insight.
#2. Data Deduplication
Any duplicate data entry can cause confusion and miscalculations in the data mining process. With data deduplication, all redundant entries of a dataset are extracted, so the datasets are free for duplications.
This process saves money a company might have needed to store and process duplicate data. It also prevents such data from affecting performance and slowing query processing.
#3. Data Aggregation
Aggregation refers to collecting, searching, and presenting data in a concise format. Companies may perform this type of data transformation to gather from multiple data sources and join them into one for data analysis.
This process is highly useful when making strategic decisions on product, operations, marketing, and pricing.
#4. Data Integration
As the name suggests, this type of data transformation integrates data from different sources.
Since it combines the data related to different departments and provides a unified view, anyone from the company can access and use the data for ML technology and business intelligence analysis.
Moreover, it is considered a major element of the data management process.
#5. Data Filtering
These days, companies have to deal with an immense volume of data. However, not all the data is required in all the processes. For this reason, companies need to filter the data sets to get refined data.
Filtering keeps any irrelevant, duplicate, or sensitive data away and separates what you need. This process allows businesses to minimize data errors and generate accurate reports and query results.
#6. Data Summarization
It means presenting a comprehensive summary of generated data. For any process, raw data is not suitable at all. It can contain errors and might be available in a format that certain applications can not comprehend.
For these reasons, companies perform data summarization to generate a summary of the raw data. Thus, it becomes easier to access the trends and patterns of the data from its summarized version.
#7. Data Splitting
In this process, the entries of a data set are divided into different segments. The main purpose of data splitting is to develop, train and test the data sets for cross-validation.
Besides, this process can protect mission-critical and delicate data from unauthorized access. By splitting, companies can encrypt sensitive data and store it on a different server.
#8. Data Validation
Validating the data you already have is also a kind of data transformation. This process involves cross-checking data for its accuracy, quality, and integrity. Before you want to use a data set for further processing, validating it is essential to avoid issues in the latter stages.
How to Perform Data Transformation?
Choosing a Method
You can use any of the following data transformation methods depending on your business needs:
#1. On-Site ETL Tools
If you need to handle huge datasets regularly and also need a bespoke transformation process, then you can rely on on-site ETL tools. They run on robust workstations and can process larger data sets quickly. However, the cost of ownership is too high.
#2. Cloud-Based ETL Web Apps
Small, medium, and startup businesses mainly rely on cloud-based data transformation apps since these are affordable. Such apps are suitable if you are preparing data once a week or a month.
#3. Transformation Scripts
If you are working on a small project with relatively smaller datasets, then it is good to use legacy systems like Python, Excel, SQL, VBA, and Macros for data transformation.
Choosing Techniques to Transform a Dataset
Now that you know which method to choose, you need to consider the techniques you want to apply. You can choose a few or all from the below depending on the raw data and the final pattern you are looking for:
#1. Integrating Data
Here, you integrate data for one element from different sources and form a summarized table. For example, accumulating customer data from accounts, invoices, sales, marketing, social media, competitors, websites, video sharing platforms, etc., and forming a tabular database.
#2. Data Sorting and Filtering
Sending raw and unfiltered data to a BI app will only waste time and money. Instead, you need to filter out garbage and irrelevant data from the dataset and only send a chunk of data that contains analyzable content.
#3. Data Scrubbing
Data scientists also scrub raw data to weed out noise, corrupted data, irrelevant content, erroneous data, typos, and more.
#4. Dataset Discretization
Especially for continuous data, you need to use the discretization technique to add intervals between large chunks of data without changing its continuous flow. Once you give a categorized and finite structure to continuous datasets, it becomes easier to draw trends or calculate long-term averages.
#5. Generalization of Data
It is the technique of converting personable datasets to impersonal and general data to comply with data privacy regulations. Furthermore, this process also transforms large datasets into effortlessly analyzable formats.
#6. Removing Duplicates
Duplicates can force you to pay more as data warehousing fees and also distort the final pattern or insight. Hence, your team needs to scan the entire dataset meticulously for duplicates, copies, etc., and exclude them from the transformed database.
#7. Creating New Attributes
At this stage, you can introduce new fields, column headers, or attributes to make your data more organized.
#8. Standardization and Normalization
Now, you need to normalize and standardize your datasets depending on your preferred database structure, usage, and data visualization models. Standardization ensures that the same dataset will be usable for every department of the organization.
#9. Data Smoothing
Smoothing is the removal of meaningless and distorted data from a large dataset. It also scans the data for out-of-proportion modifications that might deviate the analytics team from the pattern they expect.
Steps to a Transformed Dataset
#1. Data Discovery
In this step, you understand the dataset and its model and decide which changes are necessary. You can use a data profiling tool to get a sneak peek into the database, files, spreadsheets, etc.
#2. Data Transformation Mapping
In this phase, you decide many things about the transformation process, and these are:
Which elements require reviewing, editing, formatting, cleansing, and changing
What are the reasons behind such transformations
How to achieve these changes
#3. Generating and Executing Codes
Your data scientists will write data transformation codes to execute the process automatically. They could use Python, SQL, VBA, PowerShell, etc. If you use any no-code tool, you need to upload raw data to that tool and indicate the changes you want.
#4. Review and Load
Now, you need to review the output file and confirm whether or not the appropriate changes are there. Then, you can load the dataset to your BI app.
Benefits of Data Transformation
#1. Better Data Organization
Data transformation means modifying and categorizing data for separate storage and easy discoverability. So, both humans and applications can use the transformed data easily as it is organized in a better way.
#2. Improved Data Quality
This process can also eliminate data quality issues and reduce the risks involved with bad data. Now, there are fewer possibilities for misinterpretation, inconsistencies, and missing data. As companies need accurate information for successful outcomes, transformation is crucial for making a major decision.
#3. Easier Data Management
Data transformation also simplifies the data management process for the teams. Organizations that deal with a growing amount of data from numerous sources need this process.
#4. Broader Use
One of the biggest benefits of data transformation is it lets companies make the most out of their data. The process standardizes that data to make them more usable. As a result, companies can use the same set of data for more purposes.
Additionally, more applications can use the transformed data as these have unique requirements for data formatting.
#5. Less Computational Challenges
Unorganized data might lead to incorrect indexing, null values, duplicate entries, etc. By transforming, companies can standardize the data and reduce the chance of computational errors that the applications can make during data processing.
#6. Faster Queries
Data transformation means sorting the data and storing it in an organized manner in a warehouse. It results in high query speed and optimized use of BI tools.
#7. Reduced Risks
If you use inaccurate, incomplete, and inconsistent data, decision-making and analysis become hampered. Once the data goes through the transformation, it becomes standardized. Thus, high-quality data reduces the chance of facing financial and reputational losses from inaccurate planning.
#8. Refined Metadata
As enterprises have to deal with more and more data, data management becomes a challenge for them. With data transformation, they can skip the chaos in metadata. Now, you get refined metadata that will help you manage, sort, search, and use your data.
DBT is a workflow for data transformation. It can also help you centralize and modularize your data analytics code. Not to mention, you get other tools for data management, like versioning datasets, collaborating on transformed data, testing data models, and documenting queries.
Qlik minimizes the complexity, cost, and time of transferring large data from sources to destinations like BI apps, ML projects, and data warehouses. It uses automation and agile methodologies to transform data without hectic manual coding of ETL codes.
Domo offers drag and drop interface for SQL database transformations and makes data merging effortless and automatic. Moreover, the tool makes data easily available for different teams to analyze the same data sets without conflict.
EasyMorph relieves you from the painstaking process of data transformation using legacy systems like Excel, VBA, SQL, and Python. It offers a visual tool to transform data and automate when possible for data scientists, data analysts, and financial analysts.
Data transformation is a crucial process that can un-hide outstanding value from the same sets of data for different business sections. It is also a standard phase in data processing methods like ETL for on-site BI apps and ELT for cloud-based data warehouses and data lakes.
The high-quality and standardized data you get after the transformation of data plays a vital role in setting up business plans like marketing, sales, product development, price adjustments, new units, and more.
In the information age, data centers collect large amounts of data. The data collected comes from various sources such as financial transactions, customer interactions, social media, and many other sources, and more importantly, accumulates faster.