What is data transformation?: Definition, use cases & automation
Data processing and collecting have undergone a rejuvenation over the past decade. And the analytics team now has access to more data than they ever did. But the quality of data is questionable.
46% of CPOs said that low data quality makes it challenging to apply digital technology. ~Statista
Working with and filtering raw information can take time and effort. The issue is not how to collect additional data but which data to manage and process. Data transformation comes to the rescue here.
Data transformation gathers relevant, valuable data and makes it accessible across many platforms. Let's see how it works, why you should use it, and how to automate it easily.
What is Data Transformation?
Data transformation is changing information from one format or style into another. This can involve data cleansing and normalization, primitive data conversions, and key-value mapping.
Data transformation is required structured and uniform data. That can be accessed rapidly and efficiently by the techniques and algorithms deployed to it.
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Try Nanonets to automate every aspect of data processing with no-code workflows in 15 minutes!
Why Should You Use Data Transformation?
Data can impact a company's revenue and growth if used efficiently. Data can be used to perform predictive analytics to understand customer behavior, forecast product demand, identify financial trends, or even make data-driven strategic decisions. But, again, this happens only when data is valid, uniform, and normalized. That's when data transformation comes here.
Let's look at some benefits of using data transformation in your organization:
- Data transformation standardizes the information and enhances its readability and accessibility. Organizations can fully realize the total worth of the record they have accumulated with data transformation
- Data transformation homogenizes the data making it easier to comprehend and manage.
- Data transformation helps in identifying and handling missing or inconsistent data entries. It inputs or interpolates missing data and flags outliers.
- Data transformation reduces data anomalies, noisy data, and variability. This ensures a more robust & accurate analysis.
All of this helps in making data usable and ready for further analytics.
Looking for a no-code data transformation platform?
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How to do Data Transformation? - Step-by-Step Approach
Data transformation follows some simple steps. It's advised to perform data transformation after data cleaning. You should add or resolve null values, remove contradictions and remove duplicate entries.
After cleansing, you can proceed with data transformation using the following steps:
Step 1: Data Exploration
The first step in data transformation is understanding the sources of data. Identify the sources from where your data is pouring in. Understand the structure of the data flowing in your database, the variables, and the possible missing data points in the incoming data.
Make a list of all the data points that need to be transformed. This planning helps in giving a structure to this entire process.
2. Data Mapping & Profiling
Data mapping serves as a route map for data relocation. It determines how you can proceed with data merger, retention, and transformation.
With data mapping, you can eliminate discrepancies and mistakes. At this point, you decide which data changes and which points should remain. Data is also verified to ensure its reliability and authenticity.
3. Execute Your Data
Determine how you might change your information at this point. Will you employ a technique for data transformation or a manual script? Data extraction from various sources and diverse data processing techniques, including:
- Integrating: Combining or linking information from multiple sources.
- Filtering: Carefully separating specific columns from rows. Thus, you keep some entries out of a database and delete others.
- Enriching: Updating the structure of assets is enriching. For instance, changing a name's typeface from lowercase letters to uppercase letters would be lee rang to Lee Rang.
- Split: Convert rows into multiple columns by dividing them.
- Summarization: Creating a synopsis will save the information as key metrics. For instance, total installations broke down by race, geography, or socioeconomic position.
- Derivation: Creating new information pieces from current ones by applying rules or algebraic modifications.
- Binning: Minimizes the consequences of minor observational inaccuracies. We substitute data indicative of extensive spans for the initial values symbolized by a small interval (bin).
- Deleting Redundant Data: When converting the content, consider if the information's format will shift over time and if you can modify it quickly to accommodate changing requirements. Make it simple to grasp for others so others may use it without you.
4. Migrate processed data
Once data is transformed, you can migrate the data to the required location (Salesforce, Google Sheets or Drive).
Check the processed data to confirm its accuracy and reliability. Make a list of any problems, then address them as needed.
Are you looking to automate data processes?
Automate data tasks like cleaning, extraction, parsing, and more with Nanonets' no-code workflow platform for free. You can contact our team to set up a complex use case if you have a complex use case.
How to automate data transformation?
You can easily automate every aspect of data transformation with platforms like Nanonets. Nanonets is an AI-based no-code workflow automation platform that automates data transformation for data extracted from documents.
With Nanonets' workflows, your data transformation will look like this:
Data get automatically uploaded on the platform. Any new data (email, URL, documents, attachment, etc) gets uploaded on Nanonets.
Nanonets proceeds with data processing. First, it extracts the relevant data, classifies, verifies, and then wrangles it using the rules set by you on the workflows. After the previous step, your data is cleansed, normalized, and ready for efficient data merging.
Before migrating, you can run automated tasks like approval workflow, data matching, etc
Nanonets connects with 5000+ business applications so you can sync data across the business applications of your choice.
This is how data automation platforms simplify data transformation easily.
Nanonets for Data Transformation
Nanonets is a flexible workflow automation platform with in-built OCR software. Nanonets can automate complex manual business processes like data collection, wrangling, and more in minutes with the no-code workflow management system.
Nanonets allows enterprises to use their document data effectively by extracting data from documents on the go and making data ready for further advanced analytics. With Nanonets, you can load, extract and transform on a single platform using various drag-and-drop tools.
Data Transformation for Enterprises
Enterprises receive information from customers, business documents, sales, markets, and more. Every data source contains different aspects of customer experience. Bringing them all together requires transforming data points for enhanced data unification.
Data transformation becomes essential in this situation. The appropriate transformation techniques will yield excellent results and help you make the most of your data. Here are many reasons why enterprises should go after data transformation:
- Improved data quality: Data transformation helps cleanse, standardize, and validate data. This leads to improved data quality and accuracy.
- Better decision-making: Data transformation converts data into a more usable format. Businesses can gain insights and make data-driven decisions more quickly and effectively.
- Increased efficiency: Data transformation can automate and streamline processes. This reduces manual data entry and minimizes errors.
- Cost reduction: Data transformation eliminates data silos and inconsistencies. This can help organizations save on operational costs.
- Increased competitiveness: Businesses can gain a competitive edge with better data insights. They can make better strategic decisions with proper datasets.
- Compliance: Data transformation makes organizations compliant. This includes regulations such as GDPR, HIPAA, and others.
- Improved customer experience: With a single source of truth and accurate data, businesses can enhance customer experience
What are the Best Practices for Data Transformation?
You can employ the following best practices for data transformation in your workplaces.
1. Set an objective
Select a designated target when beginning the data transformation process. Involve the consumers to comprehend better the procedures you will be analyzing.
They feel invested in the procedure and responsible for the outcomes. You can discover the necessary data throughout conversion if you know the intended target and the analysis you will perform ahead.
2. Data profiling
Analyze your information to determine the basic information's state before converting. It also demonstrates how much work is required to prepare it for the change.
The volume of information you would be dealing with, the attribute values, row headings, variety of matters, section associations, the number of columns and regularity of garbage, and deleted and redundant data are all things you should be aware of before transforming.
3. Data cleaning
Purify your information following data profiling before relocating it to make it usable. To make the required adjustments, you must understand what type of formats your intended target supports.
Early deduplication ensures that your official results are of the highest quality and support more appropriate choices. Talk with your team members to determine how to fill in the blanks or exclude the records in the event of missing or inaccurate data.
4. Data modification
Storage facilities that might impede collected data are degraded when data is converted to the intended location structure. For valuable insights to emerge, different data must have time to combine.
Analysts have more time to cope with other problems when the data they are working with is more consistent.
5. Handle Facts & Dimension Tables
Set two tables, one for each type of transformed data: dimension tables and fact tables. Set dimensions first while converting so you can connect them here to facts. If you want to relate client, item, and time data to sales figures, for instance, load those first.
6. Evaluating data integrity
Using a monitoring audit, you can track the data you upload throughout each stage and the moment it occurs. An inspection ensures there are no redundant or empty data points and that data is correctly structured.
Whenever a customer has a concern, this makes certain you can explain where every piece of data originated from. Additionally, it confirms the accuracy of the statistics. Accurate data supported by evidence increases trust in the information and enhances interaction with end users.
Are you looking to automate data processes?
Automate data tasks like cleaning, extraction, parsing, and more with Nanonets' no-code workflow platform for free. You can contact our team to set up a complex use case if you have a complex use case.
Conclusion
Businesses that use data effectively gain the upper hand in everything. Data makes understanding customers, numbers, markets, and easier. But data in raw form is not useable. What makes it better is data transformation.
In this blog, we saw how data transformation could be used and automated. Using ETL tools like Nanonets simplifies business tasks, enhances data security, and reduces manual errors. In case, you're looking to automate document data processes, try Nanonets or reach out to our team so we can set up entire process for you.
In case, you have another use case in mind, please reach out to us. We can help you automate data extraction, processing, and archiving using no-code workflows at a fraction of the cost.
Read more about data processing on Nanonets:
- How to improve data insights with data aggregation?
- Improve data consistency with efficient data matching
- Turn raw data into structured data with data enrichment
- Find the best data extraction tool in 2023
- Eliminate data inconsistencies with data wrangling
- Improve data quality with efficient data classification
- Best data migration tools in 2023
FAQs
How Does Data Transformation Work?
Most businesses use cloud-based database systems to grow their computing and storage capacity. Due to the enormous scale that is possible, cloud-based enterprises can omit the ETL procedure.
Instead, they employ an extraction, loads, and transform (ELT) process that transforms the information as the actual data is submitted. Data transformation could be done manually, automatically, or by combining both methods.
Data transformation is crucial in several processes, including data integration, migration, warehousing, and wrangling. Data transformation can take the following forms:
- Constructive: The data conversion method that accumulates, duplicates, or repeats.
- Destructive: The program can delete columns and data records.
- Aesthetic: The information is standardized through the transformation to adhere to specifications or guidelines.
- Structural: By rebranding, relocating, or merging sections, the dataset is reorganized.
What is data transformation automation?
Data transformation operations should be streamlined and mechanized. To enhance productivity, decrease mistake rates, and facilitate more complex data analysis types. Several methods and technologies can be used, including computer languages, information management systems, and machine learning frameworks.
Data changes usually include data filtration, data summarization, and data structuring. It can be performed manually, automatically using accessible and paid tools, or both manually and automatically. Many tools may be used to hurry the transformation, making it easier to control and scale.
Additionally, modifications combine and evaluate the data, conduct web searches, and transport data to various locations. To alter data in the most efficient manner possible, it is advantageous to possess data transformation software. That offers a wide variety of conversion possibilities to modify data in the most efficient way possible.
What are the Different Use Cases of Data Transformation?
In big data metrics, data transformation performs a much more significant and complex function. That is due to the likelihood that you will run into scenarios where a considerable volume of information is. Must be converted from one style to another while working with substantial amounts of data, various information analytics techniques, and various storage systems.
Thus, that is a general explanation of data processing. Let's look at real-world use cases of data transformation to clarify the idea better.
Speech-to-Text Conversion
When you wish to convert a human voice, which is recorded in an mp3 recording, into text, the process is known as a data transformation. Although possible, opening an audio recording as a text document will be inappropriate for the audio recording.
You need to convert the voice in the sound version into a text document. To make the valuable information useful for people who cannot hear it. Or convert it into a form that enables it to be processed by software that reads text.
The content in the recording might be individually transcribed by hearing it. Or you may automate the task by using a speech-to-text application. You should employ the adaptive algorithm if converting data on a vast scale.
Since it entails more than simply handling discrepancies in data format, this instance may not be one of the first that data transformation experts think of.
Transformation From CSV To XML
There are two standard formats for data storage: CSV and XML. But how they operate is very distinct.
Punctuation is used in CSV files to separate the various data elements from one another. In XML, labels that specify many data types and their contents are the primary means of representing data. Data structures can also be described using any file, although they accomplish this in various ways.
A program created to read and store information in the CSV format often needs help accessing an XML document and vice versa. Since CSV and XML operate independently, data transformation becomes helpful in this situation.
One may switch data from the CSV format into an XML file. That can be opened with the appropriate tools by employing a data transformation tool.
Letter Encoding
Data transformation is required as a result of standardized coding issues. You must first comprehend the purpose of letter decoding to understand why.
Systems can represent certain letters using character encoding, which uses codes. In other words, any letter of the alphabet can serve as a coding and an extra text character like commas and periods. Your system can decode data to convert it into numbers and symbols and display them on your display as soon as it knows the specific coding employed to encode the characters in a particular data collection.
Character decoding causes issues because, occasionally, one program will encode letters only using an encryption algorithm. And then send that data to another program, which by default utilizes various encryption methods. The second version could not understand every letter when it attempted to view the data.
