×

Data Transformation: from Basics to Advanced Techniques

Discover how Data Transformation can revolutionize your business analytics and decision-making. Learn everything from definitions to real-world applications, tools and best practices

Articles November 22, 2023

In the realm of technology, data stands in various forms and sizes. With enterprises using varied software products for different purposes, integrating data in many formats becomes challenging. This is where data transformation comes in.

Data transformation is a crucial component of data integrations like a ETL and ELT. During the data extraction, the format from different sources may still vary. But after the data transformation, it is in a unified form useful to enterprises for making data-driven decisions.

The "T" (Transform) part of ETL and ELT is the focal point of this article.

What Is Data Transformation?

Data transformation, at its core, is the process of systematically reshaping, cleaning, and enriching data. It readies data for analysis, reporting, or integration into other systems. In short, it is the process of converting data from one format to another.

Data transformation schema

In the above diagram, data transformation takes raw data. Then, it applies various operations and serves them in a brand-new format. These operations can involve converting data types and standardizing formats, or even merging data from different sources. In data pipelines, it happens in the middle of ETL or at the final stage of ELT.

The following section will showcase real-world scenarios where transforming data is crucial.

Data Transformation Examples

Data transformation, often depicted as complex, is better understood through real-world scenarios. Let’s have a few practical examples:

  1. Currency Conversion: This happens in an e-commerce website with shoppers paying in different currencies. To ensure consistent analysis and reporting, sales data undergoes data transformation. Using foreign exchange rates, payments are converted into a common currency.
  2. Date Formatting: An online travel agency receives booking data from around the world with different date formats. To ensure accurate booking records, data transformation standardizes dates.
  3. Data Aggregation: A retail chain collects sales data from various stores. Data transformation aggregates this information into useful reports.
  4. Customer Segmentation: An email marketing platform gathers data on subscribers. Data transformation categorizes customers by behavior, allowing targeted marketing campaigns.
  5. Data Cleaning: In a helpdesk system, clients may file repeated support tickets causing duplicate records. So, to ensure clean and accurate data, transformation removes the duplicates.
  6. Data Enrichment: A healthcare provider enhances patient records with demographic data. By categorizing information into different demographics, personalized care is easier to offer. The categorization is done using data transformation.

Each of these examples highlights data transformation’s power over data. Ultimately, it leads to better business decisions. But its significance goes beyond these scenarios.

The next section will discuss why transforming data is important in today’s data-driven world.

Why Data Transformation Matters

In the age of data, the importance of data transformation cannot be overstated. Here’s why it matters:

Data Consistency

Data transformation ensures that data is consistent and follows a common format. Going back to currency conversions, data transformation will make sure reports make sense by using a common currency.

Better Decision-Making

Transformed data streamlines information, enabling swifter and more accurate decision-making. It equips a company with clearly structured reports for swift and informed strategic choices.

Integration Flexibility

It plays a pivotal role in integrating data, much like adaptable software accommodating various file formats without compromising functionality or efficiency.

Data Quality

The process ensures the elimination of errors, inconsistencies, and duplicates. This will standardize procedures that diminish data discrepancies, ultimately fortifying its information reliability.

Cost Savings

Automating the transformation process minimizes manual labor and associated costs. It streamlines operation reducing time and financial resources for data formatting tasks.

Imagine having different CSVs with the same purpose. Manually formatting them to have a common format has cost implications. Automating the process with an efficient transformation will save time (and costs). It leads to more efficient operations, reducing manual work and costly errors.

Competitive Advantage

Businesses proficient in data integration gain a competitive edge. A company with an innovative approach to data management will outshine its peers in a crowded market.

Innovation

Data transformation unlocks innovation potential by enabling data-driven experimentation and adaptation, leveraging transformed data to derive novel insights and approaches.

Understanding the critical role of transforming data, the subsequent section will delve into the intricacies of this process, unraveling its methodologies and mechanisms to clarify this integral aspect of data management.

What Is the Data Transformation Process?

The data transformation process operates as a structured series of steps. The fundamental steps include:

  1. Data Discovery: This first step involves identifying and comprehending various data source formats. It also establishes the foundation to align data into the desired output format.
  2. Data Cleaning: This phase involves removing data errors, duplicates, and inconsistencies to ensure high-quality information. Data integrators often spend a lot of time making sure they will process clean data for accurate results.
  3. Data Mapping: It focuses on the correlation of diverse data sources to fit the target format. This phase will determine if a piece of information will transfer correctly to the target.
  4. Data Transformation: The central phase of reshaping, restructuring, and enhancing data to suit the desired output.
  5. Validation and Testing: Data transformation undergoes rigorous validation and testing to ensure accuracy and reliability. Using test cases with valid data, experts will approve if the transformation is a success.
  6. Automation: Automation plays a significant role in expediting the process. It will take away the manual work to make the process more efficient.

The following segment will delve into common techniques and tools utilized in accomplishing this goal.

Common Approaches to Data Transformation

There are many techniques and tools to transform data. The following will discuss the common ones.

Techniques for Transforming Data

  1. Aggregating and Summarizing: This technique condenses large datasets into manageable summaries. Summations and averages are typical examples.
  2. Filtering: Selectively extracts specific data records based on defined criteria. As an example, think of isolating a portion of sales data for only the stores that did not perform well in sales.
  3. Joining: Data joining merges data from different sources based on common attributes. One example is joining client information with their purchases.
  4. Ranking: Assigns positions to data elements based on specific criteria. An example in sales includes identifying the top 10 performers.
  5. Data Masking and Encryption/Decryption: Data masking protects sensitive data by replacing it with masked values. Meanwhile, encryption/decryption secures data during transmission and storage.
  6. Sorting: Involves arranging information in a specified order. Think of alphabetical listings of client names, store sales from highest to lowest, and more.
  7. Restructuring and Pivoting: This technique reshapes data. It rotates rows into columns or vice versa for easier analysis.
  8. Categorizing: Involves grouping information into meaningful classes. One example is profiling or grouping people based on age brackets.
  9. Splitting: Divides data into components. It is useful when dealing with compound data elements or parsing a row of data. A row in a CSV file is a candidate for splitting.
  10. Combining: This is the opposite of splitting. Sometimes the source data is already split. Examples are customer names. They are divided into last, first, and middle names. Combining them is useful in reports, printing bank checks, and the like.

The following segment will delve into common techniques and tools utilized in accomplishing this goal.

Data Transformation Tools

  1. Coding and Scripting: Using programming languages like Python, Java, and SQL provides flexibility and customization. Benefits include fine-tuned control and handling complex transformations. However, it involves a steeper learning curve and potentially longer development times.
  2. No-Code Solutions: User-friendly graphical interfaces facilitate pipeline design without coding. This approach provides simplicity and expedites development. However, it may limit customization for intricate transformations.
  3. Mixed of Code, Low-Code, and No-Code Solutions: These tools offer the best of both worlds for maximum flexibility. There’s a graphical user interface to design pipelines. However, the user has the option to add some code. The mix offers an easy user interface with a dash of flexibility. But can include limitations in the graphical user interface compared to no-code solutions.

These data transformation tools cater to different needs. The choice depends on the complexity of transforming data and the user’s expertise.

We’ve explored the techniques and tools for transforming simple to complex data. It’s time to shed light on the common challenges and pain points that often arise in data transformation. The next section will show how to tackle these issues and ensure a smooth sailing journey.

Realizing the Benefits of Data Transformation

Let’s explore how businesses have transformed their data for remarkable outcomes.

TecneValue: Unlocking Insights for a Telco Company

TecneValue extracted data, metadata, and dark data from modems and various internet-connected devices. This data was then meticulously cleaned to cut redundancy and misinterpretation. It was transformed into a format suitable for ingestion and interpretation and loaded into a data warehouse. See how they did it in the full case study here.

Alhena: Simplifying Lead Data Entry

For Alhena, a company managing leads from different clients, data transformation brought relief. Initially, manual uploads to Salesforce were time-consuming and clients have different formats. Collaborating with Skyvia, Alhena standardized the format and automated the process. See how they did it in the full case study here.

CashMe: Streamlining Marketing and Billing Data

CashMe faced a common problem of platform diversity. What’s more, creating KPI reports took days. Data transformation was the key to harmonizing this diverse data. See how they succeed in their story here.

These success stories show the transformative potential of data transformation. It turns raw data into actionable insights, streamlined processes, and strategic advantages.

The next section will discuss the evolving trends and how they’re shaping the future of data management.

Future Trends in Data Transformation

The world of data is evolving at a rapid pace. It’s driven by technological advancements and the ever-increasing volume of data. Here are the trends to watch out for:

  1. Real-Time Data Transformation: As businesses demand instant insights, near-real-time data transformation will become paramount. The ability to process and transform data on the fly will enable immediate decision-making.
  2. Machine Learning Integration: Machine learning algorithms will play a more prominent role in data transformation. It will automate complex tasks and uncover deeper insights from data.
  3. Data Governance and Compliance: Data joining merges data from different sources based on common attributes. One example is joining client information with their purchases.
  4. Cloud-Based Solutions: The cloud’s scalability and flexibility will continue to drive the data transformation to cloud platforms. offering cost-effective and accessible solutions.
  5. No-Code/Low-Code Tools: Non-technical users will find it easier to transform using no-code/low-code tools. These tools will improve over time. It will also reduce the reliance on IT departments.
  6. Advanced Data Integration: More integration of diverse data sources, from IoT devices to unstructured data, will be central to transforming data. It will create a holistic view of information.
  7. Data Preparation Automation: Automation will streamline data preparation. Then, it will save time and reduce manual errors in the transformation process.

Looking ahead, businesses that embrace these trends will gain a competitive edge. They will harness the power of data for informed decision-making and growth.

Conclusion

In data transformation, the power to turn raw data into valuable insights is within reach. This article discussed the intricacies, benefits, and future trends of data transformation. The potential is boundless.

With the right tools and a dash of determination, transforming data becomes much easier in data integration. It will also become the driving force behind growth, efficiency, and innovation.