Getting Started with Data Transformation: A Clear Guide

Want to organize, blend, normalize, format large datasets and prepare for reporting with business intelligence tools? Read this guide on data transformation in the ETL process.

In today's digital age, businesses are inundated with vast amounts of data. This data, if collected, stored and used correctly, can provide invaluable insights so businesses can act data-informed and drive business growth.

However, even if data is collected properly, they are rarely in the format that your business intelligence (BI) tools can utilize… Usually, data connectors and pipelines provide you with raw and unorganized data, so you can not extract any significant patterns from such raw data.

This is why you need to convert this raw data into a usable, business-ready format, to structure the data to match your business needs. This is where the data transformation begins.

In this guide, we will discuss data transformation from the very beginning. After reading this article, you will grow professional knowledge on data preparation and can successfully plan and execute data transformation projects.

Data transformation involves changing data from one structure or format to another, usually transitioning it from its original form in a source system to a desired form suitable for a target system. It is a crucial component of most of the data integration, data management and data preparation tasks.

Data transformation is about making raw data more accessible and valuable to businesses. Data becomes actionable BI as a result of data preparation, normalization, blending, and attribution.

The purpose of data transformation is to convert raw data into a meaningful and actionable format, facilitating better decision-making and insights.

The main objective of data transformation is to pull data from multiple sources, modify it into a format that's easily interpretable, and then route it to its intended destination. Data transformation refers to T from ETL (Extract, Transform, Load) process, this procedure entails gathering data from various points, reshaping it to fit a specific, required structure, and subsequently storing it in a unified database.

With the rise of big data, tracking systems, ad platforms, marketing, sales or business tools, companies are dealing with data from a myriad of sources. This diverse data can pose compatibility issues.

Data transformation addresses this by converting data from any source into a standardized format, or blending data from multiple sources into the same structure.

Data extracted from source locations can often be not usable in its original form. The transformation process adds value to this data by resolving inconsistencies, missing values, and other quality issues. This ensures that the data is consistent, reliable, and of high quality.

Here are the five essential steps involved in the data transformation process:

• This is the initial step in the transformation process. It involves identifying and understanding the data in its original format.
• Tools like data profiling can be employed to gain a comprehensive understanding of the data's structure.
• This step aids in determining the necessary transformations to convert the data into the desired format.

• Data mapping is the process of defining how each field from the source data should be represented in the destination system.
• It involves specifying how data fields are mapped, joined, filtered, and modified to achieve the desired output format.

• This step involves generating or developing executable code that can transform the data based on the mapping rules specified.
• The code can be in various languages such as SQL, R, or Python, and it defines the transformation logic.
• Some of the data transformation tools, like OWOX BI provides users with a set of no- or low-code transformation templates to satisfy specific marketing analytics needs.

• Once the transformation code is generated (or template is used), it needs to be executed on the data to produce the desired output.
• This step can be tightly integrated with the transformation tool or might require separate steps for manual execution.

• The final step ensures that the transformed data meets the specified requirements.
• It involves checking the output data for accuracy and consistency. If any discrepancies or anomalies are detected, they are communicated to the developer or data analyst for resolution.

Data transformation is a fundamental process in data analytics and data management. The goal is to improve data quality, make it more accessible, and ensure it's in the right shape for specific analytical tasks. Here are some of the primary methods used in data transformation:

Data Aggregation involves collecting raw data and showcasing it in a condensed manner for statistical evaluation. This method can be used to produce statistics like average, minimum, maximum, sum, and count. By analyzing the aggregated data, insights about specific resources or resource groups can be derived after the data has been aggregated and presented in a report form.

This method enhances the data mining process, making it both proficient and impactful. By deriving and introducing new attributes from the existing set, it supports the mining process, emphasizing attribute or feature development within data transformation.

Data discretization involves segmenting continuous data attributes into a set number of ranges and allocating specific values to these ranges. Various methods are available for discretization, ranging from simple equal-width and equal-frequency methods to more complex ones.

Generalization entails creating multiple layers of summarized data within an evaluation database, offering a broader perspective on a particular issue or scenario. This technique is commonly employed in Online Analytical Processing (OLAP) to swiftly address multi-dimensional analytical inquiries.

Manipulation entails altering or adjusting data to enhance its clarity and structure. Analytical tools aid in detecting trends within the data, converting it into a format conducive to generating insights, whether related to financial metrics or consumer patterns.

Data normalization is a method that transforms original data into a format more suitable for processing. Its main objective is to minimize or remove redundant data, enhancing the performance of data mining techniques and accelerating the data retrieval process.

Smoothing is a technique employed to discern patterns in data that's cluttered with noise, especially when the exact nature of the trend isn't evident. This approach is versatile and can be used to identify trends in diverse areas, from stock markets to economic indicators.

Data transformation is a critical process in the realm of data management and analytics. The transformation process ensures that data is clean, consistent, and ready for reporting / consumpltion by various applications or end-users. While there are several methods and techniques for data transformation, the types of data transformation can be broadly categorized based on the mode of operation. Here are the primary types:

1. This is the most common type of data transformation, where data is collected over a period and then processed in batches.
2. The transformation process in this type is pre-scheduled and occurs at specific intervals, such as daily, weekly, or monthly.
3. Batch data transformation is suitable for large volumes of data where real-time processing is not a requirement. It's often used in scenarios like end-of-day reporting, monthly financial closings, and periodic data backups.
4. While it can handle vast amounts of data efficiently, the downside is that the transformed data might not reflect the most recent changes until the next scheduled batch process.

1. Unlike batch transformation, interactive data transformation processes data in real-time or near-real-time.
2. As soon as the data is received, it is immediately transformed and made available for use. This type is particularly useful in scenarios where timely data availability is crucial.
3. Interactive transformation is commonly used in applications like real-time analytics, online transaction processing systems, and live dashboards where up-to-the-minute data is essential.
4. The challenge with interactive transformation is ensuring that the system can handle the continuous influx of data without delays or bottlenecks.

Data transformation is vital for converting raw data into actionable insights. Various tools facilitate this process, ensuring data quality and compatibility. Some of the top data transformation tools include OWOX BI, Hevo Data, Datameer, IBM InfoSphere DataStage, Informatica – PowerCenter, Matillion, SAP Data Services, Talend, Pentaho Data Integration, and CloverDX.

Among these, OWOX BI stands out as a tool with a set of transformation templates to meet all of the marketing analytics goals, designed to ensure clarity in the data flow.

In the dynamic world of data management, OWOX BI emerges as a powerhouse platform that significantly simplifies the data transformation process. Easy to use, low or no-code, dependency triggers, no maintenance required.

With OWOX BI, businesses can leverage the following benefits:

1. Automated Data Collection: OWOX BI Pipelines facilitates the seamless automation of data collection, eliminating the manual effort and reducing the risk of errors. It can integrate data from various sources, ensuring a centralized hub of information that is always up-to-date.
2. OWOX BI Streaming is a cookieless real-time user behavior tracking system, ensuring privacy compliance with regulation and extending the lifespan of cookies. Marketers can accurately track the entire conversion journey, find the true sources of conversions, and gain a deeper understanding of customer behavior.
3. OWOX BI Transformation saves time on data preparation (avg. of 70 hours per month). With pre-built low- or no-code transformation templates (based on 100’s delivered projects across multiple industries), businesses can quickly produce trusted datasets for reporting, modeling, and operational workflows:
4. Sessionization: Group on-site events into sessions to find conversion sources
5. Cost data blending: Merge ad cost data across channels to compare campaign KPIs in a single report
6. Attribute ad costs to sessions to measure cohorts and pages' ROI;
7. Create cross-device user profiles across different devices (App + Web)
8. Identify new and returning user types for accurate analysis
9. Apply a set of attribution models: Choose from standard attribution models like First-Click, LNDC, Linear, U-shape, and Time Decay, or create a custom Machine Learning Funnel-based attribution model
10. Use modeled conversion for cookieless measurements and conversion predictions
11. Prepare data for marketing reports in minutes

No Coding Required: OWOX BI doesn't require extensive SQL knowledge. It offers a range of pre-built templates and intuitive tools that make the data transformation process straightforward, even for those with limited technical expertise.

Customizable Solutions: Understanding that every business has unique needs, OWOX BI offers a customizable and scalable solution to meet every business needs. Whether you are looking to integrate specific data sources or develop custom reports, our platform provides the flexibility to tailor its services to meet your requirements.

For a comprehensive, reliable, and efficient solution to your data transformation challenges, OWOX BI stands as a trusted partner, ready to take your data management to the next level.