An Introduction to Efficient Data Transformation

Business intelligence is a streamlined system that relies on a near-continual imput of information, often in the form of structured data for analysis. The insights that these systems produce are used globally, helping businesses to create more effective development plans for the future. Data-driven insight spans everything from HR and hiring strategy to optimize business operations and improving marketing.

Working with data – illustrative photo. Image credit: Adam Nowakowski via Unsplash, free license

However, to best draw insights from data, business analysts need to transform raw and unstructured data into alternative formats that lend themselves to analysis. For example, if the destination system where they will conduct the analysis works best with structured data, then part of the transformation process will involve changing the format of data to a structured version.

Especially as we move further into an era of high data availability, we’re now able to find raw data in more formats than ever before. Although this has increased the possibilities we have available to us when it comes to data management and analysis, it has also increased the need for data transformations. Only around 20% of data is structured, with the other 80% being unstructured or semi-structured.

In this article, we’ll dive into the world of data transformation, demonstrating:

• What data Transformation is
• Common Challenges in Data Transformation
• Strategies for Effective Data Transformation

Let’s dive right in.

What Is Data Transformation?

Data transformation, as the name of the process suggests, is the act of transforming pieces of data from one format to another. Typically, this represents a formalization of data, helping to structure and increase the integrity of data. Most of the time, data transformation focuses on reorganizing or manipulating data into specific formats, like getting data ready to put into a relational database.

While it is possible to conduct analysis on unstructured data, it is more difficult and reduces the clarity, efficacity, and quality of the insights. Instead of going through this process, businesses use data transformation to create more consistent data with a higher base level of quality.

Data transformation is a vital process in the world of data engineering and business analysis, allowing engineers to change the format of data.

Common Challenges in Data Transformation

As businesses collect data and begin to transform it, they’re likely going to run into several common problems. However, most of these problems have fairly straightforward solutions, allowing you to prepare for them ahead of time and stay on track.

Here are some common data transformation challenges:

• Missing Data – If there are missing elements to the data you collect, you can use a number of processes before transforming it, like cleaning, mean imputation, or K-nearest neighbors.
• Data Overload – Especially with the sheer amount of data out there, many businesses will overload their systems by trying to collect too much data at once. If you need to process lots of data simultaneously, look into horizontal scaling, parallel processing, and distributed computing.

While seemingly slight, these two problems account for the largest quantity of challenges that a business will run into when attempting to transform data for their business intelligence. However, by taking action to remedy these problems as early as possible, especially with the following strategies, you’ll be able to transform without worry.

Strategies for Effective Data Transformation

If you’re looking to streamline the data transformation process, there are a number of tactics that you can employ.

Always Clean First

One of the most important rules to remember when working with data transformations is that a little upfront work can go a long way. For example, if you begin to transform data without first checking it, you may have accidentally wasted time by processing duplicated data, doubling the time it takes to transform data without increasing your sample sizes.

In order to prevent wasteful mistakes or misallocations of resources, it’s a good idea to engage in data cleaning and processing. By going through the data with tools and removing duplicates, finding outliers, finding and then addressing missing values, etc, you’re able to radically improve the final product of your transformations.

Although cleaning will increase the time between data collection and transformation, it will save a huge amount of time in the long run and is well worth the extra step.

Leverage Data Transformation Tools

As an extremely useful and typical data process, there are plenty of data transformation tools for engineers to make use of. While each business will have specific systems that they turn to, it’s important to understand how each data tool can help out.

For example, users can turn to distributed computing frameworks like Apache Spark to allow for data partitioning and parallel processing. As a tool, this allows businesses to increase the amount of data they can process at any one time. Especially for companies that need to deal with large volumes of data, tools like this can become invaluable.

Similarly, businesses can turn to specialized data transformation tools, like Data Build Tool (dbt). As a tool, dbt provides a number of rich features that allow engineers to manage transformations, create and execute data quality tests, and check for data quality. Due to the many functions it has at its disposal, dbt data quality is said to be excellent, helping to create effective, powerful, and consistent transformation processes.

Making use of data transformation tools can radically reduce the spontaneity of the process and create a higher overall quality of transformed data.

Use Normalization Techniques

Normalization and standardization are techniques that can help to increase the consistency and quality of transformed data. When transforming data, the final result will not always be perfectly aligned with the constraints that you’ve set out, especially when it comes to high;y-specific structured formats.

In order to get around this problem, you can use normalization and standardization. For example, Min-max scaling and Z-score standardization, respectively, will help to bring your final values closer to your intended range. Tips like these can increase the compatibility of your data without compromising its accuracy.

Final Thoughts

Data transformation allows businesses to take stock of the data formats they currently have, outline their format requirements, and create processes that will rapidly change data into their desired format. Over time, data transformation has evolved to cover a number of other processes that look for inconsistencies, security issues, or missing regulations within data.

Modern data transformation is an incredibly efficient process, with the range of tools, systems, and frameworks out there facilitating the easy and rapid transformation of data. Considering the international usage of data in analytics and business intelligence, data transformation has marked itself as one of the most important aspects of data engineering currently in use.