How To Improve Data Transformation To Enhance Data Analytics

The use of data in data analytics has quickly become one of the most vital industries in the world to businesses. By providing utility in almost every single sector of business through actionable insights pulled from data, the value of huge data repositories for businesses has skyrocketed. Alongside this change, the value of an effective team of data scientists has also increased.

 

Yet, despite the utility of data and how vital it has become, organizations still typically waste a lot of time and resources when it comes to capturing, processing, and storing data. Over 80% of a data scientist’s work schedule is consumed by organizing data, due to poor data ingestion and storage methods in a business.

 

To increase how effective data scientists can be, companies should strive to make their data ingestion pipeline as streamlined as possible. A common bottleneck that many businesses run into is poor data transformation pipelines. As ETL and ELT pipelines are challenging to construct and maintain, most businesses tend to run into trouble in this area.

 

In this article, we’ll dive into the data transformation pipeline, demonstrate why it's essential, typical challenges that arise, and suggest useful solutions that businesses can incorporate. 

 

Let’s dive right in. 

 

What is the data transformation pipeline and why is it important?

 

The data transformation pipeline allows businesses to move raw data – be it structured, semi-structured, or unstructured – into a chain of processes that will aggregate, optimize, organize, and eventually store data. The data transformation aspect refers to changing the form and structure of data to align with that which a business can gain the most insight from.

 

Data transformation pipelines help to deliver new data to a data warehouse in the correct format and with the right conditions. There are two common approaches to data transformation:

 

• ELT – The process of Extract, Load, Transform extracts data from its original sources, loads it into a target data warehouse or database, then transforms it into the correct format for future analysis. 

• ETL – The process of Extract, Transform, Load extracts data from various sources, transforming it across the course of the pipeline, then loads it into a database once it is in the right format. 

 

Across these processes, companies will typically discover data sources, map them against the desired specification that a business wants data to have, and then move the data into the ELT or ETL process. 

 

What challenges do businesses encounter in their data transformation pipelines?

 

There are several core challenges that make the data transformation pipeline less than ideal to interact with. While ingesting low volumes of data may be easy for a business to manage, as it begins to scale operations, more opportunities for setbacks, bottlenecks, and major issues will present themselves.

 

Here are some of the common problems that a business will face when attempting to utilize a data transformation pipeline:

 

• Volume Issues – Just because a data pipeline is effective doesn’t mean it will scale well. Building robust pipelines that can scale with a company can present several issues.

• Data security – Data breaches are becoming more common than ever before. Encryption, compliance, and access controls can create alternative considerations that hinder pipeline development.

• Data Quality - Businesses must be able to effectively cleanse data without impacting its validity to ensure secure data practices. Incomplete or inconsistent data can consume resources in the analytics stage and negatively impact data practices.

 

While not an exhaustive list, these challenges routinely present themselves when businesses attempt to create a robust data transformation pipeline. However, by understanding these problems and identifying them within your own business, you can then put in place steps to overcome them.

 

Strategies to create robust data transformation pipelines

 

Creating effective data systems won’t happen overnight. However, there are a number of strategies that can reduce the total amount of time and effort that building transformation pipelines take.

 

Below are some strategies that businesses can use to create robust transformation pipelines.

 

Construct a Processing Plan

 

A data processing plan allows your business to dictate how you transform your data as it moves across the entire length of your data pipeline. By understanding how your data moves and what you hope to gain from it, you can determine the best course of action when transforming that data.

 

While this can be an extensive initial job, it helps to pinpoint the most valuable types of data for your business. Start with this, then slowly lay out a detailed data ingestion plan for every step of the transformation and movement process.

 

Optimize Infrastructure for Enhanced Data Transformation

 

One of the most vital parts of constructing a streamlined data transformation pipeline is to ensure that the final delivery location is opportune for future analysis. The vast majority of businesses now opt for cloud infrastructure due to its scalability and flexibility. For example, when comparing Databricks vs Snowflake, two leading cloud data warehouses, we can see that they excel at utilizing a range of data types and providing tools for analysis. 

 

Without effective infrastructure, businesses will struggle to get the desired insight from their data, no matter how effective the transformation pipeline is. By optimizing all other infrastructure points, your changes within the transformation pipeline will be much more dynamic and show greater instant results.

 

Governance is key

 

Without data governance, businesses simply cannot get what they need from any data they collect. By creating strict data governance initiatives and compliance regulations that your engineers and scientists must follow, you can ensure a higher final quality of data.

 

Monitoring governance across your data transformation pipeline will allow you to clearly identify moments where data is diverting from the plan. It’s a good idea to assign compliance and governance leaders to head this process, as they will be able to understand the intricacies of your system and more actively participate in improving compliance.

 

Final Thoughts

 

Data transformation is a vital part of constructing a healthy data-first ecosystem. By providing a method of changing unusable data into cultivated samples for analysis, the data transformation pipeline is an integral part of any company’s interaction with data.

 

While data pipelines can be difficult to manage, the tips and strategies on this list will point businesses in the right direction toward building a robust data system. By optimizing infrastructure, understanding the processes that go into data analysis, and clearly outlining the objectives of data pipelines, companies and data scientists can build toward a more effective method of processing raw data.

 

For data-first companies, constructing a streamlined transformation pipeline should be a top priority.