A mind-boggling 328.77 million terabytes of data is created each day. However, all this information is meaningless unless you can pull data together from different sources, standardize it, and analyze it. It's no wonder that so many organizations turn to Extract, Transform, Load (ETL) to gather data, prepare it for analysis, and move it to a new location like a data warehouse.
The problem is that, while ETL is an effective data integration method, it's far from perfect. Let's explore some ETL pros, cons, and alternative methods for handling raw data.
The ETL process
We've written about ETL before, so check out this guide for a deep dive into this data integration method. But let's quickly summarize what traditional ETL is and what it does:
ETL stands for Extract, Transform, and Load involves three steps:
- It extracts data from multiple sources and places it inside a staging area. Source systems include transactional databases, relational databases, CRMs, ERPs, SaaS tools, and apps.
- After the extract phase, ETL enters the transform phase and changes data from different formats into a common format for data analytics. This process might involve cleaning the data, removing duplicated data sets, and ensuring data complies with frameworks like GDPR.
- It loads data to a central repository like a cloud-based data warehouse or business intelligence (BI) tool. That helps you identify patterns and trends in your business processes. BI tools let you view data insights via reports, charts, heatmaps, and other visualizations.
Recommended reading: Marketing data warehouse: Everything you need to know
Disadvantages of ETL
An ETL tool is a solution that allows you to ingest data to a data warehouse like Amazon Redshift or Google BigQuery for analytics. However, it's not great for long-term data projects or high-volume data operations. Here are some ETL challenges you might encounter in a data pipeline.
ETL tools don't store data
Extract, transform, and load tools only move new data from a source to a new location. A central repository like a data warehouse will store your information, but you'll need to pay for this system separately, which can eat into your budget.
Because ETL tools don't provide storage functionality, you'll need to manually refresh dashboards in BI and visualization tools if you want to see the latest data. In some circumstances, you're actually resending huge swaths of data back into your visualization and BI software. These refreshes can cause you to breach quota limitations.
Plus, if an API breaks during the "extract" stage, relevant data will essentially disappear from end reports.
Data latency issues
ETL limitations include batch processing, which causes time lags between data extraction and availability. That means you won't be able to generate insights about your business in near real time, and there will always be some time delay in your data analysis. These latency issues can make it difficult to receive timely data insights and, ultimately, make business decisions.
Complexity and learning curve
Although low-code ETL tools exist, they still require a knowledge of programming and data engineering, as well as ongoing maintenance. This learning curve can make data integration difficult for non-technical users and even result in issues such as a loss of data quality and non-compliance in data governance. For example, you'll need to constantly update ETL tools when ad platforms like Facebook update their systems and APIs. Otherwise, data won't display correctly on reports and dashboards in BI tools.
As your data needs grow, it can be hard to scale the ETL process. Doing so can be expensive and time-consuming, especially if you need to change your system architecture or require data scientists.
Working with unstructured information
ETL tools suit structured data sets and struggle with unstructured information. That makes them ineffective when analyzing large volumes of data from diverse data sources.
Recommended reading: What is data governance? Everything you need to know
Two alternative ways to work with raw data
Consider an alternative method instead of ETL for your data teams. The best options are investing in an ELT tool or using a marketing data hub, which can solve ETL challenges.
Extract, Load, and Transform (ELT) is a data integration method that reverses the transformation process and load process of ETL. ELT pipeline tools extract data from a source, load data directly into a target system like a data warehouse or data lake, and then transform it for analytics. This technique lets you load all the data you need quicker than ETL processes. It is also better suited for unstructured information in your data pipeline. However, ELT can pose data governance problems. That's because it loads data into a second location before ensuring it complies with frameworks like GDPR and HIPAA.
2. A marketing data hub
A marketing data hub gathers, transforms, and integrates marketing and advertising data from all your sources into a centralized location. It serves as the single source of truth for that data. Here are some of the benefits of marketing data hubs for your data pipeline:
- The best marketing data hubs provide hundreds of pre-built data connectors that automatically sync with your marketing platforms, removing the need for data teams to use code.
- Marketing data hubs transform data into the correct format for analytics based on your specific needs, resulting in more valuable insights.
- You can integrate a marketing data hub with data storage and visualization tools via built-in integrations, again removing the need for code.
- Unlike generic ETL pipeline tools, these platforms specifically handle marketing data and help you overcome the challenges of managing this data.
- A marketing data hub preserves historical data, meaning you will never lose historical context despite platform or data model changes like UA transitioning to GA4.
The most efficient ETL solution?
Unlike traditional ETL or ELT solutions, Funnel is made for marketing data. This means a faster setup, high data accuracy, and a more cost efficient data stack.
What you should know about ETL challenges
ETL challenges include data latency, scaling issues, and complexity. You won't be able to generate near real-time data analytics or handle unstructured data. Conversely, ELT, also requires a learning curve and can cause problems when complying with data governance laws.
As a savvy marketer, you should definitely take a closer look at a marketing data hub. This powerful tool provides unparalleled flexibility, lightning-fast real-time capabilities, a wide array of pre-built connectors, and effortless structured and unstructured data handling.
In the end, strive to be your most productive self. Take a look at what tools you are working with today, understand how you make decisions, and see where you can improve by relying more on your marketing data.