Discover the Top Data Automation Tools with Segment

Research shows that up to 79% of global data could be collected through automation, and over 90% could be processed this way. Advances in artificial intelligence and machine learning are also adding to the ways that businesses can leverage technology to better manage their data, ranging from predictive and prescriptive analytics to better data security. 

Below, we cover a few of the most popular tools used to manage data lifecycles and workflows. 

What are data automation tools?

Data automation tools refer to the software and platforms that collect, transform, store, and analyze data without manual intervention. These tools can range from automating analytics, to predicting user behavior, flagging incorrect or inconsistent data, and even transforming bad data as it flows through the pipeline. 

Data automation tool use cases 

Just as there are thousands of ways to use data, there are almost as many automated data processing workflows. Here are a few of the most common applications. 

Supply chain management and optimization: Automation helps streamline tasks from paying invoices to processing orders while also reducing human error (e.g., duplicated orders or data entry mistakes). 

Automated data backup and recovery: With increasing data loads to save and protect each day, automation solutions can ensure backups happen frequently, fully, and without human help. 

Email campaign optimization: Marketing automation tools track campaign results and make predictions of the best days and times to send so campaigns can be scheduled to go out without human intervention. 

Fraud detection and protection: Automation tools analyze large amounts of transaction data for banks, retailers, and card issuers to find patterns most likely to indicate fraud or the potential for fraud. They flag suspicious transactions before they get processed. 

Chatbot and customer support automation: Data automation tools help chatbots serve customers any time of day. They may lead to a reduction in the need for live customer agents while improving net promoter scores (NPS).  

4 best data automation tools 

The data automation tools you choose to use will depend on the unique needs of your business, but below are a few common types to consider. 

1. Data collection

How are you currently capturing data? What type of data are you collecting?

With the vast amount of customer touchpoints, data sources, and data types organizations now have to juggle, data collection can quickly become cumbersome (if done manually). Automation helps tremendously when it comes to ETL pipelines, pre-built integrations, event streaming and batch processing. 

Customer data platforms have emerged to help organizations build and scale their data infrastructure. For instance, Segment offers over 450 pre-built connectors and the ability for businesses to quickly create custom Sources and Destinations with a few lines of JavaScript. Segment is also able to process 400,000 events per second, unify customer data into identity-resolved profiles, and sync those profiles with business data warehouses for further enrichment. 

This fine-tune orchestration balances businesses’ need to automate the workflows and processes that would become repetitive and time consuming, while also offering the flexibility for them to customize their tech stacks and data orchestration as they see fit. 

2. Data quality and cleansing

How are you safeguarding against duplicate data, inconsistencies, and protecting data quality? This is another arena where automation has quickly become essential.

Tools like Protocols automate data governance to ensure that the data being tracked adheres to a company’s tracking plan, and any bad data is flagged for review before it reaches downstream destinations. Even better, bad data can be corrected as it flows through the pipeline. 

3. Workflow automation

Zapier has earned a reputation for making even complicated workflows simple to set up. It uses a drag-and-drop interface to help users create unlimited “if this happens, do that” triggers for all types of data. It supports over 500 apps with its integrations. 

4. Data analysis & Business intelligence 

The highlight of Looker is its own language, known as LookML. It's an elevated version of SQL that helps users get the most out of the tool by reusing or building upon measures rather than rewriting. Users can define custom metrics and visualize data in new ways with others in their organizations.

Put your data to work with Segment – an all-in-one data automation tool

Segment provides end-to-end data automation solutions for tens of thousands of companies. We mentioned above how Segment is able to connect to any source or destination, and protect data quality at scale with automatic QA checks. But other features include Segment’s ability to automatically classify data according to its risk level (e.g., credit card numbers would be high risk), or mask data to protect personally identifiable information. 

Segment also helps organizations pair artificial intelligence with first-party data to provide out-of-the-box predictive models that can be added to customer profiles, helping teams to anticipate customer behaviors and provide the best possible experiences.