Automated Data Pipelines: Key Benefits & Recommendations
So it’s no surprise that many companies are looking to adopt automated data pipelines wherever possible – creating huge advantages for their data engineers, data analysts, and any employee who works with data from multiple sources.
What, specifically, are those advantages? We’ll discuss:
- The 5 key benefits of leveraging automation in data pipelines
- Signs your team needs an automated data pipeline
- How to choose the right pipeline for your team’s specific needs
5 Benefits of an Automated Data Pipeline
#1 Data Engineers: Work on things that matter instead of building integrations from scratch
Without a ready-made automated pipeline, data engineers become the primary resources responsible for time-consuming tasks like writing and maintaining API calls and updating data schemas to push data into a warehouse or data lake.
With an automated data pipeline solution, data is automatically extracted, transformed, and loaded into the warehouse. Data is cleansed and de-duplicated as it enters the warehouse and schemas are automatically updated as data models shift, so data engineers can shift their focus from maintaining reliable integrations to writing the best queries and providing better insights to their stakeholders.
#2 See and derive insights from your data in un-siloed ways
Data silos make it difficult to get a holistic view of your data. When data is scattered across different departments and systems, the insights that could be derived from a singular view of related data are lost. This can lead to inefficiencies and missed opportunities to identify patterns that would reveal trends in customer satisfaction, upsell opportunities, or even churn risk.
An automated data pipeline helps to address these issues by providing an easy way to combine data from disparate sources into a central location. With a single place from which to query related data, data engineers can more easily write the queries that make it possible to spot those patterns and correlations, and spend more of their time creating consumable reports that are easier for stakeholders to understand.
#3 Use real-time metrics to make strategic decisions fast
Where does your important data live? Whether it be information about your website traffic, data about how customers interact with your product, or critical financial data that could reveal how the Street is going to react this quarter, being able to quickly access data is crucial to being able to make timely decisions.
For example, by using data to understand when and which customers are most likely to churn, customer success teams can take proactive steps to reduce churn by ramping up efforts to keep them happy and engaged.
Using your current tools, how long would it take to get all of that data organized in one place? How many reports would you have to run? Running a dashboard off a central warehouse which is utilizing fast pipelines to stream data as changes occur can reduce your executive dashboard updates to minutes, not hours or days or even weeks.
#4 Create stakeholder-centric customer journey dashboards
Data pipelines are the foundation needed to feed data into your data visualization tools. And without data visualization, you’re left with a bunch of data that’s difficult to make sense of and even harder to act on.
An automated data pipeline can help you better understand customer feedback and make changes to your offerings accordingly when you wrangle the combined data into a visualization of your customer journey. Customer journey dashboards are an effective way to show stakeholders “what’s going on” with data and highlight areas where small changes could improve business outcomes.
#5 Continually nurture customer relationships with behavioral insights
With all your customer data in one central location, you can start to track customer behavior and interactions across channels. This allows you to have a complete view of your end to end customer journey to better understand their needs and how to keep providing them new value.
Using an automated data pipeline, you don’t even have to wait to see the effects of a change across the customer journey until after it’s too late to intercept issues. Instead, you’d be able to notice quickly if a change in the products a customer is using leads to a barrage of support tickets and send in reinforcements before it leads to a lower NPS (net promoter score) rating.
Perhaps you take it one step further and implement a “Customer Health Program” in response to several customers experiencing difficulties – track in real time whether participants in the new program are showing more consumption of your features with usage metrics and understand early which customers are likely to invest more with your company as a result and which are still likely to churn.
Signs You are Overdue to Adopt an Automated Data Pipeline
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- Your current integrations can produce unreliable data
Because data integrations are complex, they can sometimes produce unreliable data. In Wakefield Research’s survey, 71% of data engineers and analysts reported that these limitations caused their company to make decisions with old or error-prone data. What if one of those decisions happened to be of the “bet the company” type? That is a scary thought when the jobs of your employees are on the line! If you’re constantly having to check data quality and manually fix errors, an automated data pipeline may just save you some sleep. - Your data integrations quickly become outdated
As APIs change and new data sources are added, integrations and schemas can quickly become outdated. 80% of those same data engineers surveyed above also reported spending even more time rebuilding data pipelines already deployed. If your team is constantly having to update data pipelines manually, it’s time to consider automation that can keep up with changes without bogging the team down.
- Your current integrations can produce unreliable data
- You use data to make time-sensitive business decisions
Also according to the same survey, 76% of data engineers using custom-built data integrations reported it taking up to a week to prepare data for revenue-impacting decisions – even at companies with over $500 million in revenue. If you’re using data to make time-sensitive business decisions, forget having the manpower to support the requests for reports, it’s time you just don’t have. You need automation to help prepare data for reports quickly without potentially sacrificing the accuracy and reliability of the data.
How to Choose the Right Data Pipeline Automation Tool
Here are some tips on how to find the right data pipeline automation tool for your team.
Focus on your data engineering team’s specific requirements
Data pipelines can vary greatly in terms of complexity, features, and the amount of data engineering involvement expected. When choosing a data pipeline, focus on the specific needs and experience of your data engineering team. For example, you should consider:
- Do they need something easy to set up and maintain?
- Would they get more use from a no-code data pipeline or would they prefer to have the ability to add code to the pipeline?
- How quickly does the data need to be updated?
Create a list of all other technical and feature requirements
Next, you’ll want to create a list of all other requirements for your data pipeline. To do this, you should consider factors such as:
- Ease of use. The data pipeline you choose should be easy to set up and use. It should also come with clear, easy to access and understand, documentation that will help you get started quickly.
- Supported data sources. The pipeline should support the type of data sources you need to connect to, including data formats such as CSV, JSON, and XML.
- Supported data destinations. The pipeline should also support the type of data destination you need, such as a data warehouse, data lake, or cloud storage.
- Compatibility. Does it play nicely with other software and systems you use?
Determine the unique requirements for your company or industry
Are there any features that your data pipeline must have to work for your specific company or industry? HIPAA compliance? PCI compliance?
If you’re selling products in the Subscription Economy, you likely want specific metrics produced. The data pipeline you choose should be able to provide you a way to derive key metrics like churn rate, customer lifetime value, MRR and ARR.
Schedule demos for software that meet your requirements
Before deciding on a data pipeline software, schedule demos for the top contenders. This will give you a chance to see how each one works and decide which one is the best fit for your company’s needs.
Data Pipeline Automation with Zuora Secure Data Share for Snowflake
Recently, Zuora has partnered with data cloud platform Snowflake to release Zuora Secure Data Share for Snowflake. If you’re a business in the Subscription Economy, it could be exactly the data pipeline automation tool you need to combine important financial data with data from other systems.
Zuora Secure Data Share for Snowflake is a zero-effort, no-code data pipeline solution, requiring no setup or maintenance from data engineering teams. The automated data pipeline frees your engineers to focus on writing analytical queries they want to perform on data rather than writing and maintaining the calls and schemas required to make the data accessible.
Use this solution to create advanced financial reports, analytics, and dashboards – including MRR, ARR, and Churn – without additional integration effort by utilizing productized connectors to/from Snowflake and world-class BI tools your data engineers and data consumers already know how to use.
Zuora Secure Data Share for Snowflake provides data engineering teams with a near-real-time, change-driven data stream. With an average latency of under 10 minutes, your business will no longer have to wait hours for an updated report or bug your data engineers to perform a manual refresh for that high visibility board meeting.
Zuora’s Head of Corporate Data Strategy, Karl Goldstein, estimates a near-real-time data pipeline comparable to Zuora Secure Data Share for Snowflake would require 2 full time data engineers to build and maintain. That’s quite the investment if you choose to go it alone!
Final Thoughts
With an automated data pipeline in place, data engineers can focus on more valuable tasks instead of building custom data integrations that may produce less desirable results that lead to missed opportunities and poor business decisions.
For a data pipeline specifically designed to support data engineering teams at subscription-based companies, check out Zuora Secure Data Share for Snowflake.