It is a capital mistake to theorize before one has data

Even though Sherlock Holmes said this back in 1888, it still rings true today, with data becoming one of the most valuable assets of a modern company. With new SaaS tools springing up, the data is getting increasingly siloed. This hampers your ability to gather insights from the data. So, how do you collect your data in a central location for effective analysis?

It’s elementary, dear Watson. You use a data pipeline.

Imagine a water pipeline. Now, instead of water, replace it with data, and you have a data pipeline. For a data pipeline, the destination can be data lakes or data warehouses. Data is ingested from a business’s vast number of sources and transformed based on the business use case.

Data transformation involves cleaning and aggregating the data, similar to how the water is filtered here before sending it to the destination.

But is this the only purpose of a data pipeline?

Nope…

Data scientists have created machine learning algorithms to solve complex business problems. Data pipelines help these algorithms:

Forecast customer demand

Personalize customer experience

Refine the ability to predict fraud

Identify the desires and tendencies of consumers, down to the last-minute details. This also decreases the financial burdens while promoting brand awareness and increasing revenue margins.

A data pipeline will collect and store your data in a central location while providing access to all users from various points. Apart from providing a single source of truth, data pipelines can also support cross-functional collaboration. In the absence of centralization, businesses struggle with managing their data. With centralized data management, businesses can collaborate across functions. It also supports data transparency throughout the business. This lets different users, like marketing teams, BI teams, and data analysts, access the same data via a single management system.

That would be the primary advantage of a data pipeline. Apart from this, here are a few more key advantages of data pipelines:

Data pipelines provide you with a framework where you can flexibly respond to changes in the sources or your data users’ needs. This flexibility makes it a great choice for agencies of all sizes.

You can easily build and scale your pipeline as needed!

From a simple pipeline- with only a couple of clients and few integrations- to a complex network, your pipelines will grow along with your business.

Traditional pipelines could cause delays and slowdowns if various organizations in a company need access to the data at the same time. The business should be able to add processing capacity and data storage within minutes as opposed to days or weeks, without adversely affecting the budget.

These data pipelines can be slow, inaccurate, and difficult to scale and debug. Their creation and management require considerable money, time, and resources.

Modern data pipelines provide the instant elasticity of the cloud at a fraction of the cost of traditional pipelines. They offer agile, immediate provisioning when workloads and data sets grow, enable businesses to quickly deploy their entire pipeline, and simplify access to common shared data, without being limited by a hardware setup.

As the data flows through the pipeline, it gets refined and cleaned, making it more meaningful and useful to end users. Since data pipelines standardize your reporting process, it ensures that all of your data is processed and collected consistently. This ensures that the data in your reports are reliable and accurate. Say goodbye to the inconsistent date range for metrics, copy-and-paste errors, and Excel formula errors that hinder your agency’s performance.

Data standardization converts raw data into a common and uniform format to allow analysts and other business users to analyze and extract actionable insights from it. It offers a comprehensive catalog of data to have a deep understanding of how the data has been transformed. This understanding is essential to ensure reliability, consistency, and security. You can standardize the data through the following steps:

Set the data standards: Find out the data sets that should be standardized and how they should be standardized.

Understand the data sources: Learn where the incoming data is coming from. Understanding the data sources will help data analysts identify the data standardization issues they may face.

Clean the raw data: Make sure that the data is verified and formatted correctly.

Through repetition, data pipelines can help you pinpoint trends and identify patterns over time. For instance, if one step consistently slows down your data flow, that should be your cue to explore the reason behind it and if there’s a way to optimize it.

If you find yourself repeatedly looking for a specific data point, you might be better off adding it as a widget.

The iterative nature of data pipelines also aids in data architecture standardization. In layman’s terms, you can reuse/repurpose your pipelines instead of building new processes every time.

Since data pipelines standardize and streamline the data ingestion process, it is easier for you to integrate new data sources.

Data pipelines can boost productivity as the data is easy to access throughout your agency and reduces the need to recheck the data. It also saves crucial time by automating the process of transforming, extracting, and loading the data into a reporting tool.

It also automates repetitive tasks like extracting data from multiple sources and transforming it into a format that can be easily analyzed. This frees up your time to focus on more productive tasks such as:

Managing and optimizing core data infrastructure

Building and maintaining custom ingestion pipelines

Supporting the data team with design and performance optimization

Building products to resolve internal problems

Business decisions are becoming more data-driven, especially in marketing. Whether it’s using data to come up with new strategies or analyzing a campaign’s metrics to understand its performance, marketing initiatives strictly adhere to the numbers and data produced.

As data pipelines pump up the data flow while collecting the data in a central repository, it makes it easier for you to drill down on it. So, the quality of your decisions doesn’t just depend on the quality of the data, but also on the quality of the analysis itself.

To further illustrate the advantages of data pipelines, let’s take a look at a case study- how Hornblower is making a dent in their industry powered by data pipelines built by Hevo Data.

The Hornblower Group is a global transportation and experiences leader with an unrivaled reputation for delivering delightful experiences to its customers. Spread across 125 U.S. cities and 114 countries and territories, Hornblower Group serves over 30 million customers. It offers everything from land and water-based experiences, ferry, to overnight cruise experiences.

With such a massive footprint, data is pivotal for the marketing team to handle bookings and extract actionable insights for growth.

Hornblower’s data is generally produced by its booking platform, Anchor (stored in DynamoDB). Along with this, surveys, CRM, websites, call center data, and loyalty rewards are collected on various tools like Salesforce, and Google Analytics, to name a few.

Initially, the data team was using AWS Glue to move all the data to Redshift. It made sense to move data from DynamoDB with AWS Glue, courtesy of the AWS ecosystem. The stumbling block becomes visible when non-AWS sources came into play.

Hornblower’s thousands of products meant to serve their customers produce a large amount of data. The cost of moving this data to the warehouse was gradually shooting up. Plus, with such a massive volume, junk data was gobbling up unnecessary events.

Karan, a data scientist, at Hornblower, wanted to optimize the stack.

This involved strategically moving only the required data to the warehouse and scaling economically.

However…

Employing traditional methods to move data would’ve required an A-team (picture data engineers instead of Liam Neeson’s team), which meant additional investment.

That would’ve been fine, but prioritizing this investment over growth opportunities would have led to a lower business impact and ROI.

So, the Hornblower Group started looking for a solution that allowed them to move data without investing too much in engineering resources.

Enter Hevo Data.

Data engineering is like an orchestra where you need the right people to play each instrument of their own, but Hevo Data is like a band on its own. So, you don’t need all the players.

Hevo brought together automated schema management and pre-load transformation to move the required data to the data warehouse.

Without having to manually create tables!

With Hevo Data, Hornblower can now effortlessly move critical marketing data from its key sources to the warehouse and deliver any and all insights. Just the way they want it.

Hornblower ended up saving the cost of two to three data engineers with Hevo Data, depending on the connected source and data volume processed.

Saving countless precious dollars is only a fraction of what you can achieve with a data pipeline tool. With time, the demand for data pipeline tools like Hevo Data will just go up. The global data pipeline tools market reached a value of $6.9 billion in 2022 and is estimated to grow to $17.6 billion by 2027, at a CAGR of 20.3% during the forecast period. Since data pipelines are here to stay and are increasingly making their way into every industry, it’s high time to cash in on this gravy train and stand out of the crowd.

A major roadblock in implementing modern data pipelines is a workforce’s weak skills and limited knowledge. A way out could be prioritizing certifications and investments in training to address this issue. Another major challenge plaguing organizations attempting to include a data pipeline in their workflow is data downtime.

Every data engineer has experienced periods where the data is unprepared or unreliable (especially during events like reorganizations, migrations, and infrastructure upgrades). Subpar analytical outcomes and customer complaints are two ways this data unavailability can hinder business growth.

According to research, data engineers spend almost 80% of their time maintaining, updating, and guaranteeing the integrity of the data pipeline. Data pipelines can be difficult to handle due to their size and complexity. Automated ways and constant data outage analysis to decrease it are the need of the hour. You can manage data downtime with SLAs and accountability. This is where data observability can come in handy, allowing you to peer into the future, and ensuring a smooth migration of data.

To simplify the implementation of data pipelines, you can opt for cloud-based automated ETL tools like Hevo Data, which offers more than 150 plug-and-play integrations.

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of learning about different types of data pipelines! Let us know in the comments section below!