Data analytics, a cornerstone of the wider digital transformation, led organizations—particularly innovative startups and more agile emerging companies—to quickly recognize the potential of data as an objective performance measure, paving the way for the adoption of advanced data pipelines.
From here, the idea of basing decision-making on business intelligence began to take root. Many companies have subsequently embraced business analytics and incorporated it throughout their workflows. The early startups were the first in their sector to adopt an internal data pipeline. As a result, they were also able to operate with greater flexibility and efficiency, thus proving the competitive value of large-scale information processing.
Today, you’d be hard pressed to name a company not focused on becoming a data-driven entity. Yet, emerging businesses without the established infrastructure to orchestrate data processing must first build their information pipeline.
What is a Data Pipeline and Why Does it Matter?
In the simplest sense, a data pipeline is analogous to a regular city water system. The city stores the water in a large reservoir, just like a typical data storage warehouse. However, in order for residents to utilize this resource, the water needs to flow through a series of pipes from its original source to the end-users. Along the way, the city treats, filters and possibly even pressurizes the water to turn its raw format into a usable resource right out of the tap.
In much the same way, an organization’s data pipeline pulls information sets from their origination point – a consolidated warehouse or a dispersed set of sources – to an analytics processing center. Here, you can extract insights without impacting the collection of information. And just as the city treats the water in its system, the pipeline uses data management tools to “clean” and organize this information along the way.
You can get a more granular understanding of this process’s mechanics by breaking it down into its component stages:
- Ingestion: The first stage is the collection of data across various sources.
- Transportation: Next, the data is transferred from the collection points to a storage and processing center. This may be a data warehouse, lake or cloud storage, depending on the organization’s infrastructure.
- Processing: Data is cleansed before analysis by eliminating errors and inconsistencies, while sources are amalgamated through integration and enrichment with additional information sets.
- Analysis: Here, complex algorithms, machine learning models, aggregations or statistical methods are used to derive intelligible insights from the information.
- Reporting: Insights are then conveyed through dashboards that visualize the findings in graphs and charts.
- Maintenance: The flow of a data pipeline requires regular maintenance from end to end to ensure efficiency and proper governance at all points.
4 Creative Strategies to Build an Advanced Data Pipeline
The accuracy of the insights coming out of the pipeline is entirely dependent on the quality of the wider business intelligence process. This underlines how critical it is to optimize every stage to achieve the best results.
Not all startups or emerging businesses are necessarily overseeing their data management protocols effectively during their initial stages of growth. It’s both a challenge and an opportunity for newer organizations to develop a strong data pipeline from the get-go.
With this in mind, there are several strategies new businesses can implement to overcome the common obstacles in data pipeline development.
- Utilize a Modular Design and Recognize Design Patterns
There exists a constant need to monitor, update and potentially alter components of the data pipeline. For this reason, it’s crucial that the system can be modified without significant disruption.
That’s why a modular structure is one of the best approaches. It breaks down the cohesive system into separate building blocks that can be altered or replaced without impacting other parts.
Essentially, a modular design is one in which each function within the pipeline is a self-contained unit in the processing workflow. This contrasts with monolithic pipelines, where the entire system is impacted if any component parts are modified or scaled.
Modular designs are particularly beneficial to startups and emerging businesses. Not only is the framework more flexible and responsive, but it also allows you to scale more readily. As data volume grows, you can enhance specific modules without the need to scale the entire pipeline.
Furthermore, you can improve or alter functionality more readily to meet the rapidly changing demands of a growing business.
- Collaborate With End-Users
A successful data pipeline isn’t necessarily one with the most complex or feature-rich design. Oftentimes, the prime determiner of how much value you can extract from your data, especially in advanced data pipelines, is how closely the system designers worked with the eventual end-users.
Therefore, designers should engage the users in their process from the outset. They can provide valuable input into the intended uses of the pipeline. This input, in turn, allows engineers to create a solution that’s tailored to the specific needs and applications of a department. Most likely, users won’t know exactly what technical specifications they want.
Generally, the best way forward is to start with an open dialogue about the types of insights that a user wants to obtain from the data. From there, it’s up to data pipeline architects to balance functionality with user-friendliness. The goal is to create a system that encourages and rewards data-driven decision-making.
An iterative design process that incorporates cycles of prototyping, testing, analyzing and refining naturally pushes the final outcome toward a more practically useful implementation. And, as a result, the data pipeline will transform and transport data in the most streamlined way possible.
- Use a Configuration-Based Approach
Configuration-based pipelines allow you to disentangle the properties of your data storage process from its logic. Put simply, in a traditional hard-coded pipeline, details are deeply embedded in the logic. As such, it’s exceedingly difficult to alter or modify any commands within the code – especially if it contains redundant values. On the other hand, configuration-based pipelines store the details within a separate folder, making it easier for engineers to gain access and make alterations.
The process of changing particulars in a traditionally structured data pipeline requires coders to delve into logic. As a result, hard-coded pipelines will likely grow brittle and outdated sooner. Over time, the updates and alterations for these pipelines simply can’t keep pace with their configured counterparts.
- Leverage Automated Tools and Techniques
The amount of data the average company generates is staggering. Even small businesses and startups are creating endless trails of business intelligence every day.
Without some degree of automation, the sheer quantity of information funneling through your data pipeline is likely to overwhelm even a well-staffed team of data engineers. That said, recent progress in machine learning and AI has taken data management to a whole new level. These advancements allow for the handling of vast and complex information landscapes with minimal manual intervention.
Automation takes repetitive tasks off your hands. Plus, the ingrained logic of computer processing eliminates human error from such rapid and orderly tasks. You can integrate features such as automated data checks to improve the quality and consistency of data across the pipeline. This then improves the accuracy of subsequent data-based decision making.
The Transformative Power of an Advanced Data Pipeline
For startups and newer companies, an advanced data pipeline isn’t just a technical asset, it’s a strategic imperative. By granting the capacity to base goals and strategies on objective analysis of both real-time and future performance, startups and emerging businesses alike can go further in fewer steps.
In leveraging cutting-edge pipeline design principles like modular structures, end-user collaboration, configuration-based approaches and automation integrations, organizations can set themselves apart in terms of agility and efficiency.
The knowledge and skills required to build a user-friendly yet highly functional pipeline are in limited supply. You may not yet have the internal talent to effectively create the scalable solutions they’ll need for the future. As such, you may need to enlist external expertise.
Growth Acceleration Partner’s (GAP’s) teams of highly experienced software engineers are skilled at creating bespoke data solutions and software. Their work meets the highest levels of functionality and efficiency, allowing startups to scale fast and gain a competitive edge.
Reach out to learn more about partnering with GAP.
