Building effective data pipelines is critical for organizations seeking to transform raw research data into actionable insights. Businesses rely on seamless, efficient, scalable pipelines for proper data collection, processing, and analysis. Without a well-designed data pipeline, there’s no assurance that the accuracy and timeliness of data will be available to empower decision-making.
Companies face several challenges in pipeline establishment, including handling large volumes of data, integrating data from disparate sources, and ensuring compliance with data privacy regulations. A careful and strategic approach is necessary to garner the insights required to drive growth, optimize operations, and stay competitive.
Why Effective Data Pipelines Matter
Conceptually, data pipelines are comparable to traditional supply chains. In a well-oiled supply chain, the components for a product are sourced, shipped, constructed, and made usable and accessible in a timely and efficient manner for consumers. An effective supply chain is streamlined so the product is available as quickly as possible. It has safeguards in place to prevent bottlenecks, avoid shortages or excesses in accordance with market demand, and ensure compliance with all shipping, safety, and production regulations. Without a reliable supply chain, it’s impossible for an organization to deliver its product on time in a quality manner.
Similarly, data pipelines involve the streamlined movement of a product from its raw form into a finished, purposeful product. This includes pieces of research that companies acquire through various touchpoints with their customers and consumers, then store in data centers or on-premises hardware for immediate access, analysis, and, eventually, translation into meaningful insights for chief data officers (CDOs) and other high-level decision-makers within an organization. A data pipeline is the digital supply chain that moves and translates raw data into meaningful, understandable patterns that guide decision-making at the organizational level.
It’s important to note that the systems, technologies, functions, and rules that govern data pipelines and physical supply chains are quite different. Still, the comparison helps visualize why data pipelines are so critical. Comparable to how physical supply chains form the basis for effective product management, digital data pipelines are the backbone of the ever-increasing reliance on data collection and analysis for decision-making. If CDOs and other decision-makers want to translate their raw data into meaningful insights in a timely and compliant manner, then building and maintaining an efficient and accurate data pipeline should be a priority.
The Dangers of Disorganized Pipelines
In the big data world, most organizations rely on multiple data pipelines to influence their decision-making. This is true whether a large company like Walmart uses information from its loyalty members to determine the most profitable Black Friday deals and meet projected demands or a small tech startup analyzes quarterly costs to bundle its services most efficiently. This doesn’t mean all data pipelines operate identically or are created equally. An effective data pipeline is designed with specific goals and purposes in mind and implements tools, strategies, and benchmarks that align with those goals. Without this level of attention to intention, data pipelines can go from being a crucial element for success to a costly downfall.
For example, consider a fictional tech startup and one of its less-savvy competitors. LittleCloud Inc. and Mondo Microservices are niche startups that help nonprofit organizations migrate from on-premises and legacy systems to cloud-based microservices architectures to save organizations unnecessary hardware maintenance and data storage costs. Both organizations have small budgets, and they know it’s critical to rely on data analysis to make decisions about how to maximize their costs, deliver quality services, identify and address their clients’ needs, stay compliant with data storage laws and regulations, and determine the tools, systems, and strategies needed to maintain a consistent workflow.
Mondo Microservices decided to collect as much data as possible from its clients. Its data pipeline ingests multiple kinds of information — projected date ranges and timelines, multiple quotes for services, ongoing open comment forms, weekly email surveys, star reviews, and check-ins from numerous touchpoints, including its app, site forms and pop-ups, emails, newsletters, and phone calls.
Meanwhile, LittleCloud collects only the essential information on its service costs and implementation timelines. It provides all clients with a standardized form that requests essential information like their size and operating budget, the services they provide, in addition to the specs of their current on-prem systems. At the end of LittleCloud’s services, the company emails clients a follow-up survey to collect feedback.
Soon, Mondo Microservices will have a mountain of data, while LittleCloud will have significantly less. This doesn’t mean Mondo has the advantage. In fact, Mondo is faced with standardizing and processing every piece of data collected, a timely and costly job that the company wasn’t prepared for. Then, once the data is finally processed, Mondo must store all of it, spending a significant proportion of its overall budget and resources on storage in data centers. Mondo realizes it has abundant data from numerous sources, making it difficult to analyze, identify patterns, and turn the data into meaningful insights. Mondo’s poor planning and foresight have transformed what could be an efficient data pipeline into a series of backlogs and silos, batches of incomplete or incompatible data, costly storage practices, and latency in decision-making that ultimately hurts its performance and reliability. It also risks accidentally violating compliance and regulatory laws for data storage due to its disorganization, rushed practices, and lack of oversight.
On the other hand, LittleCloud has made the most of its comparatively smaller data pool by ingesting data from fewer incompatible sources, requiring less storage, and recognizing patterns in smaller sample sizes. This approach leads to more effective decision-making. It can monitor its data closely and transparently, and the company is ready to alter its practices or encrypt any information should compliance regulations change. At first glance, LittleCloud might seem less focused on data analysis than its competitor, but the intention and framework behind its data pipeline means LittleCloud can optimize the information collected to provide better services faster.
Building the Perfect Pipeline: Concrete Steps and Strategies
The examples of LittleCloud Inc. and Mondo Microservices seem simple, but an alarming number of organizations are trending toward the Mondo approach, with one recent report determining that over half of all companies rely on inaccurate data in their pipelines. This doesn’t necessarily mean that relying on big data will always lead to poor decision-making. It does mean, however, that it’s vital for organizations to focus more on the intentions, strategies, tools, and contexts behind their data pipelines. Organizations can confidently build data pipelines that work for them by following these steps:
- Establish organizational goals — As demonstrated through Mondo Microservices, data ingestion for the sake of data ingestion doesn’t pay off. Decision-makers and CDOs can create a concrete list of goals and intentions behind their data collection and make all future decisions with those goals in mind.
- Evaluate available resources and capabilities — One of the most destructive moves in building a data pipeline is to overstretch a company’s resources. Companies that want successful and seamless data pipelines need to stay within their budgets for data processing, storage, and analysis. It’s paramount for organizations and leadership to consult with their financial teams to create an operating budget for tools, training, and maintenance.
- Shortlist and prioritize tools and functions — After determining a budget and overall goals, the next step is for leadership to research the many existing tools that aid data pipelines. These tools can be prioritized based on need, function, and cost. Smaller organizations can opt for something off-the-shelf, while larger organizations with more resources can look for specialized tools and combinations.
- Work with consultants on implementation and training — It’s crucial to consult with experts who can provide frameworks, best practices, and proper support for moving to new systems. These consultants can be in-house or hired as outside third parties to assist with implementation. This essential step ensures a seamless integration of new data analytics tools.
- Launch with a phased approach — Begin with a pilot program that focuses on improving a data pipeline for a single job function, like human resources, sales, or marketing. Then, scale the process to other job functions using those insights. It’s crucial to recognize that employing a new data pipeline is an ongoing process that relies on consistent monitoring and transparency.
- Establish and measure metrics for success — As the new systems and tools take root in the data pipeline, ensure dedicated job functions are assigned to measure successes and identify new challenges.
- Maintain flexibility to monitor and adjust policies and practices — Be aware that compliance and regulations for collecting, storing, and using data are moving targets. Laws and regulations surrounding data change frequently, and any successful data pipeline needs to be able to access its data easily to encrypt or remove it from its stores in accordance with the law. This is another reason it’s vital to have a dedicated team overseeing the overall data flow throughout the pipeline.
A Competitive Advantage
Few things are more crucial to an organization’s success than managing its data pipelines. This is especially true with the abundance of power from artificial intelligence (AI) and machine learning (ML), making it imperative to accurately collect, process, and analyze data. With such massive data-driven decision-making developments, it’s essential for organizations to implement and maintain effective data pipelines. Without one, using data to make informed, timely decisions is an increasingly daunting task prone to error, downtime, and wasted resources. Considering this reality, constructing a thorough framework for optimizing, implementing, and maintaining data pipelines across teams and functions is a critical investment.
