How to Build Reliable Data Processing Pipelines

Data Engineering
Dec 2, 2022
How to Build Reliable Data Processing Pipelines

This article was contributed by Loretta Jones, VP of Growth at

When data processing pipelines are reliable, businesses can be confident that their data environment is operating efficiently and delivering information that can be used for better decision-making. This helps business leaders predict challenges, increase efficiency, and garner insights by understanding how historical trends relate to current trends.

However, building a data pipeline that does all of these things is not always straightforward. Here are some factors businesses should consider when establishing a data pipeline and how they can build one that is reliable and effective.

The role of effective data processing in ensuring competitiveness and profitability

Improve operational and cost efficiency with complete visibility over all business data

Business leaders are always looking for new ways to maximize operational output. Organizational efficiency has become increasingly important due to productivity ceilings, talent shortages, and increasingly demanding customers.

The first step in optimizing business processes is understanding the impact each one has on the entire business and analyzing the connections between seemingly unrelated business processes.

Assets that can be utilized across different business verticals or operational departments can sometimes go woefully underused. Data analytics processes built on top of effective data observability can give business leaders unprecedented insight into their operations and enable them to find areas where costs can be cut or efficiencies can be maximized.

Gain a better understanding of customers’ needs and preferences

Business leaders can sometimes make decisions that are in the best interest of their organization from an operational perspective but might not be what customers are looking for from the company. Data collection and analysis can help businesses ensure that they are always aware of what their customers need and can align themselves with the roles that their customers have assigned to them.

Product development, updates, and price changes are usually the result of lengthy strategic planning and discussion. It is therefore prudent that these plans are designed with an accurate understanding of the customers they are meant to serve. Effective data pipelines allow businesses to stay ahead of constantly evolving customer preferences and respond to them in a timely manner.

Overcome deeply entrenched information silos to receive a holistic overview of their organization

Data has become essential to all aspects of modern enterprises. Departments within an organization often build their own data processes to serve their unique data needs. However, these disparate and unconnected data processes can lead to a further entrenching of existing information silos.

On average, companies draw on 400 different data sources. This can make it extremely difficult to create a complete picture of an operation. While data can give business leaders greater insight, a lack of effective data pipelines to deliver these insights can lead to an incomplete overview that they then use for decision-making.

Companies draw on an average of 400 different data sources according to Pipa Partners
Image sourced from Pipa Partners

4 best practices when establishing a data processing pipeline

1. Ensure that data analytics or collection processes are intuitive and easy to use for non-technical staff members

Data sharing, storage, and analysis often come easily to staff whose jobs are technical in nature. However, data processes rely on consistent participation from employees across the organization to be useful. In a study analyzing the top complaints of business users that conduct data analytics, 74% of respondents cited “difficult to use” as their top data analytics complaint.

When designing data processes to bridge the gap between operational and administrative departments, businesses should always consider the least technical staff members. Data processes should not be overly disruptive to daily workflows nor should they require significant technical expertise.

2. Design data pipelines that align with clear and identifiable business objectives

Businesses can sometimes be indiscriminate in their data collection. This can lead to high data storage costs, excessive strain on data teams, and poor return on investment on organization-wide data programs. Data programs must always be built with a clear set of objectives. Once these concrete objectives are established, business leaders must determine the data points most relevant for reaching those goals.

Data pipelines must then be built to align with these objectives as much as possible. This allows businesses to put the most relevant pieces of data front and center during decision-making processes and optimize reporting.

3. Build an entirely interoperable and fully integrated data stack to serve the needs of different departments while retaining complete data visibility

While business leaders use data pipelines to build a holistic view of their organization, each department uses data differently. This often means that unique processes must be designed to help each team extract the insight they need from data collection and analysis.

However, businesses must be careful not to let these processes create data silos that are difficult to overcome or create gaps between departments. Each team within an organization can use data processes that are unique to their needs as long as they are integrated into a larger data program that serves overarching organizational goals.

4. Consistently evaluate how effective existing data pipelines are at delivering insights to business leaders

Once data pipelines are established, business leaders must take a proactive approach to improve the results generated by these pipelines. Even if data is being sent at quick speeds and with optimal accuracy when data pipelines are built, new solutions are consistently being developed. Businesses should periodically evaluate if their existing data systems are generating the returns they desire. This consistent evaluation also reveals data issues that can then be solved in a timely manner.

Data collection, storage, and analysis can help businesses cut costs, improve operational efficiency, and make better business decisions. The extent to which each business experiences these benefits depends on its ability to create effective data pipelines that deliver actionable, accurate, and reliable insights to business leaders at the right time.

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