6 Best Practices For Data Quality In Data Engineering

Every data engineering team knows that data quality is a central factor in success. But what does it mean? Poorly-handled data and inaccuracies can have disastrous consequences, from misguided decisions to lagging revenue.

Every data engineering team knows that data quality is a central factor in success. But what does it mean? Poorly-handled data and inaccuracies can have disastrous consequences, from misguided decisions to lagging revenue. Data engineering teams must perfect their processes for optimal Data Quality (DQ). Incorrectly managed DQ can cost companies up to 25% of their total income. It is paramount that any information used in decision-making be battle-tested—correct, relevant, and up to date. 

As the scope of the project broadens, so will the importance of high-quality standards when handling large amounts of data. This article will explore the best practices to help create a stronghold—the Secret Recipe for Data Kingdoms that champion superior data quality! Let’s get started!

Remove Human Error

Data engineers understand that errors caused by human input must be minimized whenever possible. For example, when users fill out forms with required fields, they must ensure all important inputs are mandatory before submission. To reduce the risk of incomplete or incorrect data, consider replacing text fields with drop-down menus or any other option input wherever applicable. This easy and efficient technique will help minimize potential human errors when inputting data.

Deduplicating And Matching Data

Data duplication is a major data quality issue impacting any system and industry. This issue can arise from duplicate entries, multiple systems, or various data silos, increasing the probability of duplicated information. Consider Netflix, for example – in 2015, their system began producing duplicates for their primary key and left them without an effective solution which shut down Netflix worldwide for 45 minutes. It's clear to see how grave duplicity can be.  

To counteract this detriment, monitoring and reducing duplication is important; this alone enables users to remove up to 30% of potential data errors. However, one must remember that there is no "one size fits all" solution when addressing this; instead, you will need a scalable system capable of automatically monitoring every aspect of your data to prevent duplication. 

Analysis of Source Data

Business data comes in different forms depending on when and where it's stored, so having the right tech stack, infrastructure, and processes is key to gathering reliable and trustworthy insights. Here are some quick tips on what your plan should include: 

  • Understand Your Requirements & Goals: Plan for the type of data you will collect, how it will be stored, and who will analyze it with due consideration given to applications & systems from which to extract the necessary data. 
  • Centralize Data Sources: Extract structured, semi-structured & unstructured information from your apps & systems in a unified data store – commonly via ELT or ETL – to have all relevant business files centralized.
  • Utilize Data Modeling: Before transferring info into a warehouse or data lake, consider using data models that show how different parts of the information fit together & flow. 
  • Analyze Results: While analyzing historical records, use various analytical methods to understand the performance of enterprise operations in real time while predicting future outcomes.

ETL Tool Evaluation

When selecting an ETL tool, it's important to consider several criteria. Pre-built connectors and integrations, usability, cost, scalability and performance, customer support, security and compliance, batch or real-time processing preferences, and ETL vs. ELT decisions must be considered. An effective ETL tool can save your business time and money while providing valuable insights that enable smarter decision-making in finance, sales, customer service, and marketing departments.

Data Acquisition Strategy

Data acquisition is a crucial process that requires careful thought from the start. Society today has so much data streaming in from multiple sources; it's important to be prepared for it and have a strategy on how best to harvest useful information. Here are some key tips we've gathered through our experience: 

  • One-click Ingestion lets you quickly move existing data into new systems and create efficient mapping structures for analytics and downstream reporting tools.
  • Incremental Ingestion can also be beneficial; this technique only extracts modified data from sources, freeing up resources usually spent on ETL duration. 

It's vital to ensure your data ingestion is accurate - otherwise, following stages such as analysis, reports, or decision-making will suffer inaccuracies and unreliability.

Storage Capability – Centralized & Staging

As every enterprise has its own storage needs, here are the primary factors to keep in mind when selecting an appropriate data warehouse: 

  • Cloud vs. On-Prem: Examine whether your business' mission-critical databases require a cloud-based or on-prem solution. Be aware of potential stressors and strains of running an on-prem infrastructure. 
  • Cost & Implementation Time: Carefully review each vendor's pricing plans, as they can differ drastically depending on computing power, storage configurations, and more. When considering implementation time, ensure it doesn't take months - this could prove expensive when you need access to the necessary insights fast enough to exceed competitors! 
  • Tech Stack: If you've already heavily invested in a particular tech stack and most of your info is within this ecosystem, it makes sense to go with it; for instance, Azure, if you're utilizing SQL Server backend with custom integration requirements.
  • Scalability is key when choosing a data warehouse, especially in an enterprise setting. It's important to review the current volume of data, the likelihood that it will grow over time, and if the data warehouse will be able to expand as needed. Additionally, ongoing costs such as storage and compute resources, maintenance costs, and staff time for performance tuning must also be considered. 
  • Reliable IT support should also be considered when considering which tool to use. Check that your preferred option comes with an online community and live support included in the pricing tier – quick access to IT help can save you in times of need.


Data engineering as a service can provide businesses with a competitive advantage, accelerating their ability to make decisions based on collected data. Using the best practices for data pipelines ensures consistent, reliable, and reusable production-ready processes. This way, data scientists don't need to worry about managing data and can focus on maximizing value from their analysis.

At Agilisium, we understand that the future of data lies in the cloud – our team is experienced with both Azure & AWS platforms, ready to assist you through your journey in building a strong foundation of data infrastructure so you can get valuable insights from your data and drive growth & transformation for your business.

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