The importance of data quality in data engineering

Table of Contents

Categories

Sign up for our newsletter

We care about the protection of your data. Read our Privacy Policy.

High-quality data is a central aspect of all organizations. From an operational standpoint, it allows organizations to avoid business process errors that can lead to mounting operating expenses. In a financial context, maintaining high-quality, reliable data reduces the cost of identifying and fixing bad data within a system. Maintaining data quality is integral to its wider application in data processes such as analytics. Whilst increasing the accuracy of business decisions, high-quality data can also expand the use of BI dashboards, giving organizations a competitive edge over rivals.


Data processing in data engineering

Data processing in data engineering

  With so many insights to be unlocked and so much potential for development, an increasing number of organizations are acknowledging the need for data engineering solutions to manage this valuable resource. As the gateway between raw data and reliable data application, data engineering ensures that only accurate data is utilized for analytics and business insights. In data-driven organizations, impactful financial and marketing decisions are founded on the output of data engineering and analytics teams, so data accuracy is essential. Take a closer look at how data quality informs data engineering and how ProCogia’s data engineering solutions can help your organization below.


What determines high-quality data?

What determines high-quality data?

  At the heart of all good data is accuracy. Other qualities that data should include are:

  • Consistency: to ensure that there are no conflicts between the same data values across different data sets and systems
  • Completeness: to assure that data sets contain all of the correct data elements that they require, without duplications
  • Validity: to ensure that data contains the correct values within the proper structure
  • Compliance: to meet the standard data formats and guidelines that govern your organization
  • Relevance: to ensure your data is up to date and reflects the present, ever-changing data landscape.

What are some of the emerging data quality challenges in data engineering?

What are some of the emerging data quality challenges in data engineering?

  As the volume of data being produced and processed by organizations continues to grow at scale, so too have the demands of data quality around compliance and storage. The importance of meeting regulatory guidance and privacy and protection laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) is of the highest importance in data engineering solutions. Equally, vigilance around the quality of unstructured data and the secure storage of structured data in cloud systems for efficient data workflow is essential. With the increasing number of big data systems and cloud computing being incorporated into data engineering practices, data protection measures require organizations to grant individuals access to the data they collect about them. This means there is a constant requirement for data to be quickly and easily accessed, without inaccuracies or inconsistencies, making data quality integral. Common data pipeline challenges include big changes in data volumes and sizes, mismatched data in migration and resource-intense solutions. Bad data can significantly impact organizations. From inaccurate analytics to ill-informed business strategies and lost sales opportunities, bad data can lead to errors that are damaging both financially and reputationally. Take a closer look at how data quality informs data engineering and how ProCogia’s data engineering solutions can help your organization below.


How is data quality managed in data engineering?

How is data quality managed in data engineering?

  To meet an organisation’s data quality requirements, including providing accurate and consistent data, data engineers create efficient data pipelines. The processes within the pipeline are designed to derive useful data insights to inform reliable business decisions and may include:

  • Validating data input from both known and unknown sources to enhance its accuracy
  • Monitoring and cleansing data to verify it against standard statistical measures and matching defined descriptions
  • Removing duplicate data to maintain data quality and integrity across shared folders and documents.

Within the pipeline, there will be extensive data testing to ensure only accurate data forms the models that inform data dashboards. Data models are also regularly updated according to a schedule before the data is retrieved from the data warehouse to update dashboards across an organization. Performing dashboard updates behind automated tests ensures that BI data is only updated if it meets the current standards and expectations, so only high-quality data is processed.


Discover ProCogia’s data engineering solutions

As a trusted data engineering company, ProCogia delivers game-changing solutions for organizations operating at scale with large datasets. A key aspect of our data engineering solutions is the assurance of data quality, through extensive data profiling, cleansing, wrangling and monitoring. We work closely with your organization to adopt a cloud-first approach whilst driving down computing costs and utilizing high-quality data for your operations. Allow our expert team of data engineers to take the pressure of managing your data quality off your hands.

Author

Keep reading

Dig deeper into data development by browsing our blogs…
ProCogia would love to help you tackle the problems highlighted above. Let’s have a conversation! Fill in the form below or click here to schedule a meeting.