Why Teams Struggle With Data Quality

Today, businesses are overwhelmed by the volume and variety of data pouring in from various sources. This often leads to poor data quality, resulting in major losses. According to Gartner, organizations lose an average of $12.9 million due to poor data quality.

High-quality data, on the contrary, drives intelligent decisions and fuels accurate algorithms.

But teams face significant challenges to maintaining data quality—from inconsistent formats and outdated systems, to human errors and data silos. As AI becomes more prominent, so will the issue of data quality.

Let’s look into why teams struggle with data quality, and discuss strategies and actionable steps to improve it.

Click to skip down:

• Why Do Teams Struggle With Data Quality?
• Why Do Data Quality Issues Emerge?
• What Should Data Teams Do?
• How can Dataddo help you improve your data quality?
  

Why Do Teams Struggle With Data Quality?

A strong foundation for data quality lies in effective governance. Improper data governance policies can create a chaotic environment where data is collected, stored, and used without clear guidelines. Teams need clear roles, responsibilities, and standards for how they use and work with data. 

Technology also plays a vital role in data quality management. Misaligned or improper technological solutions can hinder data quality efforts. Selecting and implementing the right tools and platforms for data cleaning, validation, and integration is essential.

A well-chosen technology stack is just as important to achieving high data quality as a sound data governance policy.

Why Do Data Quality Issues Emerge?

Before we explore solutions to address data quality issues, it is important to understand the sources of these issues. Here are some of the most common.

Legacy Systems with Inefficient Internal Processes

Outdated systems can be a major source of data quality problems, because they often lack the flexibility to adapt to new data standards, leading to inconsistencies and errors.

For example, a system designed for a smaller data volume might struggle to handle the increased complexity and volume of data generated today.

Integrations Implemented Without Considering Data Quality

When integrating data from various sources, it's crucial to consider the potential impact of integration on data quality. If not appropriately addressed, incompatibility between datasets from different systems can lead to data integrity issues.

For instance, data can be inconsistent when combined if two systems have different definitions for a particular field. Poorly configured integrations can also introduce errors like duplicate records or incorrect data mappings.

The Sheer Volume of Data

The total amount of data consumed and created globally is expected to go over 150 zettabytes by 2025. This volume presents significant challenges for data quality management, and ensuring accuracy and consistency at scale will only continue to get more complex.

Data Silos

Data silos, where important information is isolated across departments and systems, can significantly hinder data quality efforts. Silos make it difficult to get a complete and accurate view of the information.

This can lead to inconsistencies, redundancies, and a lack of trust in the data. A unified data management approach is essential to break down data silos and ensure data quality across the organization.

Low Level of Data Management Culture

Data quality issues are likely to occur when data quality is not prioritized, or when there is a lack of awareness of problems that cause low-quality data. When an organization doesn’t emphasize the value of high-quality data, discrepancies such as inconsistencies between BI tools and source data are likely to arise. Additionally, insufficient data training and resources worsen these problems.

It's important to foster a data-driven culture within the organization, where data quality is valued and prioritized. This can involve:

• Providing training and education on data management best practices
• Investing in data quality tools and technologies
• Establishing clear data governance policies

What Should Data Teams Do?

Let’s understand some basic steps data teams can take to improve data quality.

Assess the Current Data State

The first step to improving data quality is to assess the current state of your data. This involves conducting data profiling and audits to identify issues such as inconsistencies, duplicates, and missing values.

Tools and methodologies for things like data quality assessment and data lineage can be used to effectively assess the data.

Engage Stakeholders

Engaging stakeholders is crucial for successful data quality management. For instance, you can empower your stakeholders to participate in data quality management by assigning them roles and receiving feedback from them.

Moreover, defining quality standards in collaboration with stakeholders ensures that the data meets their needs.

Understanding the unique data requirements of different departments can help you prioritize data quality efforts and allocate resources accordingly. Identifying and prioritizing critical data assets allows for a focused approach to improving data quality.

Effective Planning

Developing a robust data governance framework provides a foundation for data quality management. This framework should outline data management roles, responsibilities, policies, and procedures.

Planning for future data needs and scalability ensures that your data quality infrastructure can accommodate growth and change.

Monitoring and Taking Action

Implementing data quality checks and alerts helps to prevent errors from propagating through the system.

Using dashboards for daily or hourly data quality monitoring provides visibility into data quality trends and enables proactive action. Data cleansing and enrichment tools can be used to maintain data quality by correcting errors, filling in missing values, harmonizing disparate formats, and filtering out corrupt or anomalous data.

Balance Between Organizational vs. Technological Solutions

While technology can significantly improve data quality, it’s important to remember it is not the complete solution to data quality management. A strong organizational culture around data quality is equally essential.

Balancing between implementing technological tools and fostering a data-driven culture is key to achieving long-term success in data quality management.

How Can Dataddo Help You Improve Your Data Quality?

Struggling with data quality issues like duplicates, inconsistencies, and non-harmonized formats? Dataddo's Data Quality Firewall is your first line of defense. By configuring individual rules for every column, you can granularly control what data enters your downstream systems, solving the biggest data quality issues right at the point of data collection.

Dataddo is an any-to-any data integration tool that offers ETL, ELT, reverse ETL, database replication, and direct app-to-BI-tool integrations. Its no-code interface is designed for business teams, but developers can connect to Dataddo's unified API to configure any custom integrations via code.

• Huge connector library. Apps, BI tools, databases, and files. Custom connectors on request in 10 days.
• Embedded data quality tools. Data Quality Firewall, real-time data monitoring, detailed logging, automated data cleansing, data blending, and more.
• Flexible deployable. Deploy via UI and API. Subscriptions via AWS/Azure/Google Cloud Platform. Technology-agnostic.
• Fully secure. SOC 2 Type II certified and compliant with major data privacy laws in all regions across the globe.
• Predictable pricing. Always know what you're paying; never be shocked by your bill.