How Data Quality Impacts Analysts—& What You Can Do to Improve It

Nothing is more challenging (or frustrating) for an analyst than poor data quality, because it disrupts their ability to deliver value. For our purposes, we’ll define “poor data” as inaccurate, inconsistent, or stale. Poor data can lead to a slew of issues: When reporting falls short, advanced analysis stalls, users struggle to self-serve their data needs, and the consequences reverberate throughout the organization. Business decisions become slower and less reliable, hindering agility, innovation, and strategic foresight.

In this blog, we’ll explore what defines poor data quality, how it blocks analysts from driving value, and why understanding these challenges is crucial for business success.

What is poor data quality?

Low-quality data fails to meet one or more of these standards:

• Accuracy: The data doesn’t reflect real-world values or business objectives
• Consistency: Conflicting or redundant information leads to confusion
• Completeness: Missing data limits analysis
• Timeliness: Outdated data reduces relevance
• Reliability: Data pipelines become inconsistent or riddled with errors

Good decisions require high-quality data. It’s essential for analysts to report accurately, uncover actionable insights, forecast future trends, and empower teams through experimentation and strategic enablement. When data can’t be trusted, these tasks ultimately lead to inefficiencies and missed opportunities.

How poor data quality blocks analysts’ work

Diminished reputation

When dashboards and metrics are based on faulty data, stakeholders lose trust in the insights provided. This mistrust diminishes the perceived value of analytics and leaves analysts out of key projects.

Value reduction

Inconsistent data makes deep dives and forecasting unreliable, preventing analysts from delivering forward-thinking strategies and actionable trends.

Self-service failure

Poor data quality undermines teams’ ability to self-serve. Frustrated by flawed dashboards, users will require analyst help for basic requests, creating bottlenecks.

Wasted time

Instead of delivering strategic insights, analysts spend countless hours cleaning and reconciling data errors. These operational costs compound over time, pulling analysts away from the high-value work of actually analyzing the data.

The business impact of poor data quality

Slower decisions

When insights are delayed due to insufficient data, businesses lose the agility to act on opportunities or respond to challenges proactively.

Missed opportunities

Flawed data obscures crucial trends and risks, leading to decisions based on incomplete or incorrect information.

Stifled self-service

Self-service becomes impossible without reliable data, leading to inefficiencies and over-reliance on analysts for simple tasks.

Eroded trust

Inconsistent reporting damages confidence in analytics across the organization, making it harder to rally teams around data-driven decisions.

Garbage in, garbage out

Feeding AI and augmented analytics poor-quality data will churn out equally poor results, leading to poor business decisions if companies assume the accuracy of their AI's supposed artificial expertise.

How analysts can improve data quality

Fix issues at the source

Collaborate with engineers to address pipeline issues upstream. Fixing data quality at its origin saves time downstream and builds trust in reporting and insights.

For example, companies like SafetyCulture have used Amplitude to fix issues at the source effectively by identifying and cleaning up unnecessary event-tracking data within their analytics. This led SafetyCulture to a more streamlined and accurate understanding of user behavior.

Standardize metrics and reporting

Make sure that everyone in your company has a shared understanding of the terms you’re using.

• Establish clear definitions for key metrics
• Ensure consistency across dashboards
• Develop governance frameworks for periodic auditing to prevent discrepancies

Educate your teams on data’s impact

Help stakeholders understand how data affects decisions and workflows. Champion governance tools and implement anomaly detection or alerts to catch errors before they disrupt operations.

Cross-team collaboration initiatives, such as session replay parties, empower your organization to make smarter decisions and better understand how data impacts everyone, from leadership to data scientists to marketing.

Use tools for real-time data quality monitoring

Invest in tools that validate and monitor data quality in real time. These tools allow analysts to focus on uncovering insights instead of fixing broken data.

For example, Amplitude delivers:

• Accurate, real-time insights for reporting and analysis
• Governed, self-serve data stakeholders can trust
• Time savings, enabling analysts to focus on strategic work that drives their businesses forward

Better data, better decisions

On the other (brighter) side, trusted data empowers analysts to report accurately and stay compliant, deliver deeper and more impactful analyses, and reduce bottlenecks across the organization by enabling stakeholders to self-serve. By addressing data quality problems, businesses can unlock the full potential of their analytics teams and position themselves for innovation, agility, and data-driven success.