The Cost of Poor Data Quality: A Comprehensive Analysis

If you're a data professional, business owner, or business intelligence (BI) analyst, you know how crucial accurate data is for strategic decision-making. Data scientists spend about 60% of their time preparing the data for analysis while collecting datasets accounts for only 19% of their workload. 

Every year, organizations lose around $12.9 million on average due to poor data quality. Beyond its immediate revenue impact, bad data complicates data management and affects decision-making. Therefore, any data-driven company must get the correct data and deliver it to the right people.

This article discusses the impact of poor data on business outcomes and presents strategies to improve data quality to drive growth.

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1. What is poor or bad data?
2. The impact of poor data quality on your business
3. Case studies: The impact of poor data quality on big companies
4. Steps to estimate poor data quality costs
5. Strategies to improve data quality
6. Dataddo's inbuilt data quality mechanisms

What Is Poor or Bad Data?

Poor or bad data refers to information that is inaccurate, incomplete, inconsistent, outdated, or not properly maintained, secured, or validated. It is data not fit for its intended purpose, or lacking the quality required to support the outcomes it is being used for. Poor data can arise from various sources, including human error, system issues, data integration problems, and lack of standardized data entry processes.

Below is a breakdown of various categories of poor data.

• Inaccurate data: This applies to data that contains errors, typos, or simply doesn't reflect reality. For instance, a customer's address with a wrong zip code, or a product listed with an outdated price.
• Incomplete data: This refers to data that is missing crucial information. Imagine a customer profile without an email address, or a sales report lacking quantity figures.
• Inconsistent data: This describes data that follows no set format or has conflicting entries within itself. Examples include customer names spelled differently across records, or product descriptions varying in length and detail.
• Irrelevant data: This applies to data with no bearing on the task at hand. For instance, social security numbers in a marketing campaign list would count as irrelevant data.
  
  

The Impact of Poor Data Quality on Your Business

From multinational corporations to small businesses, data quality is crucial in driving strategic initiatives, enhancing operational efficiency, and informing innovation. However, not all data is created equal. Poor or bad data often goes unseen, posing significant challenges and risks to organizations.

• Wasted resources: Inaccurate or incomplete data necessitates additional time and resources for cleaning and correction. This can divert valuable effort away from core business activities.
• Ineffective decision-making: Flawed data analysis leads to misguided decisions. This can result in missed opportunities, wasted marketing spending, or product launches that don't resonate with your target audience.
• Operational inefficiencies: Poor data quality disrupts workflows and hampers operational efficiency. For example, inaccurate inventory data can lead to stockouts and delays in fulfilling customer orders.
• Damaged customer relationships: Incorrect customer information can lead to misdirected marketing campaigns, delayed responses to inquiries, and overall frustration for your customers. This ultimately erodes trust and loyalty.
• Compliance issues: Depending on your industry, regulations might mandate specific data quality standards. Failure to meet these standards due to poor data can result in hefty fines and legal repercussions.
• Reputational damage: News of data breaches or persistent customer service issues stemming from poor data management can severely damage your brand reputation. 21% of companies acknowledge suffering reputational harm due to inaccurate data.
  
  

Case Studies: The Impact of Poor Data Quality on Big Companies

Since companies rely heavily on accurate and reliable data to drive their decision-making processes, poor-quality data can significantly affect even the largest corporations. When data quality is compromised, the consequences can be severe. 

Let’s explore a few real-world examples of how poor data has affected big companies and resulted in million-dollar losses. 

1. Unity's Audience Data Corrupts AI Models

Unity Technologies, known for its real-time 3D content platform, suffered a data quality incident in Q1 2022. The inaccuracies in its Pinpointer tool caused bad data ingestion from a large customer, leading to a $110 million revenue loss. Unity's shares dropped by 37%. Its CEO Ricicitello pledged to improve data quality to regain investor confidence.

2. Samsung’s $105B “Fat Finger” Data Entry Error

A striking example of how small data mistakes can rapidly escalate occurred at Samsung Securities. An employee accidentally fat-fingered an incorrect data value and mistakenly issued billions of shares to employees during routine dividend payments. The error resulted in 2.8 billion "ghost shares" being issued, about 30 times the total number of existing shares.

3. Equifax's Inaccurate Credit Scoring Fiasco

The major credit bureau Equifax faced heavy regulatory fines, lawsuits, and a massive dent in its credibility when inaccurate credit scores were generated for millions of consumers over several years. The scoring errors risked lending decisions and shook public trust in Equifax’s data practices.

Steps to Estimate Poor Data Quality Costs

Since low-quality data can result in significant business loss, understanding how to estimate these costs is crucial.

Step 1: Assess Your Data Volume

Evaluate the quantity, sources, and types of data in your system, including new entries and updates. This will allow for an accurate gauge of managed data volume, alongside considerations such as storage and management. You can take this data from audit logs or data comparison snapshots taken at different times. 

Step 2: Error Rate Analysis

Determine the percentage of your data affected by inaccuracies or incompleteness. You can get this estimation from data quality logs, data profiling, or insights gathered from interviews and survey responses conducted during the situational analysis phase. 

If, for example, you discover that a significant portion of your product data is inaccurate, this signals a need for immediate action. This may involve implementing data validation processes, conducting thorough data cleansing initiatives, etc.

Step 3: Calculate Direct Costs

Measure the time and resources spent identifying, correcting, and cleansing poor-quality data. This may also include considering the costs of third-party services for data cleaning. Estimating the cost of poor data entry typically involves three categories, each with its own criteria:

• If identified during entry, the default cost range may be, for instance, $0.5-$1.
• If cleansed after entry, the default cost range may be $1-$10.
• If left unaddressed, the default cost range may be $10-$100.

These cost ranges are illustrative and may vary depending on factors such as the specific organization's circumstances, the complexity of the data, and the effectiveness of remediation efforts.

Step 4: Identify Missed Opportunities and Inefficiencies

Consider the indirect costs such as lost sales opportunities due to outdated customer data or production inefficiencies like overstocking or understocking of products due to inaccurate inventory records. A relatable scenario might be a marketing campaign that fails to reach its target audience because of outdated contact information.

Strategies to Improve Data Quality

Implementing strategies to improve data quality is essential to ensure the integrity and reliability of organizational data. 

Below is a list of a few strategies that can help you enhance your data quality. 

• Establish data governance policies: Effective data governance policies provide the framework for managing data quality within an organization. Establish a centralized governance model with clear ownership over data policies, standards, and management. By defining clear roles, responsibilities, and procedures for data management, you can ensure consistency and compliance with regulatory requirements. 
  
  
• Offer data quality training: Even with automation, employees remain critical data stewards. Organize tailored training sessions for data entry best practices, validation techniques, and the importance of maintaining data accuracy. Hands-on training using real-world examples can also help reinforce learning and encourage practical application. 
  
  
• Keep documentation accurate and up-to-date: Keeping documentation up-to-date regarding your data sources, processes, and systems is essential for users to grasp the context of the information they handle. It helps users understand where the data comes from, how it's altered, and the assumptions behind its analysis. Accurate documentation prevents misunderstandings that could affect data interpretation. 
  
  
• Implement data validation techniques: Data validation techniques such as profiling and schema validation help identify errors and inconsistencies in data. Establish validation rules and constraints to enforce data quality standards at various data lifecycle stages. Automation of validation processes reduces manual effort and improves efficiency, while error handling mechanisms ensure timely resolution of validation errors. 
  
  
• Implement feedback loops: Feedback mechanisms enable end-users to report data quality issues and concerns, facilitating continuous improvement. Analyzing feedback data helps identify recurring patterns and root causes of data quality issues, guiding corrective actions. 
  
  
• Use data quality tools: Invest in data quality tools and software solutions that automate data profiling, cleansing, enrichment, and monitoring processes. Organizations can benefit from Dataddo’s suite of data quality tools, including a Data Quality Watcher, a Data Quality Firewall, detailed activity logs, and an anomaly detector for easy troubleshooting and fixing data errors quickly. 
  
  
• Monitor data quality metrics: Define key performance indicators (KPIs) and metrics to measure data quality across various dimensions such as accuracy, completeness, consistency, and timeliness. Implement monitoring mechanisms to track data quality metrics in real-time or at regular intervals. This helps detect issues early and take corrective actions before they affect business operations. 

Dataddo's Inbuilt Data Quality Mechanisms

Robust data quality and governance require scalable, intelligent data management platforms. That’s where Dataddo provides a critical advantage.

Dataddo offers a comprehensive solution for ensuring data quality, incorporating a range of built-in mechanisms to secure against inaccuracies and anomalies. With features such as an anomaly detector, filtering system, and rule-based integration triggers, Dataddo empowers users to maintain data integrity and prevent the flow of suspicious datasets downstream.

Dataddo has helped several businesses overcome data integration challenges and optimize their data processes. For instance, Livesport, a leading sports data provider, faced data integration hurdles across various sources. Despite utilizing BigQuery, they needed to streamline integration without overburdening their team. Turning to Dataddo, they found a solution for synchronizing data from diverse sources, ensuring fixed pricing and robust support.

Explore Dataddo today to enhance data quality and improve decision-making!