In the digital age, the importance of data and its role in driving modern business decisions cannot be overstated. High-quality data across an organization is incredibly valuable and can lead to innovations, actionable results, and profits. In contrast, low-quality data can result in significant costs, both financial and reputational. Take control of your data quality and ensure future success rather than suffering the negative consequences that low-quality data inevitably causes, including company-wide problems and expensive solutions.
What Is Poor Data Quality?
Key Characteristics of Poor-Quality Data:
The key characteristics of Poor-Quality Data include:
- Inaccuracy: If the accuracy of the data being produced is questionable, any analysis or decisions made will rebound to a company’s detriment.
- Incompleteness: If the data is not providing a cohesive platform for benchmarks, performance, and other attributes, it will be virtually impossible to take practical actions.
- Inconsistency: Similarly to issues with data completeness, a lack of consistency in data being produced can cause performance issues that resonate throughout a business.
- Differences in Data: While some degree of different data is helpful for analytical purposes, incorrect data across multiple datasets will only create new issues or compound existing ones.
- Lack of Reliability: If the data produced by your business cannot be relied upon, the negative consequences can extend to internal issues, supply chain problems, customer dissatisfaction, and even tarnishing a company’s public image.
- Not Being Timely: If your employees and customers are unable to access information promptly, ensuring the delivery of accurate data to its intended recipients becomes a fool’s errand.
- Lack of Unique Data: To prevent duplication of data and its attendant issues, each piece of data should accurately reference a single event or point in time. Failing to produce unique, reliable data creates an unstable business foundation that will lead to problems with customer confidence and unneeded friction between corporate divisions.
- Lack of Useful Data: To be considered valid data, it must be both relevant to the tasks at hand and also capable of being applied to corporate decision-making and problem-solving. A lack of valuable data can undermine a business before it even gets off the ground.
Why Data Quality Issues Happen
To prevent the impact of poor data quality from negatively influencing your business endeavors, it is crucial to understand how and why issues with data quality occur to avoid them:
- Data Integration Issues: Data integration refers to the collection of data from various disparate sources used by your business, and any issues can result in untrustworthy analyses.
- Duplicate Data: If there are unnecessary copies of data floating around your system, not only are you filling up data storage with junk, but any inaccuracies or inconsistencies can also cause problems with reporting, analysis, decision-making, and even customer communications.
- Failure to Ensure Authoritative Data: A company’s data source must not only be accurate but can be considered part of the bedrock of preventing data quality issues. If employees only address issues that appear in reporting without resolving the underlying issue of authoritative data, these issues will remain unresolved and may even worsen over time.
- Human Errors in Data Entry: While this is one of the more understandable explanations for a company dealing with bad data consequences, it is no less frustrating to deal with than other data quality issues. Suppose your company relies on manual data entry into the system. In that case, there will inevitably be issues with data being entered into the wrong fields, typos, and failure to enter necessary details.
- Incomplete Data: Any gaps in data are equivalent to gaps in knowledge, and a lack of reliable data points can lead to both internal and external concerns about the validity of any decisions or conclusions made as a result. Issues arising from incomplete data include determining whether any information is actionable, a lack of customer personalization and accommodation of preferences, and an increased risk of making poor business decisions, all thanks to the cost of bad data.
- Lack of Data Entry Standards: If there are no established standards for data entry within a company, the cost of inaccurate data includes inhibiting critical functions, such as good stewardship and compliance with government regulations. By not providing employees with a standard template for formatting customer data, such as addresses and dates, search results and data analysis become increasingly unreliable due to duplicate or poorly formatted data.
- Outdated Data: If a company’s data fails to remain current, critical insights will be hindered, including information on vendors and employees, customer email addresses, product names, and more. The easiest solution to this issue is to assign data ownership to management rather than the IT department. Executives should take a direct role in the day-to-day management of data, enabling them to implement standards that ensure reliable data is produced and reliable results are achieved daily.
The Real Cost of Poor Data Quality
Financial Losses
Back in 2016, during the relative infancy of the modern information sphere, the renowned computer company IBM issued a study that estimated the cost of bad data to be a whopping $3.1 trillion per year at the time. Business data problems inevitably led to data waste, with issues identified in the study including increased maintenance costs, mismanaged inventory, loss of revenue and reputation, decreased productivity, and unplanned system outages, among others. Given that various industries have been slow to adopt new solutions for addressing data quality issues and the associated problems, it is reasonable to speculate that the $3.1 trillion figure is likely an understatement. Similarly, a 2017 study by Forrester into the cost of bad data estimated that only 0.5% of data is used or analyzed, which is shocking, to say the least.
Operational Inefficiencies
If your enterprise is suffering issues related to poor data quality, your whole operation is likely riddled with inefficiencies. Examples of these inefficiencies include missed deadlines, leading to customer dissatisfaction; processes that are unnecessarily slow and burdensome; and wasted person-hours spent correcting and validating data. A 2024 study conducted by HRS Research and Syniti, which included survey data from over 300 businesses on the Global 2000, revealed that less than 40% of said organizations possess neither the metrics nor methodology in place to assess the impact of poor data quality. Executives who do not wish to spend inordinate amounts of time dealing with business data problems should take care not to become part of HRS and Syniti’s statistics.
Poor Decision-Making
Without a complete understanding of any data quality issues your company may be experiencing, any attempt to remedy said issues may end up being ineffective, causing more harm than good, and even negatively impacting executive decision-making. Having poor data quality exposes a company to a barrage of self-inflicted wounds because without accurate data, it will be impossible to calculate the actual risk of any strategic decisions, lack a proper understanding of market analysis, and create error-prone forecasts that turn out to be utterly wrong.
Damaged Customer Relationships
Beyond any strategic or prognostic risks, the consequences of bad data can also extend to a company’s customer relations and negatively impact its public perception and reputation. For example, if your business struggles to accurately account for customer data, including email addresses, effective marketing campaigns become incredibly challenging. Suppose you have ever been contacted twice in short succession by different people from the same organization who were unaware of each other’s outreach. In that case, it is a clear sign that data quality issues exist. Bad data consequences in customer relations can be especially damaging, as sub-par communication and personalization can leave clients feeling annoyed or disregarded. As such, if data quality issues persist, they can have a lasting impact on the level of trust placed in a business, as well as overall customer retention.
Compliance Risks
In addition to the problems previously described, the cost of poor data quality can be severe, including fines and fees, if it leads to issues with regulatory agencies such as the GDPR in Europe. Both British Airways and Marriott received extensive penalties due to poor data security and failure to comply with timely reporting regulations, respectively. Suppose you are unable to rely on the quality of your data to draw actionable conclusions for business purposes. In that case, it is improbable that your data system will fare much better, as failure to comply with laws and regulations can be very costly indeed.
Missed AI & Automation Opportunities
AI and machine learning models struggle when they encounter dirty data. Poor data quality can lead to automation errors and biased predictions from AI systems, resulting in potentially irreparable losses of profit and reputation. Let’s fix your data before it costs you more. Speak with a data expert and get a personalized strategy to help your company go from strength to strength.
Four Real-World Examples of Poor Data Quality in Business
Retail Example
In 2018, the international fast-food retailer Kentucky Fried Chicken (KFC) made the ill-fated decision to switch from their longtime poultry delivery company, Bidvest, in favor of a substantially lower bid by DHL. As a result, the American chain was forced to temporarily shut down hundreds of franchises across the UK due to a lack of chicken, which led to angry customers complaining to their parliamentary representatives and even calls to various police organizations. By failing to verify the reliability of their new supplier before making a significant change to their business operations, KFC suffered weeks of negative media attention and ultimately had to rehire Bidvest to ensure consistent deliveries.
Marketing Example
Renowned for their sought-after platform that assisted game developers in creating content for augmented reality (AR), virtual reality (AR), 2-D, and 3D gaming, Unity technology suffered a severe financial loss of roughly $110 million in Q1 of 2022 thanks to an issue involving poor data quality. The specific issue was related to incorrect data from a significant client fed into the training sets for machine learning (ML), which caused substantial accuracy issues for Unity’s Audience Pinpoint tool, as well as a notable degradation in the tool’s performance. Unity CEO John Riccitello stated that the $110 million loss resulted from the costs incurred to rebuild and retrain ML models, delays in launching new features designed to generate revenue, and the resulting negative impact on revenue. In addition to the stock price dropping by 37% during this interval, Unity was also subjected to negative press attention about stockholders losing confidence in Riccitello as CEO.
Finance Example
For executives seeking to understand the potentially devastating impact that data quality issues can have on the financial world, the 2008 market crisis serves as a poignant example. To quote Kevin Buehler, the co-founder of McKinley’s commercial practices for global risks and head of the company’s risk advance analytics division:
“There were companies engaged in mortgage lending who had hard-coded into their models continuous home price increases of 4% a year for as far as the eye could see. Those models did not perform well when home prices fell.”
This grievous example of how being overly dependent on modeling predictions, especially those hard-coded to produce results that are rosier than reality, can lead to financial devastation across both industry and society.
Healthcare Example
During the height of the 2020 COVID-19 pandemic, Public Health England was a prominent organization responsible for identifying confirmed cases of the virus and facilitating contact tracing to notify individuals. Unfortunately, authorities later discovered that between September 25th and October 2nd, 15,841 positive cases were completely missing from PHE’s daily reports. As a result, it is estimated that over 50,000 individual infected with COVID carried on with their daily routines while remaining ignorant about their exposure.
The cause for this almost unfathomable lapse in data quality management? The simple fact that PHE was using the XLS spreadsheet rather than XLXS, a decision that sharply limited the amount of verified COVID cases to a mere 1,400 reports. Combined with the fact that other data firms were using updated file formats capable of accurately listing and reporting data, PHE’s failure to upgrade its software led to thousands of names being left off official case counts. Given that this error occurred before any effective medical treatment or vaccination had been developed, the cost of human life thanks to an Excel error may never be fully known. Nevertheless, it serves as a potent example of bad data consequences and their real-world impact on people’s lives.
How to Prevent the Cost of Bad Data
Establish Clear Data Governance
To prevent the potential costs of poor data quality from eroding your company’s profit margin, public reputation, and even its continued success, it is vital to establish well-defined policies for data governance. In addition to a strict and shared set of cross-company procedures and policies, it is crucial that executives, including CFOs, assert ownership of data that is essential to the long-term success of their business. It is also advisable to consult with data strategy consulting agencies to develop and implement appropriate data governance principles.
Implement Regular Data Audits
One aspect of maintaining data governance standards often overlooked by businesses is the need to implement regular data audits to ensure data reliability. Companies who disregard the need for regular audits will soon find the costs for doing so to be untenable, so make sure your company performs data audits regularly.
Use Automation Tools to Validate and Clean Data
Rather than shying away from incorporating AI and other automation tools, executives should recognize how the right tools can significantly ease the management of data quality. Prominent examples include custom data pipelines for automating complex data lifecycle processes without compromising quality, deduplication tools that check for multiple instances of identical data to improve overall quality and reduce digital storage, and validation scripts —a set of coded standards for a database and its users to confirm that data is valid and correct.
Align With a Data Strategy Partner
By teaming up with a data strategy partner like Data-Sleek, your organization can receive the benefits of expert data management services, including Analytics, Data Integrating Services, Data Purity, Dimensional Modeling Expertise, Programming, and System Architecture. Get a leg up on the competition by aligning your company with an organization that deals primarily with data, its regulation and analysis, and swiftly resolving any existing impediments to success.
How Data Sleek Can Help
At Data-Sleek, we specialize in untangling complex database challenges in cloud environments. If your team is dealing with proven methods for implementing data governance, real-time data validation, and cross-company date integration services. Poor data doesn’t just slow you down; it eats your profit, breaks trust, and ruins your corporate strategy. Ready to clean it up? Book your free consultation today to schedule a no-cot discovery call with our consultants.
Visit us at: https://data-sleek.com
