Top 5 Consequences of Bad Data
Duplicate, incomplete, or inaccurate data can be costly for any organization. Operations, customer service, billing, marketing, sales, and information technology are just a few of the internal departments affected by bad data.
Before we highlight the most devastating effects of poor data quality, let’s take a moment to discuss how bad data happens in the first place. Data errors will always be something that plague organizations, but understanding how data goes bad can prevent situations from getting out of hand.
How Data Goes Bad
Data collection is a prime source of bad data. If controls are not in place or the controls differ according to location or processing system, collected data will be inconsistent. Companies should establish corporate-wide standards for data collection and validate data as it enters the organization, regardless of the source. One step that can make a difference is a tool such as Firstlogic’s Address IQ® API that prompts users for correct address elements and verifies postal address information from the very beginning.
Obsolescence is another factor that contributes to bad data. In many cases, data is not static. It needs to be refreshed or updated regularly. If an organization neglects to update previously acquired data, the chances are good that it will be out of date when you need to use it. Email addresses and postal addresses are two data items that change frequently. Outdated contact information can hinder customer relationships and increase costs.
Poor data management practices can also cause data quality to degrade over time. Companies should establish corporate-wide standards to ensure employees define, view, and manage data consistently across the enterprise.
The Negative Business Effects of Bad Data
Once data becomes unreliable for any reason, organizations begin to suffer the consequences.
1. Operational Inefficiency
Aggregated information is helpful to many business operations. By assessing the number of customers within defined geographic areas, for example, companies can decide how to allocate resources. Examples include establishing territory boundaries or charting efficient routes for company delivery or service vehicles. Other times, businesses may use addresses of specific customers for operational functions, such as dispatching service calls.
Bad data results in lost time, poor customer service, and extra expense in daily operations.
2. Regulatory Compliance Problems
Every organization deals with regulations. Industries such as insurance, healthcare, financial services, or utilities are heavily regulated. Failing to provide customers with regulated materials or charging customers improperly because of geographically dependent rates based on outdated customer data can cause big problems. Laws like HIPAA, GDPR, and CCPA make companies responsible for how they collect and use consumer information. Compliant practices are difficult to maintain unless the data is consistent and reliable.
The consequences of bad data that contributes to regulatory infractions can include fines, penalties, mandatory audits, or lawsuits.
3. Increased Cost
Bad data can have wide-ranging effects. The costs of extra work and out-of-pocket expenses necessary to overcome the impact of poor data can accumulate quickly. Duplicate records, for example, can increase inbound customer service calls and require staff time to manually resolve and merge data. When employees spend as much time correcting data as they do using it, the value of the data diminishes.
Bad data almost always results in unnecessary expenses such as wasted money on duplicates or poorly targeted communications, re-shipping, spoilage, fraud, and more.
4. Missed Opportunities
Inaccurate information about a customer’s buying history, where they live, their credit score, etc. can cause organizations to market ineffectively, missing opportunities to make the most relevant offers to the best prospects at the ideal time. Defective customer data makes it difficult to connect with customers on a personal level. Product development may be constrained by missing or poor-quality product data, leaving competitors free to seize market share with innovative new products.
Not acting, or taking the wrong action because of misleading data can result in competitive disadvantages from which it is difficult to recover.
5. Poor Decisions
Incomplete sales data can affect an organization’s ability to decide where to locate new stores, when to hire more employees, how to structure the service and support department, and more. Data analytics based on faulty information yields misleading statistics and errant trends. Forecasts based on patterns derived from errant data are likely to be off target.
Bad data can lead an organization astray as they make forecasts or develop strategies based on what they thought the data told them.
Poor quality data creates misleading circumstances throughout the enterprise. Bad data causes inaccurate analysis, disconnections from customers, and poor decision-making. Data validation tools like Firstlogic’s Data Quality IQ Suite or SAP’s Data Services Platform help companies collect, validate, analyze, and maintain data so they can avoid the most severe consequences of bad data. Contact Firstlogic to learn more about how we can help you address your data quality issues.
The Non-Mailing Benefits of Good Address Quality
We often think of postal addresses as merely a tool to transport mail from sender to recipient. Postal addresses remain the fundamental means of contact, even with all the electronic communication channels available. When all else fails, we can always send a letter. But an accurate and current address can be the gateway for helping […]
What is a Postal Code?
If asked, most people can supply a reasonably correct answer about what a postal code is. But postal codes are more complex than they seem. In the first place, mail delivery services across the world use postal codes. Each country has a different way of using codes on mailpieces to deliver mail to the proper […]