Imagine, you have invested thousands of dollars in healthcare data that helps you to reach out to your prospects. You are very excited to run an email campaign. Even though you have spent days preparing your cold outreach pitch. But when you send the emails, instead of a high open rate what you see is a high bounce rate. There are you felt the impact of poor data quality in healthcare?
Isn’t it frustrating?
That’s what poor data quality in healthcare does to you. Your money, time, and efforts go in vain with poor-quality data.
You purchase the healthcare data to reduce the time you spend finding the contact details and other important details of your prospects in order to pitch them better.
But when you have poor quality healthcare data on your hand you end up spending more money, time, and effort than manual lead generation.
If you want to know how poor-quality healthcare data affects your business and how you can avoid poor-quality healthcare data and how to improve data quality in healthcare then this blog is for you.
We are going to mention the effects of poor-quality data on your business and how you can improve it.
But before that let’s understand what is data quality in healthcare means?
What is Data Quality in Healthcare?
There are several factors that make your data poor quality, let’s see which are they
- Lack of contact details( Phone numbers, emails, physical address)
- Outdated information
- Data entered in the wrong field
- Duplicate entries
- Wrong spellings/typos/spelling variations
- Non-normalized data
If anyone of these is present in your healthcare data, your data is poor.
Now let’s see how bad quality data impacts your business
Impact of Poor Data Quality in Healthcare
Impact of poor data quality in healthcare can cause you a loss of millions of dollars. Yes, as per Gartner’s research, bad quality data causes businesses $13million per year. And not only money, but you may also lose your reputation, efforts, and time.
You May Get Blacklisted
For healthcare professionals, email is one of the best marketing channels because of two reasons. One, it is cost-effective, and second, you can segment your audience and have more personalized communication with them.
This helps you acquire your clients at a very low investment. But you have to be very careful about certain things in email marketing or your emails will reach into spam boxes instead of inbox.
One such thing is the recipient’s email address. If by any chance the email address is inaccurate, or not updated, then your emails will definitely go into the spam box.
The repetitive occurrence of this for multiple recipients led to spoiling your reputation. And your domain gets trapped in a spam trap which eventually leads to a black listing of your domain.
Email Churn Increase
Email churn refers to the reduction of email subscribers. The churn rate is the percentage of your subscribers who leave your service over a certain period of time.
Now, it’s not only the subscribers who left your service but it’s un-updated data that provides you the email address of the person that is no longer in use.
If that email address is no longer in use, then obviously the open rate will reduce, the spam rate will increase and the number of subscribers will reduce.
Reduced campaign effectiveness
Marketing campaigns often require personalized messaging tailored specifically for different segments of the target market.
If the underlying data is flawed, you may struggle with delivering relevant content, resulting in decreased campaign success rates. When messages don’t resonate with recipients due to inaccuracies or inconsistencies in their profiles, it leads to missed opportunities for generating leads or driving conversions.
Damaged brand reputation
Incorrect or outdated data can cause embarrassing situations where companies send promotional materials, offers, or communications addressed incorrectly at best—or worse—to non-existent individuals who never signed up for such interactions.
Such mistakes not only waste time but also damage brand reputation by showing incompetence and lack of attention to detail.
Wasted resources
Maintaining a database requires time, effort, and financial investment from businesses; however, if the data within this database is of poor quality—filled with duplicates, incomplete entries or inaccurate details—it becomes an inefficient use of these valuable resources spent collecting it initially.
Missed cross-selling/up-selling opportunities
Accurate data enables companies to understand their customers’ preferences better and identify potential cross-selling or up-selling opportunities based on buying patterns and behavior analysis—a crucial aspect of increasing revenue streams through existing clients.
By contrast, when faced with unreliable data sets, the chances increase significantly missing out on these lucrative possibilities as the insights derived from inaccurate information may lead to faulty conclusions.
Decreased customer satisfaction
Poor data quality can also impact post-sales activities, such as customer support and service. Incomplete or incorrect data about customers’ purchase history, preferences, or communication records can impede timely issue resolution and frustrate customers who expect a seamless experience. This dissatisfaction could result in negative word-of-mouth referrals or even churn.
“Precaution is better than cure” right?
The solution to poor healthcare data lies in the careful selection of the right data provider. At the time of choosing the data provider, you must consider certain factors to avoid these kinds of situations. Let’s see what these factors are.
How to Improve Data Quality in Healthcare
When it comes to avoiding poor healthcare data, there are several crucial factors to consider. Here’s a breakdown of the key elements that can help ensure the accuracy and reliability of your data:
1. Data Source
The source of your data plays a vital role in its quality. Consider reputable sources such as Ampliz healthcare data. Ampliz gives you 85% accurate, real-time updated, and compliant data. Ampliz collects data from 100+ credible sources and gets them validated by 800+ experts.
Ampliz refreshes the data at regular intervals with the help of AI and human efforts.
Ampliz tracks the past buying history of your prospects and thus gives you only targeted data.
Moreover, Ampliz lets you choose the recency of information as latest as 30 days, 60 days and 120 days with accuracy >85%, >75%, and <75% respectively.
2. Data Collection Methods
Pay attention to how the data was collected. Ensure that appropriate methodologies were employed, such as random sampling techniques or well-designed surveys with adequate sample sizes.
If possible, cross-validate your findings using different collection methods for added confidence.
3. Bias Assessment
Identify potential biases within your dataset that could impact its integrity and objectivity.
Investigate any underlying motivations or conflicts of interest associated with the source or collector of the data that may introduce bias into their reporting.
4. Data Cleaning and Validation
Scrutinize your dataset for inconsistencies, errors, duplicates, outliers, and missing values during the cleaning process.
Implement robust validation procedures to double-check for inaccuracies by comparing against known standards or conducting independent verification tests.
5. Ensure Sufficient Sample Size
Small sample sizes increase the likelihood of biased results due to insufficient representation of diverse perspectives within a population group being studied (e.g., demographics).
Therefore, aim for an adequate sample size based on statistical power calculations suitable for your research objectives.
6. Data Privacy & Security
Protecting sensitive information is paramount in today’s digital age where security breaches are increasingly common occurrences.
Hence, gather only necessary personal details while ensuring compliance with applicable privacy regulations like GDPR (General Data Protection Regulation).
7. Documentation & Transparency
Clearly document all steps involved in collecting and analyzing data from start to finish so others can understand and replicate your work if needed.
This will also help you identify any discrepancies later on when reviewing your findings. Maintain transparency throughout the process by sharing methodologies and data sources used.
8. Quality Assurance
Establish a comprehensive quality assurance process that includes regular audits, peer reviews, or external validations.
This will help identify potential flaws in your data collection or analysis methods while ensuring accuracy and reliability.
9. Constant Monitoring
Continuously monitor your data to detect any unexpected patterns, trends, or anomalies that may indicate poor quality. Regularly update and refresh your dataset as new information becomes available to maintain its relevance.
Conclusion
Data quality issues in healthcare are very harmful to businesses. That’s why it’s important to check all the parameters while deciding any data provider.
By considering these factors diligently, you can significantly minimize the chances of incorporating poor-quality data into your analyses.
Remember that maintaining rigorous standards for data integrity is crucial for making informed decisions and drawing accurate conclusions from your research endeavors.
Read Also: Healthcare Data Providers
