Many great business people, thinkers, and scientists have offered brilliant insights into the importance of data.
But none hold a candle to the wisdom and wit of Sherlock Holmes when he said, “It is a capital mistake to theorize before one has data.” Capital indeed, Mr. Holmes.
While Sherlock worked in the fictional world of crime in 19th-century England, his point rings true across all time periods and in all contexts — including real-life, 21st-century business operations.
Suppose you hired Sherlock as your Chief Information Officer (which wouldn’t be a terrible choice, barring the lack of proficiency in Microsoft Excel). In that case, he’d tell you that making your next business decision without collecting and analyzing all the relevant data will cost you a lot of money and time.
But if I can humbly suggest an improvement to Mr. Holmes’ insight, it’d be this: It is a capital mistake to theorize before one has useful data.
You can collect and analyze all the data you want, but it won’t help you make smart business decisions if it's dirty data.
And that brings us to the key RevOps topic: improving your data integrity. We’ll cover what data integrity is and why it’s important, the different types of data integrity, the common risks, and finally, some steps you can take to improve and maintain data integrity within your organization.
What Is Data Integrity?
Data integrity has three components: correctness, completeness, and consistency. It encompasses more than the information’s reliability but also its usefulness. Maintaining data integrity results in a database that contains:
- Accurate values (correctness)
- No missing values (completeness)
- Identical values in multiple locations (consistency)
Data Integrity vs Data Security
Data integrity isn’t the same thing as data security.
The two terms are closely related, but there’s a simple difference. While data integrity refers to data correctness, completeness, and consistency, data security protects information from being corrupted or compromised by internal and external threats.
In other words, you need data security to achieve and maintain data integrity. However, it’s important to note that data security can also protect dirty data. Data security doesn’t examine the accuracy of what it safeguards.
Why is Data Integrity Important?
With no data, you’re walking down a path with no guide.
But with dirty data, you’re being led down a path by someone who’s blindfolded and has never walked the path before. Even if you arrive at your destination, you probably took some lengthy detours and crossed treacherous terrain along the way, unnecessarily exposing you to danger.
That’s what it’s like for your business if you don’t maintain data integrity.
You can’t effectively achieve your goals because you don’t have real insights. You’ll waste time, energy, and money making wrong decisions requiring more resources to recover from. The data you have will be left exposed and vulnerable, creating a slew of other issues regarding data security and privacy.
To make matters worse, you’ll also make yourself vulnerable to your competition. Here’s how New Breed's Director of Demand Generation explains it:
“If you don't have reliable and consistent data, you're almost certainly absent an ace in the hole. You're probably not going to achieve or maintain an edge over your competition using data. Data isn't everything, but it's a really effective way to make better business decisions.”
So, to recap, why is data integrity important? Because it gives you the right information at the right time so you can make the right decision.
Types of Data Integrity
Data integrity can be divided into two types: physical and logical integrity. Both are important and play a role in keeping your data correct, complete, and consistent.
Physical integrity refers to the technical ability to store and retrieve data while maintaining its correctness, completeness, and consistency.
Power outages, natural disasters, storage erosion, extreme temperatures, and corrosion or other environmental factors are all threats to the physical integrity of data.
Logical integrity refers to the processes that maintain data’s accuracy and reliability as it’s used. There are four types of logical integrity:
Entity Integrity: The creation of primary keys that identify unique values in a database and ensure there aren’t any duplicates or null fields.
Referential Integrity: The use of foreign keys that regulate how values in a database can be altered, added, or deleted and can be used to relate values that can be shared between records or null.
Domain Integrity: The processes and rules determine which values the database owner wants to permit in that column — including the format and type of values. For example, domain integrity could look like determining that values in a certain column can only be numbers rounded to the nearest whole number (or, in other words, no decimals).
User-Defined Integrity: This refers to data that users create — whether it’s a new column or a set of rules and constraints the user defines for a particular purpose.
Risks That Threaten Data Integrity
Anything that could result in inaccurate, missing, or inconsistent data is a risk. These risks typically fall into four main categories:
Human Error: When a person makes a mistake working with data, the results might be inaccurate or incomplete. This could include accidentally deleting data, entering in wrong information, misunderstanding the data, or not following the proper procedures that are meant to keep data secure.
Data Transfer: When you transfer data between devices or between two databases, the information could fail to transfer completely, maintain its accuracy, or produce matching values in the original and destination databases.
Spyware, Malware, Viruses: Your data could be affected if a malicious software program or hacker attempts to steal, delete, or change your data.
Hardware Risks: When a server or computer crashes and hinders your ability to retrieve or store data, you could lose key information.
How To Improve and Maintain Data Integrity
Here are seven ways you can improve data integrity within your organization.
1. Review and adjust the collection process
If you don’t start with clean data, you can’t maintain clean data.
Assess how you’re currently collecting information, identify any weaknesses in the process, and make the necessary adjustments. Whatever system you implement, communicate the value and importance of data collection to every employee.
If people understand how data integrity is connected to their role and success, they’ll care about maintaining it.
2. Check for data errors
There’s no way around it — humans make mistakes.
When employees enter data, they should review their own work and have someone else check it. This data validation process will help ensure you start with data integrity, but it’s also important that you double-check your data when using and analyzing it.
Multiple checkpoints that validate the correctness and completeness of data throughout its lifecycle will mitigate human error's impact on your data integrity.
3. Log and track data
Logging and tracking when data is entered, transferred, altered, or deleted will help you identify where and how people are making errors or inadvertently creating vulnerabilities.
4. Limit data access
Only the people that need access should have access. Restricting access to only the necessary users will reduce the opportunities for data integrity to be compromised.
5. Monitor cybersecurity threats
Hackers employ all sorts of methods to try to access your data.
Knowing their tactics, monitoring suspicious activity, and implementing the best data security practices help protect your data.
6. Backup your data
Backing up your data protects you if a server crashes or a hacker gains access to your data. This safeguard could save your organization a significant amount of time and money. Ensure your data isn’t just secured in a different digital location and a different physical location — like on the cloud — to protect against disasters like fire or flood.
7. Error-detection software
It’s unreasonable to rely on manual processes to monitor and validate all of your data all the time.
Error-detection software will alert you to activity — whether malicious or not — that poses a potential risk to data integrity.
Changing Your Organization’s Trajectory
If left alone, data integrity will deteriorate.
Your organization constantly collects and uses data, and your processes and culture are either helping improve and maintain data integrity or actively contributing to its deterioration. If you feel like your organization falls in the latter category, your next step is to focus on where you can change your trajectory.
Even if it just means starting in one area.
The result will be smarter business decisions that give you an edge over your competition, delight your customers, and empower you to achieve your goals.
Gain Data insights with a Free RevOps Assessment
A well-structured CRM system is essential to maintaining data integrity and maximizing revenue. Poor data management can lead to incorrect reporting, missed opportunities, and reduced efficiency.
That's why we offer a free RevOps assessment with our CRM experts. Our team will work with you to identify areas where data quality may be lacking and provide recommendations for improvement.
In addition, we will also assess your overall revenue operations processes and provide insights into areas that can be optimized for maximum efficiency and accuracy. By having a reliable, accurate, and efficient revenue operation process in place, you can have confidence that your data is secure and your revenue potential is being realized.
Sign up today and take the first step towards ensuring data integrity and maximizing your revenue potential!
Tag(s): Revenue Operations data management
Jon is a copywriter and content marketer based in Buda, TX. If his 18-year-old self could see him now, he'd probably say, "Dude, I can't believe you live in a suburb and gave up on being a musician." Current Jon would just chuckle and say, "Yeah, well, I can't believe you think those skinny jeans look cool."
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