Data Integrity: The Structure Behind Successful Data-Driven Enterprises
It’s an endless data sprawl that threatens the efficacy and profitability of your entire enterprise.
Whether or not your company can harness the power of all that information will determine its success today, tomorrow, and months or years from now.
According to Accenture, those enterprises that leverage their data effectively experience an average of 30% annual growth. But that kind of growth isn’t possible without data integrity, a hallmark of every data-driven organization.
Here you’ll learn the concept of data integrity and why it’s so important. You’ll also be able to explain how data integrity is different from data security, data accuracy, and data quality.
You can leave this page with an understanding of the factors that impact your data’s integrity — plus an 11-point checklist for implementing data integrity at your organization.
What is Data Integrity?
Data integrity ensures the value, usability, accuracy, and consistency of an organization’s data from its creation or acquisition through its analysis and observation, and on to its archival or destruction. Data with integrity is accurate, reliable, optimally stored, and standardized despite modification, transfer, or deletion.
In other words, data integrity represents structural soundness — maintaining accuracy and completeness in both value and form — everywhere that data lives and flows.
Why is Data Integrity Important? What are its Benefits?
Data-driven organizations recognize data as an essential asset that can have a significant impact on their bottom line. As a result, it’s fueling data democratization and other data management initiatives (i.e., metadata management, marketing taxonomy, and more) to ensure users have easy access to data to perform their job responsibilities.
But there’s a caveat.
Gaps in an organizations’ data integrity can lead to poor decision-making that can impact the bottom line.
Unless your data is high-quality and trustworthy, there’s little value in it. In fact, it becomes a liability. That’s where data integrity comes in.
The most exciting benefits of data integrity include:
- Ensuring data is intact and accurate through its entire lifecycle
- Providing the searchability and traceability of data to its source
- Leveraging the value within big data or an organization’s data sprawl
- Increasing the speed and confidence level of data-driven decision-making
- Minimizing the need for expensive audit trails to trace errors and recover data
- Supporting meaningful, personalized customer experiences
- Improving system performance by minimizing or eliminating incomplete and duplicate records
- Eliminating excess storage for outdated, inaccurate, or duplicative data
- Protecting sensitive data from being miscategorized or improperly stored
- Avoiding long-lasting and far-reaching consequences of decisions based on inaccurate, untrustworthy data
- Ensuring data security and regulatory compliance
- Maximizing marketing ROI by ensuring the integrity of an organization’s marketing taxonomy
- Safeguarding the personal information of your data subjects or customers
More Than Physical Protection of Data Storage
While physical integrity is key and crucial for any data operation, it isn’t the primary focus of data integrity. But while it’s a given that for data to have integrity it must be protected from things like power outages — caused by human or natural forces — ransomware, or a big, giant anvil falling on your data center, that’s more an IT responsibility than a data integrity initiative.
Simply put: Physical data integrity is probably already at the core of your data collection and storage strategies. That’s evident by the market for enterprise data management already being worth over $70 billion, and expected to grow nearly 15% in the coming years. You’re already preventing disruptions to your enterprise data flow. Physical integrity processes ensure those data assets are stored and retrievable in their complete and accurate form regardless of internal or external forces.
So why aren’t you giving the same attention to the logical integrity of your data?
A 4-D Look at the Logical Integrity of Data
Logical integrity processes ensure the accuracy and consistency of the data itself, ensuring it makes sense in context.
Safeguarding the context of data falls into four dimensions: domain, entity, referential, and the fourth, a general bucket for user-defined characteristics.
Domain integrity ensures that all data in a field contains valid values.
For example, consider a user who tries to enter their last name into the ZIP code field of an online form. Since the ZIP code table in the backend database only accepts integers, the value Jones will not be accepted. Because of domain-level data integrity, the user will get an error until they enter the allowed values of the ZIP code field, a five- or nine-digit integer.
This could also protect formats, such as a dollar value or significant figure; the number of options selected; and other structural, input-focused considerations.
Consider this the aspect of data integrity that “protects its domain” or shields its input environment from inaccurate, misplaced, or invalid metadata.
Entity integrity prevents duplicate records and null values by enforcing a unique primary key to identify and retrieve individual records. It protects against data duplication and incomplete data, among other headaches.
For example, a customer’s name isn’t necessarily unique. So an auto insurer will designate “policy number” as a primary key in their customer table to avoid duplicative results. And they’d certainly avoid using “home telephone” as a primary key as it would produce null values related to the customers who don’t have a landline.
In the same way a Social Security Number (SSN) can connect a citizen to a whole host of networks and services, entity integrity connects and preserves distinct identity.
Referential integrity ensures uniform usage and proper storage of data. It requires a foreign key (a column that links data between tables) to have a corresponding primary key or it must be null.
For example, a common referential integrity constraint requires a customer ID in the Order table must match a valid customer ID in the Customer table.
Ready for another analogy? Think about how a passport is linked to your SSN but gives you verifiable identity to a foreign country with a different societal numbering scheme.
User-defined integrity refers to rules established by the user that fall outside of domain, entity, or referential integrity.
For example, to strengthen data integrity and safeguard data, an authorized user might add a specific business rule related to GDPR compliance.
Other examples of internally defined metadata include:
- Campaign ID
- Targeting type
- Year, quarter, date
- Country, language, region
- And other trackable elements
As you grasp these dimensions of data integrity — the lenses through which it can be measured, managed, and threatened — you can use them to guide your approach to establishing data integrity.
So in keeping pace with the domain, entity, reference, and user-defined requirements of your data integrity strategy, you must now enlist the building blocks of data integrity itself.
The Building Blocks of Data Integrity
Think of these four dimensions as four categories of ways in which you can shore up the integrity of your data. They’ll help achieve data integrity in all four dimensions of logical integrity (domain, entity, referential, user-defined).
You may have data in legacy systems, relational databases, data warehouses, and cloud-based apps, to name a few. And each has its own “language” and method of information storage.
Data integrity requires aligning these systems to provide a holistic view of customers, prospects, and the individual areas of the business.
Accurate, complete, reliable, consistent, timely, unique, and valid (AKA, high quality) data is necessary to ensure accurate insights and sound decision-making.
Investing in data integrity is an essential first step in achieving data quality.
*Data quality ≠ data integrity. More on this later.
Key word here: access. Who created and contributed to the data? Who has access to observe, change, or draw insight from the data?
Instilling data integrity requires a fine balance between controlling access and facilitating collaboration. You need a central, easy-to-manage source of truth that is referenceable across your organization.
Data integrity is about more than high-quality data. It requires context or connections with other data points to provide value.
Data enrichment offers a more contextualized perspective of existing data sets which supports better decision-making and maximizes the value of an organization’s data assets.
And once you build up a fortress of data integrity — stand guard.
Protect Your Data Integrity From…
Many things can create data integrity issues. Here are common data integrity threats that can thwart the execution of processes and jeopardize integrity.
Arguably the top threat to data integrity, human error can account for data entry and formatting errors, duplicative data, accidental deletions, and more. Although proper training can help avoid unintentional errors, it is more difficult to protect data from users with malicious intent.
Hardware and Infrastructure Issues
Unexpected computer and server crashes or other events that compromise hardware and infrastructure can incorrectly render data or eliminate access altogether.
Transfer errors result from transferring a data item to the wrong destination or when data is compromised or corrupted during a transfer between two devices. Either scenario renders the data useless.
Spam, bugs, malware, worms, viruses — there’s no shortage of cyber threats that can undermine data integrity. They can take control of or damage the host system. They can spread and infect other programs, applications, and devices.
Countless security issues can leave an organization vulnerable to hackers and breaches. Examples include security loopholes, misconfigurations, poor data access controls, and more.
Data Integrity ≠ Data Quality, Accuracy, Security
There’s a ton of terminology associated with data governance, the democratization of data, and metadata management in general. However, many terms are closely related, so it’s understandable when they’re used interchangeably to make the same point.
Data accuracy, data security, and data quality are often substituted for data integrity. But each is a subcomponent of data integrity — not telling the whole story, but inarguably incomplete in its absence.
Data Security vs. Data Integrity
Data security is solely focused on protecting data from internal and external threats. It ensures data integrity as unauthorized data access can lead to record changes and data loss.
Data Accuracy vs. Data Integrity
Data accuracy measures ensure data is error-free. It represents the first and most critical component of the data quality framework, the starting point for elevating data’s usefulness or achieving data integrity.
Data Quality vs. Data Integrity
Data quality refers to the overall reliability of data to serve its intended business purpose. For data to be considered high quality, it must meet the following criteria:
- Accurate: error-free and a reliable information source
- Complete: encompasses the information requested
- Consistent: represented similarly throughout the system repository
- Timely: up-to-date for its intended purpose
- Unique: free of duplicate entries
- Valid: adheres to the specified business need and required format
Poor quality data leads to erroneous decisions that are damaging to productivity and the bottom line. According to Gartner, the financial impact of poor data quality costs the average organization $9.7 million per year.
By establishing a data governance framework that addresses policies and standards for data security, accuracy, and quality, organizations can establish a solid foundation for achieving data integrity.
What’s at Risk Without Data Integrity?
Failing to address data integrity or one of its underlying components can have serious consequences. Here are a few unfortunate and common data integrity examples across relevant industries.
A retailer lost multiple customers when a data transfer error caused a product to show as “ready to ship” when it was actually on a weeks’ long backorder. Once they were notified of the error, those customers went to another ecommerce site with the product in stock.
A financial services firm didn’t correctly secure sensitive customer information and received a crushing fine for violating a consumer privacy regulation. They had to shut down a regional branch and embark on a major (read: costly) PR recovery campaign as a result.
A hospital system faced a multi-million dollar lawsuit over a data collection error that misrepresented a patient’s health history. It led to an improper diagnosis, the wrong treatment, and a patient’s death.
Marketing operations for a SaaS startup ran a qualified leads report in preparation for a marketing blitz. Unfortunately, that list was riddled with duplicates. As a result, 20 strong prospects received multiple, identical follow-up emails. That poor first impression led many of them to choose a more tech-savvy competitor.
An insurance company used inaccurate location data to classify properties’ risk from wildfires. After one of the worst years on record, the insurer discovered it had failed to classify hundreds of properties as high risk and, as a result, suffered over $100 million in losses.
11 Steps to Instilling Enterprise Data Integrity
By now, most organizations recognize the value of data integrity but aren’t sure where to begin. That’s understandable when just getting a handle on your organization’s data sprawl can seem like an impossible challenge.
So we’ve broken it down for you. Here’s a checklist of 11 best practices for achieving data integrity.
1. Eliminate duplication
Duplications present a number of potential issues. They increase storage costs and contribute to sluggish performance. They compromise security (i.e., when a user copies sensitive data into a separate document, email, or shared folder). And they also fuel ambiguity that leads to errors. That’s why it’s essential to regularly monitor for and remove duplicate data.
Dedicated staff may fulfill this responsibility in larger organizations, while smaller shops may depend on data deduplication and data cleansing tools. There are plenty of open source and proprietary tools to choose from, such as Dedupe.io, Data Ladder, WinPure, BleachBit, CCleaner, and more.
A proactive route would be to use a data standards platform to solve workflow discrepancies on manual platforms like spreadsheets which tend to cause duplication to begin with.
2. Focus on improving data quality
Given data quality’s crucial role in data integrity and decision-making, it’s essential to create data quality standards that align with your business processes and goals. Furthermore, ensuring those standards are applied from the get-go — beginning with data collection and generation — will mean you’ll have fewer errors to correct down the road.
3. Commit to data entry training
Human error accounts for data breaches, data loss, improper corrections, and more. Most of the time, these errors are unintentional. Still, they damage the integrity of your data. Fortunately, you can prevent many mistakes from happening in the first place. Through data entry training, users can learn how to correctly enter and maintain data, which empowers them to be accountable for data quality.
This elevated data literacy also has the positive effect of improving and increasing collaboration between teams. Thanks to a central source with strong guidelines, data-driven creativity and teamwork is able to flourish.
4. Update data frequently
Data that are irrelevant and outdated undermines the overall integrity of an organization’s data. Whether they’re performed in real-time or within scheduled windows, updates are essential for ensuring data can support timely decision-making.
5. Validate data and the data source
Assuming your organizations’ specifications and key attributes are clearly defined, setting data validation rules will help control what users can enter into their systems and better protect your data’s integrity. It’s equally important to validate data sets supplied by other sources (i.e., another application) to ensure the input is accurate.
6. Back up your data
Data loss can seriously impact an organization. It can disrupt employee productivity, damage your reputation, expose confidential information, damage customer relationships — even shutter a business forever.
Backing up your data regularly mitigates these risks and ensures that you can conduct business with a backup copy you can trust in the event of data loss.
7. Control access
Of course, you can’t achieve data integrity without strong data security controls. Users inside or outside your organization with improper access can do serious harm to your data. Implementing the principle of least privilege (PoLP) or a zero-trust approach that grants users minimum levels of access necessary to perform their job duties is a sensible approach.
8. Test for vulnerabilities
Assuming your data security measures are bulletproof can leave you vulnerable. That’s why it is important to perform periodic data integrity checks with penetration testing. Ethical hackers use “pen tests” to simulate a cyber attack against your network, infrastructure, or applications to expose vulnerabilities that need to be addressed.
9. Keep an audit trail
Audit trails are another essential security measure to support data integrity. They provide the proverbial “breadcrumb trail” to help pinpoint the source of a problem or data breach.
Audit trails should be:
- Generated automatically
- Tamper proof and unalterable by users
- Timestamped and aligned with the user who accessed, created, modified, and deleted the data
10. Encrypt your data and website
Encryption ensures that even if someone gains unauthorized access to your data, they won’t be able to read it — unless, of course, they have the decryption key. But your website also needs an added layer of protection, particularly if you’re asking for any personal information.
An SSL certificate authenticates your website’s identity and provides an encrypted connection for more security.
11. Promote a culture of integrity
Clear processes and policies aren’t enough to achieve data integrity. The organization as a whole must foster a culture that’s based on honesty, transparency, and integrity as a foundation.
That starts with collaboration and ensuring team members, managers, and leaders are united and working towards common business goals — while taking ownership and ensuring the integrity of their own data.
To leverage these best practices further, you may want to consider implementing a data integrity solution, partnering with a data integrity services provider, or hiring a dedicated data integrity manager.
Data Integrity Manager: Human or Software?
Protecting sensitive data and providing users with easy access to reliable, relevant data is a strategic initiative for more and more organizations. Consequently, roles related to data integrity are in high demand.
A data integrity manager or data integrity analyst ensures the security and quality of data stored within an organization’s systems. Simply put, if something is wrong with the data, they’re expected to make it right (and prevent it from happening again).
Here’s a rundown of potential responsibilities of a data integrity manager:
- Maintain a thorough record of how data is gathered and accessed
- Monitor who is accessing company information at any given time
- Enact processes that prevent accidental errors
- Regulate security clearances of all data users
- Convert data from one format to another as necessary to enable user access
- Verify that internal information sources are being used appropriately
- Find deleted information and return it to its proper storage location
- Monitor data and network security for malicious breaches
- Creates backups that protect all versions of data on all devices
- Ensure firewall and security systems are up-to-date
- Perform pen tests or arrange for them with outside security professionals
- Stay informed of potential cyber threats and data security market news
- Standardize organizational data and/or draw insights from available data
- Select and manage technology solutions that improve data integrity and usability
Rather than hiring a data integrity manager, you can look to a data integrity solution provider instead. Of course, you may also choose to have a human and software in place — kudos to you.
Data integrity or data standards software adds to the above list with perks including:
- Maintaining a central source of truth from which everyone can branch to enable data integrity — and its benefits — throughout their specific workflows
- Updating dynamic data dictionary and integrity guidelines that tracks history and is easily shared between teams
- Automating workflows between teams and across channels, tools, and regions
- Connecting every part of your martech (and other tech) stack to a single unifying platform
- Automating validation of datasets in bulk with activity and change detection
- Enabling data integrity at its source, rather than reactively (or worse — passively)
- Reducing workflow disruptions as people change roles
- Saving time and money on cleaning, stitching, and finding of data on the back-end
- Built-in data governance
- Enterprise-level security controls
- Access to data integrity consultations backed by the collective intelligence of hundreds of enterprise data strategies
Guess what — that’s us. The benefits of choosing Claravine as your data integrity gatekeeper is its comprehensive network of seamless technology connections that permeate data standards throughout your organization.
Claravine = data integrity.
It’s more important than ever to be able to trust your data. The future of your organization depends on it. Claravine can make it easy to create data with integrity. Learn how by taking a video tour of The Data Standards Cloud.