While surveying 2,190 global senior executives, only 35% claimed that they trust their organization’s data and analytics. As data usage surges across various business functions, data trust and reliability issues also increase – making it impossible for business leaders to establish a data-driven culture.
The term data integrity is synonymous to data reliability and trustworthiness. In this blog, we discuss everything you need to know about data integrity: what it is, why do you need it, and how can you ensure it. Let’s begin.
What is data integrity?
Data integrity is defined as:
The reliability and trustworthiness of your data over its entire lifecycle as it is captured, stored, converted, transferred, or integrated.
Data integrity is sometimes defined as a state (data is reliable) and at other times, it is referred to as a process (measures taken to make data reliable). Data integrity ensures that data is not only kept reliable in the final destination source, but also as it transitions from one state to another or undergoes various processes, such as data integration, data cleansing, data standardization, and so on.
4 signs of data integrity
Since the terms trustworthiness and reliability can be subjective, it is important to identify the factors that make someone feel like they can trust their data. Here is a list of data characteristics (known as signs of data integrity) that are crucial to ensure the integrity of data:
- Reliability: the extent to which data is accurate, complete, timely, valid, consistent, and unique.
- Traceability: the extent to which the originating source of data and the course of changes it went through over time can be verified.
- Accessibility: the extent to which data is available and accessible to the right person at the right time, and in the right format.
- Recoverability: the extent to which data is backed up and can be rebooted with minimal time and effort.
What are types of data integrity?
Data integrity relates to multiple data aspects that must be controlled and governed to establish data trust and reliability across any organization. These aspects can be divided into two categories:
1. Physical integrity
Physical data integrity means keeping data safe from physical calamities, such as natural disasters, power outages, hackers, malware, etc. Organizations need to sustain the physical integrity of their data at rest as well as when it moves between sources. Cloud computing and storage services are widely used today as businesses increasingly trust tech giants to physically keep their data safe, accessible, and recoverable – especially during downtimes.
2. Logical integrity
Logical data integrity means keeping data logically sensible – in terms of its data model and design. You can ensure logical integrity of your data by enforcing constraints on how data entities are stored and related to other entities. It is further divided into four types:
a. Entity integrity
Entity integrity means that every record in the database is unique and relates to a real-world entity (such as customer, product, location, etc.). Ensuring entity integrity can help avoid duplicate records since every record in the database is identified with a unique, not-null attribute (also called primary key). Let’s consider an entity data integrity example: you can use Social Security Numbers to uniquely identify customers because two different customers cannot have the same SSN.
b. Referential integrity
Referential integrity means that an entity record only refers to another entity that exists in the system. You can achieve referential integrity by utilizing primary keys (unique identifiers) as foreign keys across tables. For example, consider two tables in a relational database Customer and Order. Referential integrity ensures that all records in the Order table link to a correct, valid customer present in the Customer table.
c. Domain integrity
Domain integrity means that the data values of every column in the database are valid and are true to their specific domain or context. To ensure domain integrity, you must enforce constraints on columns values in terms of data type, format, pattern, and measurement unit. For example, the Birthdate must be a valid calendar date belonging to the range of 365 calendar values.
d. User-defined integrity
User-defined integrity refers to a set of constraints that are enforced on data due to the specialized needs of an organization. These constraints are implemented if the first three types of constraints do not completely fulfill a business’ requirements. For example, a company’s leads database can have Lead Source values depending on the strategies that they use for attaining leads, such as cold calling, organic traffic, or referral.
Why is data integrity important?
Data is the most valuable asset owned by businesses today. So, you can imagine how poor data integrity can impact a business – in terms of operations or its ability to make data-driven decisions. Organizations that can rely on the quality, availability, and safety of their data enjoy a number of benefits. Let’s take a look at the top three areas that benefit when you ensure data integrity best practices.
1. Protecting personally identifiable information
Businesses collect and store sensitive information about their customers, vendors, partners, and other associates. If the integrity of such data is compromised – whether physically or logically – it can have a long-lasting negative impact on the reputation of the business.
Many institutes (especially those operating in the healthcare or government industry) mask personally identifiable information of their customers/patients. If the obfuscated data is tampered with and loses its integrity, it can become impossible to retrieve the original data. For this reason, it is absolutely necessary to host your data in a system that protects its integrity.
2. Complying to standards
Data compliance standards (such as GDPR, HIPAA, and CCPA, etc.) compel businesses to revisit and revise their data management strategies. Under these data compliance standards, companies are obliged to protect the personal data of their customers and ensure that data owners (the customers themselves) have the right to access, change, or erase their data.
Apart from these rights granted to data owners, the standards also hold companies responsible for following the principles of transparency, purpose limitation, data minimization, accuracy, storage limitation, security, and accountability. It is very difficult to comply with these standards if the underlying data is not well-managed. And a lack of compliance can limit your business operations – especially geographically.
3. Gaining reliable insights
Data integrity goes hand in hand with reliable data insights and business intelligence. If the data fed to BI tools or a team of analysts is corrupted, it is definitely going to produce inaccurate results. Many organizations struggle to consistently generate reliable data insights since their data needs to go through complex ETL processes to become usable. As data integrity testing checks for data trustworthiness even as it transitions through different phases, it can help you to keep your data in the ready-to-use state most of the time.
How to ensure data integrity?
We discussed the characteristics that validate the integrity of your system (reliability, traceability, accessibility, and recoverability). But how can one attain and sustain these characteristics in their data during its entire lifecycle? The answer lies within various data domains that contribute to achieving these factors in their own specialized way. Let’s take a look at some methods to ensure data integrity and the role they play in data integrity management.
1. Data quality management
Data quality management refers to a systematic framework that continuously profiles data sources, verifies the quality of information, and executes a number of processes to eliminate data quality errors – in an effort to make data more accurate, correct, valid, complete, and reliable.
- Profiling datasets and generating a comprehensive report about its state in terms of completeness, pattern recognition, data types and formats, and more.
- Cleaning and standardizing data by eliminating null or garbage values, removing noise, fixing misspellings, replacing abbreviations, and transforming data types and patterns.
- Executing data match algorithms and identifying records belonging to the same entity.
- Removing duplicates, overwriting values, and merging information to attain the golden record.
Since the requirements and characteristics of data quality are different for every organization, data quality management also differs between enterprises. The types of people you need to manage data quality, the metrics you need to measure it, the data quality processes you need to implement – everything depends on multiple factors, such as company size, dataset size, sources involved etc.
2. Data security and privacy measures
Data security and privacy measures play a crucial role while ensuring data integrity. Although both refer to slightly different concept but they have a lot of overlapping areas.
Data security refers to the process of safeguarding data from theft, malicious attacks, and unethical breaches. While data privacy refers to protecting sensitive information and limiting its access to authorized users only. To secure and protect your data, a number of processes must be implemented:
- Detecting and classifying private and public data so that suitable measures can be taken to protect sensitive data (during rest and transit).
- Enabling identity and access management (IAM) technology that creates digital identities and assigns the right permissions to users for data access and manipulation.
- Implementing data masking techniques to hide personally identifiable information through encryption, character shuffling or other methods.
- Complying to regulations like GDPR, HIPAA, or CCPA to give consumers more control of their data.
- Backing up data to multiple locations and enabling immediate data recovery and operation during system failures.
3. Data governance practices
The term data governance refers to a collection of roles, policies, workflows, standards, and metrics, that ensure efficient data usage and security, and enables a company to reach its data objectives. Data governance relates to the following areas:
- Designing data pipelines to govern and control data flow throughout organization.
- Creating moderation workflows to verify information updates.
- Limiting data usage and sharing between authorized users only.
- Collaborating and coordinating on data updates with co-workers or external stakeholders.
- Enabling data provenance by capturing metadata, its origin, as well as update history.
What are the threats to data integrity?
In times when data has become so significant for business success, a number of factors can pose as serious threats to its integrity. If a business cannot trust the accuracy and authenticity of its data, it is bound to be left behind in the market where every competitor has established and utilizes a data-driven system. The top five threats to data integrity are:
1. Data quality issues
Data quality is the cornerstone of data integrity. People often get confused between data integrity and data quality and use both terms interchangeably. In reality, ensuring data quality enables a part of data integrity – the part where you expect data to be reliable and accurate. For this reason, data quality issues cause data integrity issues.
The most important thing that enhances reliance on data is its completeness, validity, uniqueness, and other data quality dimensions that indicate quality of data. However, the presence of intolerable defects in your dataset can deem your data unusable for any intended purpose. If your teams cannot trust the data they have, it affects their work productivity and efficiency. To prevent data quality issues, you need to treat incoming data to data pipelines where a number of operations are performed, such as data cleansing, standardization, and matching.
2. Integration errors
Businesses implement various data integration techniques to bring data together and enable deeper, more accurate insights. To combine and consolidate dispersed data into a single view, it undergoes a number of conversions and transfers that can potentially compromise data integrity, such as:
- Confidential data loss.
- Data quality deterioration after the integration.
- Inconsistent results produced with same integration method.
- Changes encountered in data meaning pre and post integration.
3. Data firewall compromises
Data firewalls protect your data from cyberattacks and malicious data breaches. Data integrity issues are likely to occur when these firewalls fail or collapse due to misconfigurations, forced attacks, or other technical issues. Organizations sometimes set up multiple firewalls to ensure continuous uninterrupted protection of its confidential data.
4. Hardware or server failure
Many organizations have their own IT infrastructure for capturing, hosting, and servicing business data and information. Due to stringiest data security requirements, they withstand the cost of setting up hardware servers and configuring software as needed. Such setups often encounter system faults and errors and shut down. In absence of proper data retention and recovery measures, you can end up losing important data or experience long downtimes where data systems are not up and available for use.
5. Human error or lack of training
Despite all efforts to maintain data integrity, human error can intervene and damage your data’s reliability. Server misconfigurations, system design failures, and data entry errors are common reasons behind reduced data integrity. If your team does not understand how data systems work in your organization, they will probably mishandle it or use it inefficiently. You need to educate teams about your organizational data, such as what it contains and how to handle it. Ensuring data literacy across your business can help attain and sustain data integrity in the long term.
Enhance data trust and reliability with data quality
If your business is struggling to achieve physical and logical data integrity across different business functions, it is okay to start at one place and potentially grow with time. Ensuring data quality in terms of accuracy, validity, completeness, and uniqueness is one way to begin implementing data integrity techniques.
Having delivered data cleansing and matching solutions to Fortune 500 companies in the last decade, we understand the importance of being able to trust and rely on your data. Our product, DataMatch Enterprise, helps you to clean and standardize your datasets, and eliminate duplicate records that represent the same entity.
You can download the free trial today, or schedule a personalized session with our experts to understand how our product can help you gain trust in your data and get the most out of it at an enterprise level.