Your organization’s data is the source of opportunity for your innovation. However, 25 percent of executives surveyed by KPMG either distrust or have limited trust in their data. Without integrity, the information is essentially useless. So exactly what is data integrity? Let’s take a dive deeper into this topic and discuss why it is important for your organization.
Table of Contents
- What Is Data Integrity?
- Why Is It Important?
- Types of Data Integrity
- Challenges in Maintaining Useful Data
- Data Integrity vs. Data Security
- Data Integrity Best Practices
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What Is Data Integrity?
Data integrity refers to the accuracy, completeness, and consistency of information. It affects the reliability and trustworthiness of your company’s figures to inform your organization’s decision-making and strategy.
Why Is It Important?
Poor data quality hits organizations where it hurts - to the turn of $15 million annually. Your leaders and employees need access to quality figures to set goals and define your business strategy. It is critical in helping companies meet their objectives.
System Stability
Bad input can cause application errors and impact system performance. Accurate input helps minimize these errors and improves system stability.
Systems Integration
Chances are your organization has information siloed across several systems and departments. There are likely inconsistencies in these systems. For example, a “name” field in one system may only include the first name, while that same field may include the first and last name in another system. These types of inconsistencies make it difficult to reconcile fields for system integration.
Recoverability
When systems fail, it is important to restore them to their valid state. Bad information hinders recoverability as developers struggle to restore systems with inaccurate input.
Integrate.io’s suite of pre-built integrations helps you ensure data accuracy throughout the systems integration process.
Types of Data Integrity
Data quality takes many forms. Each of the below strategies must be used for best results.
Entity
Entities refer to rows within a database. For accuracy, no two rows can be the same. This is typically accomplished by defining a primary key that contains a unique identifier for the row.
Referential
Referential checks are concerned with the relationships between tables. When two or more tables are related, they should have a foreign key that matches the primary key of the related table. Without these keys, you could end up with what is known as an orphaned record. An orphaned record is when a foreign value has no matching primary key in the primary table.
Domain
Domain checks refer to the validating entries within a column. Each entry has a specified field type. Domain validation ensures these entries meet set criteria. With this approach, the database evaluates and checks each input for validity.
User-Defined
Sometimes entity, domain, and referential checks don’t fit user requirements. In these scenarios, users define business-specific rules to validate the input.
Integrate.io comes with each of these verification methods out-of-the-box to ensure your data remains accurate as you transfer it between systems.
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Challenges in Maintaining Useful Records
Ensuring information accuracy and validity is not without challenges. It is often these challenges that deter businesses from embracing system integration.
Multiple Analytics Tools
It is not uncommon for companies to have a mix of analytics tools. This often happens when teams don’t communicate about requirements. It could also occur when organizations purchase software that has overlapping functionality. The ultimate result is you could duplicate efforts or produce contradictory results.
Lingering Legacy Systems
Monolithic legacy systems pose a challenge for system integration. Getting information out of these systems can be a complex task. This task requires specific knowledge of the system and expertise in potentially outdated programming languages.
Data Integrity vs. Data Security
Information integrity and data security are related terms. However, each plays a specific role in data management. While integrity refers to the accuracy of the information, security refers to protecting it against unauthorized access or corruption.
Integrity Best Practices
Maintaining the accuracy and validity of information requires implementing several best practices. Each of the below items will give you the best chance of maintaining clean and useful research.
Encryption
Encryption is a way to encode text so that it is incomprehensible to a computer or human without the means to decrypt it. It is a process that renders the text useless to hackers.
Access Controls
Assigning privileges is a way to control access to systems. Limiting access to and putting restrictions on how to use these systems can help prevent human error.
Input Validation
It is necessary to verify that any user input contains what is expected for that field.
Merge
Merging combines input that may exist in multiple systems.
Integrate.io’s low-code tool adheres to each of these best practices to ensure your data remains useful once it reaches the destination system.
How Integrate.io Can Help
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Nearly 60 percent of organizations don’t measure the annual financial cost of poor-quality data. The impact can be detrimental to a company’s bottom line. Implementing integrity measures helps ensure access to accurate information or decision-making.
Integrate.io puts information accuracy, validity, and security at the forefront of its features. Learn more today about how Integrate.io can help maintain the accuracy and validity of information throughout your system integration. Contact us for a demo and risk-free trial.
