What is Data Consistency? Definition, Best Practices & Examples

Poor data quality costs organizations an average of $12.9 million every year. Moreover, inaccurate data causes operational paralysis and poor decision-making. Since data accuracy depends primarily on data consistency, it directly impacts organizational efficiency. Managing data consistency over time requires a multi-layered approach to adhering to data quality standards and continuous analysis.  

Let’s explore data consistency, its importance, common challenges, and safeguarding strategies through real-world examples.

Defining Data Consistency

Data consistency means the same data point stored in multiple locations has the same value everywhere.

For example, if you change an employee’s address in your internal database, you must also update it in the employee insurance database. If you only update the address in your internal database, your insurance partner might send receipts to the wrong address. Inconsistent data can lead to erroneous decisions and reduced efficiency, which can become costly in most cases.

Data Consistency vs Data Integrity

While data consistency and data integrity might sound similar, they’ve different functions. Data consistency is about maintaining a data point across the databases. On the other hand, data integrity is about protecting data from unauthorized updates throughout the lifecycle. 

For example, a customer deposits $500 into their account. The transaction is recorded in the transactions table, but the account’s table balance for the same transaction has not been updated due to a data entry error. This could lead to inconsistent financial records and undermine customer trust in the bank’s reliability.

Types of Data Consistency

Three main types of data consistency maintain different aspects of data coherence. The types of data consistency are:

Transactional Consistency

Transactional consistency maintains ACID (atomicity, consistency, isolation, and durability) properties in a database.

These properties not only ensure reliability but also help organizations meet data quality standards. Here’s what each component of ACID properties mean:

• Atomicity: Each transaction is a single unit and is either a success or failure. Incomplete transactions are disregarded to maintain concurrency.
• Consistency: Each transaction takes a database from one valid state to another valid state. Transactions that violate database rules are invalidated.
• Isolation: Each transaction is executed independently to avoid conflicts.
• Durability: Data changes are persistent in a database once a transaction is successful.

Eventual Consistency

Eventual consistency is a model that guarantees that a transaction will be reflected across all nodes after a certain period is passed. This model allows temporary inconsistency until the nodes are updated and ensure high throughput. Therefore, eventual consistency is suitable when up-to-the-minute accuracy isn’t the highest priority, but scalability is.

Strong (or Strict) Consistency

Strong consistency guarantees that all changes are immediately reflected across all nodes in a database. This allows for achieving immediate data consistency, but due to frequent communication among nodes, it might result in higher latency. Therefore, strong consistency is suitable when continuous synchronization is a priority, such as in healthcare or banking applications.

Types of data consistency influence decisions regarding database solutions and architectural designs to ensure improved data management. Therefore, you can make the right decisions by aligning the consistency model impact with your business requirements.

Explaining Data Consistency and Its Importance

Data consistency directly impacts operational effectiveness. This is because when all instances of the same data point are aligned, the decisions are based on a piece of unified information. For example, if the HR department recommends a performance bonus for an employee, the payroll department’s database must be updated accordingly to reflect this recommendation. If the payroll department’s database isn’t updated, the employee may not receive the bonus, leading to errors and dissatisfaction. 

Consistent data reduces the time required to address errors and streamlines workflows. This ensures that organizations meet industrial and regulatory data quality standards, reducing the risk of penalties and reputation damage. Additionally, consistent data ensures that customers receive accurate information, such as account balances, order statuses, or product availability, increasing customer satisfaction.

While these are a few scenarios where you can achieve efficiency, data consistency is vital across all domains and functions. It improves business decision-making and productivity, helping you achieve success. 

Causes of Data Inconsistency

Knowing the causes of data inconsistency can help you avoid it by using safeguarding strategies from the beginning.

1. Manual Data Entry

Human error is responsible for around 90% of data breaches, as data entry employees are likely to make errors. Whether the error is in the form of typos, misinterpretations, or simple oversights, it can result in inconsistency by causing variability in data points. Distractions, communication errors, and limited reviews are common causes of data entry errors.

2. Data duplication

Duplicate data points across different systems can confuse and misrepresent information by distorting results. Duplicate records may also have conflicting values for the same data points. Manual data entry errors, software bugs, lack of data validation rules, and integration overlaps can cause database data duplication. Eliminating duplicate records is time-consuming and might cause monetary loss.

3. Lack of data standards

Data standards define specific data formats, validation rules, and guidelines to ensure data consistency across all database systems. Not following data standards leads to irregularities in format and nomenclature across the system. Lack of leadership support, complex legacy systems, and resistance to change are common reasons behind the lack of data standards in organizations.

4. Integration challenges

Integrating data from external platforms can cause data inconsistency in several ways, including inconsistent data formats, compatibility issues, and transformation errors. For example, combining data from software applications and databases using different data formats can lead to inconsistency. 

5. Data migration

Data mapping errors, format differences, and data loss during data migration between different platforms can lead to data inconsistency. Additionally, incomplete data transfers or losses can occur if not all relevant data is successfully migrated, leading to gaps in the target system. Common reasons for data migration issues include poor planning, data mapping errors, lack of testing, and system incompatibility.

6. Lack of data governance

Unclear data management guidelines and poor data quality controls result in data inconsistencies. This is because, without pre-set guidelines, stakeholders might enter data without following a procedure or meeting quality standards. Poor data handling practices increase consistency issues over time. As a business grows, existing data governance frameworks might get outdated, leading to gaps in data management. Additionally, poor communication between stakeholders about data governance policies and practices can lead to misunderstandings and inconsistent data handling. 

7. Lack of training and awareness

Training ensures that employees understand data usage and adopt data quality standards. Without proper training, employees can use inconsistent data management practices, leading to poor data quality over time. Limited resources, data governance negligence, limited leadership support, and high employee turnover are common causes of a lack of training and awareness among employees.

These factors collectively contribute to inconsistent information that can hinder critical business decisions. This inconsistency impacts both financial outcomes and customer relevance.

Repercussions of Inconsistent Data on Businesses & Users

Inconsistent data can cause significant problems for both businesses and their customers. These problems result in bad decisions, wasted time, unhappy customers, and even legal trouble. Below are some of the real-world consequences of data inconsistency for businesses and customers:

Decision-making Hindered by Misguided Assumptions

All major business decisions depend on the quality of analysis. Therefore, inconsistent data can lead to inaccurate analysis and poor decision-making. For example, duplicate sales records with slightly different information can hide the true sales figures, leading to potential loss of sales opportunities.

Productivity Roadblocks Due to Data Discrepancies

Data scientists spend 80% of their time on finding and cleaning data. Consequently, employees spend valuable time identifying and correcting inconsistent records. This time could otherwise be used for making strategic decisions that drive business success. Thus, inconsistent data not only reduces employee productivity but also leads to project delays.

These issues become even more problematic when they affect multiple departments or regions of operations.

Legal Repercussions From Non-compliance

Specific industries must abide by data regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) for healthcare, the General Data Protection Regulation (GDPR), and the California Consumer Privacy Act (CCPA) for finance. Data regulations act as guidelines to reduce errors in data, streamline operations, and make reliable decisions. Not abiding by these guidelines can result in legal consequences and financial penalties. 

Data Consistency: Real-life Examples

From online retail inventory to hotel reservations and Business-to-business (B2B) revenue tracking, data consistency is crucial in impacting business decisions. Let’s explore some of the real-life examples of the impact of data consistency on business growth:

1. Online Retail Inventory Database

E-commerce databases manage thousands of products that frequently change due to seasonal styles, size variations, and restocks. For example, a customer might place an order for a coat that appears available on the website, only to receive later an email informing them that the item is out of stock. This discrepancy between the webpage and the database can lead to customer frustration.

Maintaining data consistency is crucial for ensuring customer satisfaction and loyalty. It ensures that inventory changes are reflected in real-time, which helps improve business reputation and encourages repeat purchases.

2. Multi-location Hotel Reservations

Conflicting room availability in hotel reservation applications can lead to negative guest experiences, loss of goodwill, and revenue decline. For instance, if Airbnb customers book an already occupied space through the website and encounter this issue, their frustration can spread across social media. This negative exposure can discourage potential customers from booking with Airbnb and lead them to consider other options.

Therefore, strict data consistency checks are crucial to display up-to-date room availabilities. This protects businesses from monetary and reputation loss. 

3. SaaS B2B Revenue Channels Tracking

SaaS (Software as a Service) companies usually offer their services through multiple channels, and each channel uses different systems to track conversion. For example, they might use Google Analytics to monitor website traffic, a mobile analytics tool for tracking in-app purchases, and partner channels to manage partner sales. While these tools offer valuable insights into customer behavior, isolated data views can lead to misunderstandings and a lack of cohesion in the overall analysis.

Inconsistent data across systems might result in inaccurate revenue reports. To keep the systems aligned, you need to define uniform parameters and ensure their consistency across all channels. Centralized data analytics platforms combine data from multiple sources to make the analysis easy and impactful.

Regardless of the industry type, neglecting data consistency can lead to inefficiencies, impacting business growth and revenue. To avoid such consequences, businesses must commit to establishing and maintaining data quality standards. 

Advanced Techniques for Monitoring and Improving Data Consistency

Fortunately, there are techniques to eliminate data consistency issues and improve productivity across organizations. Below are some of the advanced techniques to enhance and maintain data consistency in your business.

Data Validation and Constraints

Data validation is a technique that checks data quality during entry or transmission based on guidelines. Data validation tools use rules, constraints, and routines to verify input data against acceptable criteria. The three main types of validations are:

1. Field-level Validation: Verifies the validity of a single field.
2. Form-level Validation: Verified the validity of data across multiple fields.
3. Business Rule Layer Validation Checks: Checks that business logic conforms to guidelines.

These validation rules avoid typographical errors, improve accuracy levels, and capture inconsistencies on time.

Regular Data Consistency Checks

Scheduled data consistency audits help identify inconsistent data records, and regular consistency audits highlight risks before they cause bigger problems.

Using data visualization to represent information helps identify hidden irregularities faster and reduces the chances of inaccurate decision-making.

Data Governance and Documentation

As discussed above, data governance ensures data quality through detailed guidelines. A well-structured data governance strategy clearly defines roles and responsibilities for everyone involved in data management. This ensures accountability at each stage, leading to accurate and reliable information.

Maintaining good documentation helps track change logs and manage data changes. This simplifies troubleshooting and provides valuable insights for developing improvement strategies based on historical data. Thus, data governance and documentation, in combination, enhance the system’s functionality, efficiency, and consistency.

Build a Data Culture by Increasing Data Literacy

42% of data leaders in the US and UK believe data illiteracy is the reason behind inaccurate decision-making. Therefore, data literacy is crucial for companies to make informed decision-making, achieve competitive positioning, and drive business growth. 

Training programs that educate participants on the impact of inconsistencies and best practices for managing these issues are crucial. They help develop skilled data handlers who can act decisively, resulting in a clean, secure, and robust database that supports more accurate future analyses.

These techniques for handling data consistency issues ensure that you have access to reliable information for decision-making and business security.

Ensure Your Business Data Consistency At All Times With ClicData

While the techniques we discussed in the previous section eliminate data inconsistency, handling growing databases of complex data can be challenging. Fortunately, ClicData simplifies maintaining data consistency and supports critical decision-making.

Why Choose ClicData?

Businesses often have data scattered across various systems, leading to discrepancies and inaccuracies. This can result in skewed metrics and unreliable reports, detrimental to any thriving business.

ClicData addresses these issues with:

• Data Integration: ClicData efficiently integrates data from various systems into a centralized data warehouse, reducing redundancy and streamlining data management.
• Clean and Consolidated Data: Once data is connected, ClicData’s cleaning and transformation tools help remove outliers and correct formatting issues, ensuring consistency and improving overall data quality.
• Intuitive Visualization: ClicData’s data visualization tools make it easy for users to interpret and make sense of complex data, transforming raw numbers into actionable insights.

See for yourself today and explore ClicData’s advanced capabilities with a free 14-day trial. Connect data from multiple sources, identify inconsistencies, and clean your data to turn it into valuable insights.