What is data reliability, and why is it important?

Data reliability refers to the accuracy and consistency of data over time and the degree to which stakeholders, leaders, and employees can trust it for making decisions and conducting analytics. Reliable data is valid, complete, unique, and reproducible. It’s also free from any errors and inconsistencies.

Data reliability vs data validity 

Data reliability refers to the consistency and dependability of data, whether it’s capable of producing similar results repeatedly when test conditions remain the same. 

Meanwhile, data validity depends on truthfulness and accuracy. Does the data accurately measure what it’s supposed to? Validity is all about examining facts such as metrics, timestamps, survey responses, and financial data to ensure they represent a concept or object correctly.

Validated data must also be formatted correctly: a price column should only contain numeric values and not text or symbols. Any inconsistencies here can lead to data quality issues. 

Data reliability and data validity are often used interchangeably and are closely related, but they are different things. However, validity is a component of data reliability – data can’t be truly reliable if it’s not valid. 

Data reliability checklist: 4 important questions to consider

Here are 4 questions that you need to ask yourself in order to check whether or not your data is, in fact, reliable.

Q1: Is my data valid and accurate?

Validity forms the largest part of data reliability. For data to be authentic and dependable, it must be correct, relevant, and fit for its intended purpose. Valid data is capable of accurately representing reality. 

Data validation issues can occur following data entry errors. For instance, a warehouse worker updating inventory levels incorrectly can negatively affect analytics thereafter, making it unreliable.

To ensure validity, you can:

• Use data validation rules
• Automate data cleansing
• Conduct regular data audits

Q2: Is my data complete?

Data must be complete, providing business users with all the information they need to extract insights. Incomplete data can lead to incorrect results and biased analyses. 

Examples of incomplete data include missing customer information (email address, phone number, etc.), partial data from IoT devices, and skipped survey questions. 

To ensure completeness, you can:

• Use mandatory fields in databases
• Enforce strong data entry protocols

Q3: Is my data unique? 

Reliable data must be unique. Duplicate records or redundant data can again lead to poor analytics and insights while also making data management less efficient. 

To ensure uniqueness, you can:

• Run deduplication tools
• Assign unique IDs
• Use standardized naming conventions. 

Q4: Is my data consistent?

Data must be uniform and consistent across different systems and timeframes. For example, an organization’s financial data must be the same, whether it’s being accessed via accounting software or reporting dashboards. Consistent data is more trustworthy. 

To ensure consistency, you can:

• Define clear rules for structure and formatting to prevent discrepancies
• Use data integration tools to transform data into consistent formats

What are the benefits of data reliability?

Organizations with reliable data are proactive, agile, and ready to seize new opportunities. Other benefits of data consistency are wide-reaching and include:

• Better decision-making: Data quality issues can be catastrophic for decision-making. By ensuring that data is accurate, complete, and validated, employees can have complete confidence in business analytics. They won’t second guess data sets or wrestle with inaccurate data when generating results and insights. 
• Reduced data downtime: Relational issues, typing errors, and inconsistent data are all common causes of downtime, which costs large corps $9,000 per minute on average. Establishing accuracy and consistency vastly reduces data risks and enhances business continuity; operations always run smoothly. 
• Business growth and expansion: Bad data is like quicksand, slowly swallowing up departments incapable of making the right calls to support the business. Reliable data provides solid foundations and consistent data that teams can use to identify data trends and support strategic initiatives that drive growth and revenue.
• Increased efficiency and productivity: When employees can trust data, they don’t have to waste time double-checking its validity or righting wrongs caused by poor campaigns informed by erroneous data insights. This increases operational efficiency and team productivity.

Data reliability in the real world

The benefits of improving data reliability are felt in real-world settings in various industries and domains every day. Let’s look at two specific cases for addressing reliability issues and establishing high data quality standards.

Healthcare industry 

Bad data can have disastrous consequences in healthcare, where medical professionals rely on accurate patient records to diagnose conditions and outline treatment plans. Issues can arise from human error in databases, but also from unreliable data being ingested from smart devices. Missing heart readings, for example, result in GPs acting on incomplete information.

Data reliability is, therefore, critical to delivering the best care and support to patients. To achieve this, healthcare practices must:

• Leverage data validation tools to validate patient information and medical records in real time to prevent errors
• Establish comprehensive data governance policies for sensitive data to maintain accuracy and consistency 
• Adopt interoperable and secure systems to consolidate data and use access controls to prevent unauthorized access

Finance industry 

Data is everything for financial organizations. They need accurate, consistent, and complete data sets to assess risks and identify new financial assets to buy and sell. Reliable data is also key to preparing financial reports and meeting stringent regulatory requirements. 

Unreliable data such as transaction record errors, duplicate client records, inaccurate market data, and inconsistent currency conversion rates undermine all of these efforts. Bad data can lead to misreported earnings and non-compliance, which carries significant financial and legal consequences. 

What is the impact of AI on data reliability?

Artificial intelligence and data reliability have a symbiotic relationship, with the two supporting and feeding back into each other. 

AI has taken the busy work out of meeting growing data quality standards. Rather than having to sift through masses of data manually, workers can now use AI tools to automate data cleansing, validation, and consistency checks. 

Even more impressive has been the emergence of machine learning algorithms capable of detecting anomalies, flagging duplicate records, and even predicting future data gaps. This has transformed data quality management. 

Reliable and structured data is also the lifeblood of AI models, which require highly accurate information to train algorithms. Unreliable data leads to biased and flawed processes, making data reliability key to successful AI usage in business.

How to ensure data reliability

Identifying data and reliability solutions requires careful planning. Follow these five steps to ensure your entire data processes produce the insights decision makers need to succeed.  

Step 1: Start with data governance 

Establishing a data culture starts with a robust data governance framework. You need to set clear guidelines and protocols for how data is collected, stored, and accessed. 

This is the foundation from which data reliability principles can then be applied. Applying standardized procedures will iron out inconsistencies and improve quality.

A Chief Data Officer (CDO) is responsible for developing data governance policies, while the Chief Information Office (CIO) is tasked with ensuring the tech infrastructure is capable of delivering data reliability and data integrity. 

Step 2: Conduct a data audit 

How reliable is your data? You should use data lineage and data observability tools to first get a clearer picture of how data is generated and used within the business and then conduct an audit to ensure it meets the right standards. 

Data auditing involves data reliability assessments, which will uncover any errors or duplications that are affecting reliability and help you to measure how consistent and accurate data is based on key metrics. Make sure the reports from these audits are easy to understand for key stakeholders and decision-makers.

Step 3: Prioritize data cleansing

Now, it’s time to address the issues that are plaguing reliability. Employ rigorous and systematic cleansing processes to remove problematic data and ensure that your datasets are valid, unique, complete, and consistent. 

Use this as a time to implement new standardized formats for data entries, and don’t be shy about using automation to streamline the process. You can develop rules and protocols to root out mistakes, fill in missing values and for anomaly detection. This saves time and ensures data is accurate. 

Step 4: Overhaul data collection methods

With everything cleaned, go back and evaluate the way your data is collected. Use the data governance framework with access controls to build out a system that ensures your data is always secure and reliable. 

Work through the four principles: validity, completeness, uniqueness, and consistency, and apply best practices to achieve each one. 

Consider whether you need new data cataloging tools or updated systems to capture data from IoT and other sources. Making the right changes at the point of collection will ensure data is reliable from the start and reduce the need for emergency action for data issues later.

Step 5: Train teams and monitor

Data reliability requires a cultural shift in how enterprise data is managed and accessed. To get workers fully on board, you need to educate them about the importance of data reliability and give them the knowledge and tools to identify and address unreliable data. This can form part of broader data democratization efforts. 

You should also:

• Implement monitoring systems and investigate any errors and inconsistencies flagged by automation tools.
• Establish feedback loops with key stakeholders and conduct regular reviews to keep data reliability efforts on track in the long term.
• Prioritize backup and recovery to maintain business continuity in case of data loss and emergencies.

How do you measure data reliability? 

Following best practices is key to data reliability, but to truly understand how your data performs, you will need to track key data quality metrics. Conducting a data reliability assessment — an evaluation based on the core reliability principles is the best way to evaluate your data and identify areas for improvement.

Key metrics you can track and link to key performance indicators (KPIs) to demonstrate reliability include:

• Data accuracy rate: The percentage of data entries that are correct and have no errors
• Error rate: The frequency with which issues like incorrect values or duplicate records are occurring
• Completeness percentage: The percentage of data points that are present and correct within a specific dataset
• Anomaly detection rate: A ratio for the frequency that unexpected or unusual patterns occur in data
• Error resolution time: How long it takes for data errors to be identified and corrected
• Timeliness: The amount of time it takes for data to flow through a system (latency)

Future trends in data reliability

Future trends will aim to address this trust imbalance, ensuring data is always capable of serving key decision-makers. 

1. Data observability 

Cloud-based data observability frameworks are emerging as a vital tool in ensuring data reliability. Data observability focuses on monitoring and managing data quality business-wide. 

The frameworks support a comprehensive data operations (DataOps) workflow by automating data pipelines, moving data from source to endpoint, and ensuring it’s always available and reliable. Real-time monitoring also prevents downtime.

2. Data mesh and data democratization

Centralized data warehouses have streamlined data management. Moving forward, organizations will also experiment with decentralized data mesh architectures, which allow teams to manage their own data domains. 

This self-service approach supports the emerging trend of data democracy, the practice of ensuring every employee can access data and is comfortable using it.

Summing up

Achieving data reliability can be transformative for a business. By ensuring data is valid, complete, unique, and consistent, you can trust and rely on it for every process and every decision. 

Reliable data empowers teams to work better and do better. It also vastly reduces risks of errors, inefficiencies, and mistakes that drag your business down.

RecordPoint can help you to achieve data reliability. All of our services, including data categorization, data minimization, and AI governance, are designed to improve the quality and consistency of your data.

Contact us today to start your journey to complete data reliability and take control of your data.

FAQs

What is data quality vs reliability?

Data quality measures how well-suited and accurate data is for a specific purpose and point in time. Data reliability is concerned with whether that quality can be maintained over time based on four core principles: validity, completeness, uniqueness, and consistency.

What is the difference between data accuracy and data reliability?

Data accuracy measures whether data accurately represents reality, while data reliability refers to how consistent that measurement is over time. Data accuracy and validity is a component of data reliability. 

How do I determine the reliability of data?

You can determine the reliability of data by conducting data reliability assessments. These assessments require you to check the accuracy, completeness, consistency, and uniqueness of data by tracking metrics and conducting regular audits. Data is reliable when it meets the main criteria and can be trusted for decision-making.

What is an example of unreliable data? 

Unreliable data can take many forms, but it often manifests from incomplete, inconsistent, and inaccurate data sets. When decision-makers cannot rely on or trust the data, this often leads to poor mistakes, which can compound across the business.