The U.S. economy suffers an annual cost of approximately $3 trillion due to poor-quality data, affecting the financial performance of 88% of American companies. This underscores the crucial importance of data reliability in preventing the circulation of inaccurate, inconsistent, or unreliable data through your pipelines.
Key Insights on Data Reliability for Business Success
- Ensuring data completeness, accuracy, consistency, and compliance within your organization constitutes data reliability.
- Human error, data degradation, system update lapses, and other elements can contribute to the generation of unreliable data.
- Improved data reliability can be achieved through the use of dependable data sources, data profiling, and data testing.
- Consistent data updates and employee training in effective data management play vital roles in enhancing data reliability.
- The right data pipeline tools are essential for ensuring the reliability of data as it transitions between different locations.
This guide delves into the critical role of data reliability in achieving business success, providing insights on collecting, processing, sharing, and analyzing higher-quality data.
Why is the reliability of data important?
When data lacks reliability, it hinders effective data management and analysis, posing challenges in extracting value from organizational information. Reliable data is crucial for informed decision-making, ensuring business competitiveness, and maximizing profits. Additionally, it plays a pivotal role in delivering exceptional customer experiences by providing up-to-date information on products and services.
What are the business benefits of reliable data?
High-quality, reliable data prevents resource wastage, enhances compliance with regulations, and improves overall business workflows. Accurate data contributes to compliance with data governance frameworks, avoiding potential fines associated with inaccurate data collection and processing, such as those mandated by GDPR.
Why Is Reliable Data Important in Business?
In the world of organizations reliant on data, having trustworthy data that is valid, precise, and consistent is crucial. This reliability is vital for understanding business processes and making better-informed decisions. When data is not reliable, businesses may face challenges such as:
Poor Decision-Making
Unreliable data, marked by inconsistency or inaccuracy, can distort business decisions, leading to unfavorable outcomes. The inability to draw conclusions, identify patterns in extensive datasets, or extract value from business intelligence tools reliant on dependable data can hinder overall success.
Inability to Comply with Data Governance Frameworks
Regional or industry-specific data governance frameworks may prohibit the collection, processing, and sharing of inaccurate data. Non-compliance with these regulations can result in costly fines from regulatory authorities. For instance, GDPR mandates that data collectors must take reasonable measures to rectify or erase inaccurate data, a task facilitated by no-code data pipeline platforms like Integrate.io.
Breakdown in Business Workflows
Effective day-to-day workflows and data-driven decision-making require accurate data. If, for instance, the marketing team possesses inaccurate insights about potential customers, such as outdated contact details, efforts might be misdirected, leading to decreased revenue.
Inability to Identify New Opportunities
Unreliable data poses challenges in identifying and capitalizing on new opportunities that could propel the business forward. Incomplete or inaccurate data hampers the ability to pinpoint potential cost-cutting measures within the organization.
Where Does Unreliable Data Come From?
Unreliable data can originate from various sources within your organization. Several reasons contribute to data being outdated, inaccurate, inconsistent, incompatible, or incorrect:
Data Degradation
Over time, data can degrade, especially when stored in hardware like data storage devices, flash memory sticks, and solid-state drives. Hardware utilizes electrical charges to store data, and these charges may break down due to a lack of insulation. While moving data to the cloud can enhance reliability, it’s effective only if data hasn’t already experienced degradation.
Failure to Update Data Systems
Issues with data reliability can arise when team members neglect to update systems with accurate data. For instance, incomplete customer data profiles can hinder the ability of sales and marketing teams to understand leads due to a lack of information.
Human Error
Data unreliability can result from mistakes made by team members during system updates. Data sets may contain spelling errors, inaccurate information, missing fields, and other oversights that compromise data quality.
Using Data from Legacy Systems
A common mistake involves transferring data from legacy systems through data pipelines, even when that data no longer holds value. When merged with current data sets, legacy system data can adversely impact analysis, generate inaccurate insights, and introduce data quality issues.
How to Ensure Data Reliability
Ensuring absolute data reliability is impractical, given that no dataset can be guaranteed to contain 100% accurate information. Human errors, data degradation, and biases can impact the quality of organizational information. However, you can enhance data reliability to the greatest extent possible through the following approaches:
Data Profiling
Conducting data profiling or validation involves a thorough review of existing data to verify accuracy, consistency, and correct structure. Utilizing appropriate tools can identify errors that may affect data reliability, triggering alerts for users when poor-quality data is detected in systems.
Data Testing
While data profiling focuses on existing data, data testing ensures that new datasets conform to internal and industry standards. Digital tools like Integrate.io can automate this process, contributing to improved data reliability.
Ensuring Reliable Data Sources
The reliability of third-party data collected from external sources can vary. Data sets obtained from reputable sources, such as government statistics or established marketing companies, are more trustworthy than unsourced news articles or unverified scientific papers. Incorporating data from reliable sources into systems enhances overall data reliability.
Training Employees on How To Use Data
Human error stands as a significant cause of unreliable data. Training team members on proper data usage can enhance data reliability and contribute to high-quality data. This training involves instructing team members on collecting and verifying information accurately before entering it into a database. Adhering to data governance principles, such as GDPR and CCPA, is also emphasized.
Updating Data
Ensuring data reliability involves promptly updating datasets. For instance, when customer service teams receive new customer information, immediate entry into a customer relationship management (CRM) system is preferable to writing it down on paper. Regular updates maintain the accuracy of information within the organization.
