- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Business Analytics - Data Quality
What is Data Quality?
Data quality refers to data accuracy, validity, completeness, and consistency. It also includes a degree of acceptance by the organisations for which it is collected, processed, and analysed to find business insights. It refers to accurate and correct data that determine its adaptability in a particular context, such as decision-making, analysis, and reporting.
Data quality is a measure of collected data based on its veracity or accuracy, completeness, consistency, reliability and validity. Organizations can determine whether collected data matches its intended purpose and find flaws and inconsistencies by measuring the quality of data.
Standards for data quality ensure that companies are using data in decision-making to achieve their goals. Organizations take a greater chance of experiencing unfavourable business outcomes if data problems, such as duplicate data, missing values, and outliers, are not appropriately addressed. Data quality is important for more than just everyday company operations. As organizations are incorporating automation and artificial intelligence (AI) into their processes, high-quality data will be essential to the successful implementation of new technologies.
Characteristics of Data Quality
Some of the key Characteristics of Data Quality are as follows −
1. Accuracy
It refers to error-free data that does not include inaccuracy, misspellings, incorrect numbers, or wrong classifications in data.
2. Completeness
It refers to the degree to which all required data is present. It avoids missing values, incomplete fields, or records, which can lead to gaps in analysis and decision-making.
3. Consistency
It refers to the uniformity of data.
4. Accessibility
The simplicity with which authorized people can access, comprehend, and utilize data. Data that is accessible is kept in a standard format to make it simple to access and understand without unnecessary barriers.
5. Reliability
Reliability ensures trustiness of data that can be utilised for analysis and its insightful results can be used to frame strategic business decisions.
6. Validity
Data validity refers to the extent to which data ensures its standards.
7. Integrity
Integrity of data refers to its connectivity or relationship with other data in a data source. Referential integrity across tables in a relational database is one example of how data integrity guarantees the preservation of relationships between entities.
8. Reasonability
Reasonability of data refers to a data pattern that makes sense within its context.
9. Timeliness
It refers relevance of data that is updated from time to time. Timely data is available when needed and reflects the most recent information, making it more relevant for decision-making.
10. Uniqueness
The degree of data devoid of redundant entries. Redundancies are avoided because unique data guarantees that each entry represents a single, distinct entity.
Importance of Data Quality
Data quality is beneficial in different aspects. It saves money by lowering the cost of correcting inaccurate data and averting expensive mistakes and interruptions. Additionally, it increases the accuracy of analytics, which results in better business decisions that increase revenue, simplify processes, and give a competitive edge.
Data Quality plays a vital role in data analytics. Some of its significances are as −
1. Improves Business Decision
Qualitative data leads to more accurate and reliable decisions, reducing the risk of errors in strategic planning and operations. By identifying key performance indicators (KPIs) to gauge the effectiveness of different programs, it enables companies to better develop or expand their offerings.
2. Improves Business Processes
Qualitative data support teams to identify the breakdowns in operational workflows. It is most widely used in the supply chain industry where real-time data determines appropriate inventory and location of it after shipment.
3. Operational Efficiency
Quality data minimizes errors in processes which saves time and increases operational efficiency.
4. Compliance
Organizations may comply with regulations and avoid legal issues.
5. Increased customer satisfaction
Customers are more likely to trust an organization that provides accurate and consistent data since they rely on it for information.
6. Competitive Advantage
Organizations with high-quality data can gain insights that lead to better strategies, product offerings, and customer experiences.
Data Quality Vs. Data Integrity Vs. Data Profiling
Data Quality is an evaluation of data for accuracy, completeness, validity, consistency, uniqueness, and timeliness of data.
Data Integrity focuses on how data is interrelated with other tables within a data source like a database.
Data Profiling refers to the process of examining and cleansing data to retain data quality within an organisation. This can also involve the technology that supports these processes.