Master Data Management
The importance of data quality diagnostics for business success
As the amount of data being generated and collected by organisations continues to proliferate, the importance of data quality has never been more apparent. Poor data quality costs companies an average of $12.9 million each year. Low-quality data can lead to various problems, from inaccurate reporting and poor decision-making to lost revenue and damaged reputation. A key aspect of ensuring the integrity of your business data is using data quality diagnostics.
What is data quality diagnostics?
Data quality diagnostics are techniques and tools used to identify and assess business data quality, including checking for completeness, accuracy, consistency, and timeliness, as well as identifying and addressing any issues such as duplicate or missing data. A complete diagnosis of data quality is vital for businesses.
Several different data quality diagnostic tools are available, including commercial and open-source options to automate many data quality checks that are otherwise done manually, thereby saving time and resources.
Different data quality diagnostic tools and techniques
These are some common data quality diagnostic tools and techniques used to ensure data quality. The selection of the appropriate tools or techniques will depend on the specific requirements of the organisation and the data quality issues that need attention.
- Data standardisation:
- Data validation:
- Data cleansing:
- Data Profiling:
- Data Governance:
- Data Integration:
- Data matching:
- Data governance software:
Data standardisation is the process of formatting data to ensure that it is consistent and in the correct format. It includes ensuring the correctness of the data type (e.g., date, text, number), consistency in formatting (e.g., all dates are in the same format), and usage of the same terminology and codes.
Data validation checks data for completeness, accuracy, and consistency. It involves checking for missing or incorrect data and verifying that data meets certain criteria (e.g., values fall within a certain range).
Data cleansing is identifying and removing errors or inconsistencies from data. It includes identifying and removing duplicate records, correcting incorrect data, and removing any unnecessary or irrelevant data.
Data Profiling is the process of analysing data to understand its characteristics, structure, and quality. It includes identifying patterns, outliers, and inconsistencies in the data and understanding the relationships between different data elements.
Data Governance is a set of regulations and standards organisations implement to ensure the quality and security of the data they use. Data Governance helps organisations to manage, use and protect their data effectively.
Data Integration combines data from various sources into a unified view. This includes data mapping, data transformation, and data loading.
Data matching is identifying and merging duplicate records using various matching algorithms, such as exact matching, fuzzy matching, and phonetic matching.
Various software tools are available for data governance, quality, cataloguing, and lineage management. These tools can automate data governance tasks, such as data quality monitoring, data lineage tracking, and data cataloguing, to help organisations ensure data quality, compliance, and security.
Benefits of data quality diagnostics
According to Gartner, 2022 saw 70% of enterprises carefully evaluating data quality levels using metrics. With hopes of getting a 60% improvement in data quality, dramatically decreasing operational risks and costs. There are, of course, several other benefits of data quality diagnostics, including:
- Improved decision-making:
- Increased efficiency:
- Reduced errors:
- Increased customer satisfaction:
- Cost savings:
- Better data governance:
- Improved compliance:
- Better data integration:
By ensuring the accuracy and completeness of data, organisations can make better decisions based on reliable information.
Automated data quality checks can save time and resources compared to manual processes, increasing efficiency and productivity.
Identifying and addressing data quality issues early on can reduce the probability of errors and inaccuracies in reporting and decision-making.
Ensuring customer data quality can improve customer service and satisfaction.
Addressing data quality issues early on can prevent costly mistakes and lost revenue in the long run.
Data quality diagnostics can help organisations establish and maintain effective data governance practices.
Data quality diagnostics can help organisations to ensure compliance with legal and regulatory requirements related to data handling.
Data quality diagnostics can help organisations ensure proper data integration from different sources, allowing for better data analysis and reporting.
These are some of the benefits of data quality diagnostics, but not all possible benefits. The benefits may vary depending on the organisation’s specific needs, data volume, complexity, and other factors.
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How can Infosys BPM help?
The Infosys BPM quality diagnostic tool is an efficient, cost-effective process that helps businesses understand the quality of existing data. The tool can provide deep insights into the data’s quality, errors, accuracy, duplication, and inconsistencies.