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 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:

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:

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:

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:

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:

Data Integration combines data from various sources into a unified view. This includes data mapping, data transformation, and data loading.

Data matching:

Data matching is identifying and merging duplicate records using various matching algorithms, such as exact matching, fuzzy matching, and phonetic matching.

Data governance software:

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:

By ensuring the accuracy and completeness of data, organisations can make better decisions based on reliable information.

Increased efficiency:

Automated data quality checks can save time and resources compared to manual processes, increasing efficiency and productivity.

Reduced errors:

Identifying and addressing data quality issues early on can reduce the probability of errors and inaccuracies in reporting and decision-making.

Increased customer satisfaction:

Ensuring customer data quality can improve customer service and satisfaction.

Cost savings:

Addressing data quality issues early on can prevent costly mistakes and lost revenue in the long run.

Better data governance:

Data quality diagnostics can help organisations establish and maintain effective data governance practices.

Improved compliance:

Data quality diagnostics can help organisations to ensure compliance with legal and regulatory requirements related to data handling.

Better data integration:

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.

For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

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.