Master Data Management

Elevating data quality management to achieve data integrity

According to a survey of 200 data professionals, data quality directly impacts 25% of the business revenue. High-quality data helps in better decision-making, compliance with the laws, and enhanced customer experience. On the other hand, poor data quality can produce false reports and bad decisions.

As the data sets grow, data teams trade-off between downtime and the time spent on its quality. When the success or failure of a business depends on understanding its customers’ needs and optimising its processes, businesses must improve data integrity to track performance metrics and stay connected across locations.

This article explains the importance of data quality, the common issues, benefits, and best practices for improving it.

Why is data quality important?

Data quality impacts every department within an organisation. Let us understand this with an example. The accounts department of a medium-sized business creates and tracks between 100 and 150 sales invoices and corresponding payments monthly. Using this data, the department deposits advance monthly income tax.

However, during the annual audit, the auditor found that 8% of the invoices were cancelled, 3% were missing, and 2% were in the wrong customer’s name. This discrepancy not only produced wrong profit and loss statements and balance sheets but also gave a false view of quarterly sales targets to the management.

Poor data quality can impact the outcomes of other departments, too, such as marketing, sales, customer service, and product development. This is why a business of every size needs to improve data quality.

Common data quality issues

High data volume can introduce multiple errors, inaccuracies, and inconsistencies such as duplication, corruption, and incompleteness. This can potentially skew the conclusions the management draws and the annual reports. Common data quality issues are:

Inconsistency

Factors contributing to inconsistent data can be that they do not follow the standards or have conflicting formats and incorrect entries. For example, the customer care team updates the records of a customer in the CRM without putting the notes justifying the changes.

Duplicity

Multiple entries for each customer within the system can impact how the marketing team segments, designs campaigns, and markets the products. The customer can receive multiple similar emails or those not meant for them. Either way, the business stands the risk of customers unsubscribing or choosing a competitor.
You need de-duplication tools that automatically parse the systems and create unique, high-quality data entries by eliminating duplicates.

Corruption

Data could corrupt within the system while being written, stored, processed, transmitted, synchronised, or extracted. It may result in the data becoming inaccessible or unreadable.

Incompleteness

Incomplete data can severely impact a business’s ability to operate at its full potential. For example, customer records without email IDs limit the marketing team’s ability to reach out. Similarly, the sales team may not be effective without a customer’s history of interaction with the company. Businesses need a culture of proactive data management, timely alerts, and observability tools to ensure completeness.

Inaccuracy

Businesses must not depend on manual data entries, be it the accounts, marketing, CRM, or sales systems. For example, the accounts team should not manually match the refunds with their parent entries. Rather, the system should automatically match the refund bank entries with the original invoice or bill. Doing so manually can introduce inaccuracies.

Real-time data monitoring and standardisation of entries help reduce inaccuracies. The same logic applies to all other departments where inaccuracy can affect decision-making and strategy.

Best practices for achieving high data quality

Data quality management helps maintain integrity, consistency, and reliability. This ensures better decision-making and compliance. Some of the best practices are:

Data governance policies and quality criteria

Establish data quality standards by setting clear expectations on collecting and managing the data. The teams must have a clear protocol for collecting, entering, and vetting the data within the systems. This makes it easy to identify the gaps and correct the errors.

Validating data quality at the source

Use data validation tools to check its quality against the established standards at the source. Validate and identify the errors before they are transmitted to other systems and become costly problems. AI tools with automated validation and reporting capabilities make improving data quality easier.

Data cleansing and profiling

Regularly examine and cleanse the data to remove errors, duplicates, and inconsistencies. Use automated cleansing technologies to make the job easier and faster. Profile the data to identify and address the quality issues at the earliest.

Eliminate data silos

Data silos across departments or physical locations do not give a complete perspective of the organisation. These data silos tend to have different data entry, validation, and maintenance protocols that introduce inconsistencies. You must ensure that the data is consolidated and follows the same DQM processes and criteria.

How can Infosys BPM help?

Data quality management at Infosys BPM is a part of the comprehensive master data management (MDM) and ensures that the data is accurate and actionable, improving business intelligence efforts.

Read more about data quality management at Infosys BPM.