Overcoming Data Quality Issues in Implementing an ERP system By Janifha Evangeline, Assistant Editor, CIOTechOutlook

Overcoming Data Quality Issues in Implementing an ERP system

Janifha Evangeline, Assistant Editor, CIOTechOutlook | Saturday, 12 October 2024, 21:55 IST

Implementing an ERP system is a crucial step for enterprises that hope to integrate its processes in a single platform. However, the journey can be fraught with numerous challenges, and notably data issues which can have a huge impact on the effectiveness of the Enterprise Resource Planning Solution. Data completeness is crucial for the Enterprise Resource planning System in order to function effectively and if the data is incomplete it can result in gaps in reporting and this would affect decision-making as well as strategic planning. In order to avoid ERP failure, we should ensure that a data governance framework is developed. Also, establish clear roles, policies, responsibilities as well as standards for data quality across the enterprise.

Within ERP implementations, data quality is a multifaceted concern. In this article let us look at some of how enterprises can overcome data quality issues in implementing an ERP system. Some of the strategies to overcome data quality challenges in ERP system include

Data cleansing and validation

Prior to the implementation of ERP, enterprises must conduct a thorough data cleansing process for alleviating duplicates, correcting errors as well as standardizing formats. This is crucial for ensuring that only high-quality data is being migrated into the new system. After the implementation, regular data validation must be conducted for maintaining data accuracy as well as consistency over time.

“ERP with advantages of improved visibility and compliance also has established a robust reporting system giving a single view of information across the organization. It helped us in bringing efficiency in our work by improving their skill sets especially where the industry is lacking the domain knowledge,” says Vivek Khanna, VP - IT and Finance Havells India.

Establishing data governance

A robust data governance framework is important in order to maintain data quality in ERP systems and this comprises clearly defining data ownership, setting policies for data management across the company, and building data entry standards. What is important is that enterprises must ensure that all the departments are adhering to consistent data quality practices and this can achieved by a well-structured data governance framework.

While it is important to bridge the gap between legal and technical domains, K&S Digiprotect specializes in regulatory compliance, advisory services, and data governance. “Thanks to the information technology revolution, every aspect of life has become data-enabled. Given the rapid pace of digitization and the continuously evolving regulatory landscape, organizations need a partner to navigate this maze and modify their processes and technologies accordingly. That's where we come in”, explains S. Chandrasekhar, MD and CEO of K&S Digiprotect.

Master Data Management (MDM)

MDM ensures that the important or key business data is standardized as well as unified across the enterprise and these include customer as well as product information. It alleviates inconsistencies as well as duplicates by offering a single source of truth for critical data, by enhancing the reliability as well as the accuracy of the Enterprises Resource Planning System.

Automating Data Entry & Integration

Companies must automate data entry processes wherever necessary and possible in order to minimize human errors. While automated data entry tools will help in enforcing consistent formats as well as alleviating common entry mistakes, using automated data integration tools will help in ensuring that the data from external sources is also mapped accurately & standardized prior to being imported into the Enterprise Resources Planning system.

Ongoing Data Monitoring & Auditing

In order to maintain data quality over time regular data monitoring as well as auditing is highly essential. Enterprises must establish KPIs in order to track data accuracy, consistency & completeness. Regular audits can help in identifying issues early & render insights into segments where additional training or process improvements would be required.

Building a data driven culture

Fostering a data driven culture within the organization is key to ensure long-term data quality. Employees in an organization at all the levels must understand the importance of accurate data and how their roles contribute to the complete quality of the Enterprise Resource Planning system. Organizations should ensure that data management practices are consistently applied through embedding data quality into the culture of the company.

“Strategies and data management practices that worked for decades are showing signs of failure while putting organizations at risk and that is just the tip of the iceberg,” says Shahbaz Ali, President & CEO, Tarmin.

“Fortunately, there’s a better way, a data centric approach to data management. This approach focuses on data centricity, drawing actionable insights from data itself, and managing it for, and according to, its value,” he adds.

Investing in data quality tools

There are several data quality tools which are available that can help in automating the process of validating, cleansing as well as standardizing data and these tools can be integrated into ERP systems for enhancing data accuracy & consistency. Therefore, making huge investments in such technologies will help in decreasing manual data management efforts & enhance the overall quality of the Enterprise Resource Planning System. ERP systems play a major role in modern businesses and this helps in serving as one of the centralized platforms which integrate several business processes which include human resources, manufacturing, supply chain, finance and others.