How Poor Data Quality Can Haunt Your Business: Data Sharing as a Strategy for Prevention

In the fast-paced and data-driven world of business, every organizational process and decision is inexorably tied to the quality of available data. While companies understand the crucial importance of accurate data, the constantly evolving and dynamic nature of information makes reliable data management an ongoing challenge. A recent CDQ study indicates that in any large organization, a staggering number of master data records for customers or vendors are altered or added each minute. Even more concerning, a significant percentage of business partner records are prone to critical violations, and an average of 21% become outdated within a single year. Manual handling of large data volumes is also a common contributor to duplicate records, currently affecting 15% of business partner data. Such issues of data quality come with a high cost, Gartner estimates that poor data quality alone costs organizations an average of $12.9 million annually.

The True Impact of Poor Data Quality

While the importance of data quality in business is widely acknowledged, the true impact of poor data quality on different functions of an organization cannot be overstated. The ramifications of inadequate data quality can be frightening, as evidenced by real-world stories from finance, procurement, marketing & sales, controlling, risk & compliance. These areas of a business are particularly sensitive to issues of data quality as the following examples show.

Poor Data Quality in Finance Can Devastate Your Bottom Line

The results of incorrect data in the finance department can be far-reaching, potentially leading to disputes, revenue loss, and damage to your organization's reputation. For instance, sending invoices to the wrong party or with incorrect amounts can easily result in disputes and subsequent loss of revenue. Similarly, payments directed to the wrong account or those that are rejected can lead to payment delays, requiring additional administrative work to rectify the situation. Furthermore, incorrect data can also cause a poor customer experience, leading to damage in your relationship with the client and your organization's reputation.

Hidden Risks of Poor Data Quality in Procurement

Inaccurate master data can cause various issues in procurement, including incorrect payments made to the wrong party or with the wrong amount, which can result in payment delays or rejections, causing additional administrative work. Such mistakes can damage your supplier relationship, causing long-term implications for your business. It can also lead to incorrect SLA management, where a business partner's master data is not accurate, resulting in multiple entities being created with various SLAs attributed to them or not being recognized as part of a larger corporation. In addition, inaccurate data can lead to incorrect spend analysis, preventing proactive management of supplier groups.

Low Data Quality Can Impede Marketing and Sales Operations

In marketing and sales, having accurate and ready-to-use master data is critical for sending out timely offers and targeted campaigns. However, the use of faulty master data can result in a variety of negative outcomes. For example, incorrect delivery of targeted campaigns can significantly undermine the customer experience in critical phases of the buyer journey, leading to a lower brand value. Additionally, inaccurate sales forecasting can cause problems with supply chain management and production planning, resulting in inefficiencies in operations. Additionally, using faulty master data can lead to defective audience targeting, leading to poor results, missed opportunities, and ineffective use of resources. It can also result in inaccurate reporting, making it challenging to measure the effectiveness of marketing and sales efforts, and ultimately leading to missed opportunities for growth. Lastly, an inability to identify customer groups due to faulty master data can prevent proactive key account planning. These problems can be avoided by ensuring that your master data is accurate, reliable, and up-to-date.

Low Data Quality in the Controlling Department Can Undermine Business Performance

Effective control department operations rely heavily on accurate customer and supplier data. However, if this data is incorrect or outdated, it can lead to several detrimental business problems. For example, incorrect sales reporting can result in legal repercussions, while faulty assumptions based on inaccurate data can lead to suboptimal decision-making. Inaccurate budgeting can also lead to inefficient allocation of resources, and inaccurate financial reporting can lower stakeholder trust and result in reputational damages. Organizations must prioritize data quality in the controlling department to avoid these issues and optimize their operations.

The Risk of Non-Compliance

Incorrect master data can jeopardize the actions of risk & compliance departments and pose a serious threat as it hinders an organization's ability to accurately assess the risk associated with a business partner, potentially leading to non-compliance and legal consequences. Additionally, it can prevent deals with sanctioned parties due to unidentified affiliated accounts, and make it difficult to identify and mitigate risks effectively, leading to financial losses. Furthermore, granting wrong credit limits is a concern as business partner duplicates can lead to multiple credit limits per customer. To maintain compliance and avoid these issues, organizations must ensure high data quality.

Data Sharing as a Strategy for Prevention

By examining these stories in detail, it becomes apparent that robust data management and quality control processes are essential to mitigate risks and drive success across all business functions. Thus, companies need to prioritize agile and robust data management approaches to ensure accurate, reliable, and timely data that can drive success in today's data-driven business environment. In this context, data sharing can significantly enhance data quality. This approach represents a paradigm shift from classical siloed data management in a single organization to a cross-enterprise data management approach. By jointly managing the "overlap" data, data quality rules, and data sources, contributing companies can significantly reduce their efforts while improving data quality.

A proven example of data sharing is the?CDQ Suite , which supports organizations in managing their customer and vendor master data smartly and efficiently (read more about the fundamental aspect of data sharing in my article "Data Sharing 101 "). In his recent article "Mastering Business Partners in SAP ",?Kai Hüner ?(Co-founder and CTO at?CDQ ) demonstrates the power of data sharing with the example of creating customer and vendor records faster, more efficiently, and "first time right". The creation workflow of SAP's Master Data Governance cloud edition requires just two manual entries (i.e., company name and country) - all further company details are automatically enriched from more than 70 trusted external data sources and quality-checked based on more than 2'100 data quality rules. In "Are you a data quality champion or a data quality chump? How benchmarking can help you find out! ", my colleague Simon Schlosser (Head of Product at CDQ ) explains how data quality benchmarks within a data sharing community help to identify gaps in an organization's data quality processes and target their improvement efforts more effectively.

Finally, the following real-world examples illustrate how the CDQ data sharing solution can significantly enhance data quality and business operations:

Lower transaction costs

With CDQ DQaaS (Data Quality as a?Service) , a top global food producer could?lower transaction costs by 14%, by speeding up the business partner onboarding process from 7 to 1 day.

Financial risk mitigated

Supported by CDQ software solutions, one?of the world leaders in specialty chemicals?reduced efforts by 50% for global bank account validations and fastened the onboarding process of new suppliers.

Shorter sales cycle

By leveraging data quality rules and automating data entry, a leading food producer is now creating 80% of their customers?first-time-right and reduced creation time?from 7 days to less than 24 hours.

Master data hierarchies

With the help of CDQ, a leading chemical company automated its D&B hierarchies’ linkages and could increase the number of linked?accounts by 25 times (going from 2,000 to?50,000 linked accounts).

Compliant operations

By leveraging CDQ capabilities, a leading?manufacturer of vertical windows and?doors checked 700,000 business partners?against 1,700 sanction and watchlists?within 20 hours.

Further information about the CDQ Data Sharing solutions and the above illustrated real-world examples are provided in a recent ePaper: CDQ Business Impacts