When customer information registered in a CRM system is accurate and up-to-date, companies can make appropriate proposals at the optimal time for their customers. Conversely, if data is outdated or inaccurate, it can lead to wasted resources and a loss of customer trust, such as conducting unnecessary sales activities toward companies with whom business has already concluded, or approaching the same company multiple times.
It is said that the "quality" of B2B data is what creates these differences.
In this article, we explain the concept of Data Quality and provide our perspective on why Data Quality is critical for companies when leveraging B2B data.
What Is Data Quality?
Data Quality refers to how well data is suited for its intended purpose. Generally, Data Quality is composed of the following six elements.
- Accuracy
The extent to which data accurately reflects the actual state of reality.
- Completeness
Whether all necessary data is present and no information is missing.
- Consistency
Whether data is maintained without contradiction across different systems and locations.
- Up-to-dateness
Whether data is current and free of outdated information.
- Reliability
Whether data is obtained from trustworthy sources.
- Availability
Whether data can be utilized whenever it is needed.
By ensuring these elements are met, companies can make faster, data-driven decisions.
Prioritizing Data Quality is considered to yield the following specific benefits.
- Faster Decision-Making Through Improved Accessibility
Accessibility is closely related to Completeness, Consistency, and Availability, and refers to the ability for anyone to easily view data when needed. High accessibility allows various departments and personnel within a company to access necessary data quickly and effectively. For example, the ability for a sales team to instantly access customer data is essential for improving the quality of proposals and customer interactions. Delays in decision-making caused by poor accessibility can lead to the loss of competitive advantage and delayed customer outreach due to the emergence of inefficient operational tasks.
- Establishing a Data-Driven Culture Through Improved Productivity
Productivity is deeply linked to accuracy, reliability, and currency, referring to achieving maximum results with limited resources such as time, effort, and costs. High productivity in B2B data means that data is utilized efficiently and the benefits derived from its use are maximized. For example, by accurately identifying target markets through data analysis and focusing sales activities accordingly, companies can reduce wasted resources and increase revenue. In companies where productivity is low, the lack of strategic resource allocation leads to various lost opportunities, hindering overall corporate growth and causing a loss of market position.
- Building a Secure Data Utilization Environment
Ensuring security is deeply connected to all elements of data quality, protecting data from unauthorized access and leaks. When data security is guaranteed, it means that critical information, such as customer details and transaction data, is stored safely and protected from external attacks and internal misuse. By strengthening security, data can be leveraged as a tool to enhance trust in the company, thereby solidifying relationships with customers and partners. If security is not guaranteed, the constant risk of data leaks or tampering causing significant losses to the company makes employees feel that utilizing data is a risky endeavor.
Why Improving Data Quality Is Important
The reason data quality is critical in B2B data utilization is that it directly impacts the speed, accuracy, and efficiency of decision-making. Because corporate information, market data, purchase history, and contract details are frequently used, data quality affects the smooth operations of every department, including sales, marketing, customer success, and management.
According to Gartner, poor data quality is said to cause significant losses for companies. For example, when data consistency is lost between different departments or systems, information from individual data sources can cause contradictions, such as a situation where customer information held by the marketing department does not match the information held by the sales department. Such situations not only delay decision-making across the entire company but also lower the quality of those decisions, increasing the risk of lost opportunities.
In fact, as Gartner has pointed out in other research findings, poor data quality is said to result in an average annual loss of $12.9 million. Therefore, for companies to maintain a competitive advantage in the B2B sector, the maintenance and improvement of data quality are essential, requiring the development of appropriate systems and processes.
To summarize the concepts proposed by Gartner, it is said that the following specific actions are necessary to improve data quality.
- Data Profiling
First, begin by assessing the current state of the data to verify its accuracy and completeness. This allows for the identification of problem areas and clarifies where improvements are needed.
- Data Cleansing
This involves the process of deleting or correcting inaccurate or duplicate data. For example, if the same company is registered under multiple different names, this process includes unifying those records.
- Data Integration
Establish a mechanism to integrate different data sources from both inside and outside the company to provide consistent data. This integrates siloed data and improves overall data quality.
- Establishing a Data Governance Framework
We clarify data management responsibilities and establish rules for data creation, modification, and utilization. This ensures the long-term maintenance of data quality.
- Continuous Monitoring
Data quality is not a one-time improvement project. It is essential to continuously monitor data and implement improvements as needed.
For leaders in data management and analytics departments, it is crucial to clarify how improving data quality impacts the business, establish company-wide standards, and communicate the benefits of continuous, organization-wide data quality improvement to achieve ultimate business goals.
Actions Required for Improving B2B Data Quality
To improve the quality of B2B data, the following specific initiatives are required:
- Data Standardization
Data standardization involves unifying the formats and definitions of data used within a company. In Japanese companies, it is common for data to be managed using different formats or definitions across departments. For example, one department might record a company name in Kanji, while another uses Roman characters, leading to data inconsistencies. This makes data integration and consistent analysis difficult. To advance standardization, it is necessary to first analyze existing data to eliminate duplicates and contradictions. It is also effective to introduce tools that automatically unify formats during data entry. For instance, using an address standardization tool to unify addresses entered in different formats simplifies subsequent processing and prevents errors.
- Digitization of Business Processes
Digitizing business processes involves converting paper-based operations into electronic formats to streamline data entry, management, and sharing. Many Japanese companies still rely on paper-based contracts and purchase orders, which presents several issues. First, paper media requires manual data entry, which is prone to human error. Furthermore, it is difficult to share data in real time, which hinders rapid decision-making. By advancing digitization—for example, by switching to electronic signatures for contracts—data is immediately saved in a digital format and can be shared instantly among stakeholders. This improves data accuracy and enables real-time updates, thereby accelerating business speed. Moreover, digitized data is easily integrated into analytical tools, allowing for the extraction of insights that support strategic decision-making.
- Utilization of External Data
The utilization of external data involves building richer and more accurate datasets by combining a company's internal data with reliable external data sources. In the B2B sector, leveraging external data—such as domestic and international market trends, competitor information, and customer intent data (purchase interest and behavioral data)—is critical for building a competitive advantage. For example, when a Japanese company expands into overseas markets, it is essential to incorporate local corporate information and regulatory data from external sources. Additionally, by utilizing data from reliable database providers, companies can acquire detailed attributes of target firms and relevant market data, enabling more precise targeting and market analysis.
Data standardization, digitization, and the proactive utilization of external data will serve as key elements in enhancing corporate competitiveness and supporting success in the global market.
Conclusion
Our customer data integration solution, uSonar, is equipped with LBC, one of the largest corporate information databases in Japan, which is characterized by its accuracy and richness. Furthermore, to evaluate and improve internal data quality, the matching report utilizing uSonar is highly effective. This report allows companies to cross-reference internal data with LBC data to identify and correct duplicate or inaccurate information. It also provides essential information for improving data quality, such as data cleansing, attribute enrichment, and the visualization of corporate groups.
As Gartner points out, understanding that improving data quality is directly linked to business success and committing to company-wide data quality enhancement is key to increasing corporate competitiveness. By leveraging a highly reliable external database like uSonar, companies can grasp the current state of their data quality and implement concrete improvement measures to enhance the accuracy of their internal data.
