Data Utilization in Business: Essential Processes and Challenges to Address

Organization, Classification, and Updating for Corporate Data Utilization

By creating integrated data, companies can not only execute efficient marketing initiatives based on appropriate STP analysis (*1), but also manage information to conduct sales approaches aligned with customer status and implement retention strategies.

However, the challenge is that data cannot always be collected in a "clean state." Taking corporate data (company names) as an example:

• Names become outdated due to mid-term name changes following business integrations.
• Name changes occur for the purpose of strengthening branding.
• Abbreviations become generalized, leading to discrepancies between official names and input information.

These issues make it difficult to identify where the "latest source of information" is located within the company. [As an aside, companies like Dropbox in the U.S. refer to this "latest source of information" as the Single Source of Truth (SSOT) (*2).]

The necessary tasks to make data easier to handle are "Organization," "Classification," and "Updating." In the specific process of the company name example mentioned above, the following tasks occur continuously:

• Organization
  - Consolidate duplicate corporate information into the official name.
• Classification
  - Delete outdated information.
  - Separate the latest information.
• Updating
  - Append the latest information to the consolidated company names.

By "Organizing," "Classifying," and "Updating" data to create a "clean" integrated dataset, it becomes easier to append not only internal data but also a larger volume of third-party data.
By adding information that the company previously did not possess, it becomes easier to gain deeper customer insights and design more concrete sales strategies and marketing initiatives.

*1: Abbreviation for Segmentation/Targeting/Positioning. One of the representative frameworks in marketing.
*2: Reference URL

Four Challenges for Corporate Data Utilization

Interpreting the results (*3) of the "Survey and Research on the Economic Value of Digital Data and the Current State of Its Utilization" conducted by the Ministry of Internal Affairs and Communications in 2020, it can be inferred that many B2B companies are not engaging in integrated data utilization initiatives such as "forecasting" or "AI utilization," and that data utilization in B2B companies is currently being conducted without integrating flawed information.

The causes of this situation are common to many companies and are referred to as "Data Utilization Challenges." Specifically, they consist of the following four items:

1. Integration and Correction (Data Quality Management)

If a company assumes that all the data it collects and holds is correct, its data utilization efforts will fail. As noted in the company name example, it is necessary to adopt the perspective that outdated and inaccurate information is mixed in.
By creating a management system that distinguishes between necessary and unnecessary data from the data held by the company, it is possible to improve data quality and establish a framework for faster and more accurate decision-making.
It is said that in the U.S., the average annual loss for companies due to poor data quality reaches 10 million dollars (*4), which has led to the widespread adoption of data quality management concepts utilizing technology.
In Japan, data quality management is also considered an important corporate response item, and a guidebook (beta version) has been released by the Government CIO (*5). However, since data quality management involves significant transformation and resistance, such as the introduction of complex tools and the renewal of workflows, the current reality is that many Japanese companies are not progressing as expected.

2. Augmentation and Standardization

There are other positive effects of improving data quality. It ensures greater "data integrity." In particular, when performing "data augmentation"—such as using third-party data to add more information—being able to prepare high-quality, up-to-date data without notation discrepancies is a major advantage.
Most providers of third-party data excel in data currency. Conversely, this means that outdated or incorrectly formatted data is quickly removed from their databases.
Furthermore, third-party data is not perfect. There are providers that lack comprehensiveness in exchange for strengths in currency. To maximize the use of data from providers that offer the latest information under specific conditions (e.g., limited to listed companies or industry-specific), it is important for the recipient to also maintain accurate data.
To maintain accurate data, it is necessary to establish standard data formats. This standardizes the rules for how data is collected and held. It is also important to visualize which parts can be collected independently by the company (and which parts result in missing data) to understand what kind of augmentation data is required.

3. Workflow Organization and Adoption

Simply preparing an environment for accumulation does not mean that preparations for data utilization are complete.
As mentioned at the beginning, it is necessary to establish an environment where collected data can be easily referenced.
By defining certain reference rules and ensuring that everyone in the company adheres to the usage methods based on those rules, the company can maximize the resources of the sales and marketing departments to collaborate on lead acquisition and customer satisfaction improvement, thereby maximizing corporate revenue.
Specifically, activities can be coordinated by extracting company lists based on conditions such as "ideal customer profile," "high-potential customers," or "customers with high similarity in corporate attributes" from high-quality data collected from inside and outside the company, and further narrowing down the targets to approach now while observing recent behavioral data (interests) and published content (news).

4. Building and Operating a Cloud-Based Data Utilization Framework

Speed is required in the environment where data is referenced.
If a user says, "I was able to extract the data easily, but it was information from two weeks ago," it is impossible to provide an approach that exceeds customer expectations.
Although the sales and marketing departments lead the list creation using data, they cannot handle fresh data using analog methods.
This is where cloud-based data utilization solutions become necessary. The strength of the cloud lies in the speed of reflecting and extracting updated data, and it enables multiple people to work simultaneously. Additionally, since settings can be saved for each individual user, it leads to labor saving and efficiency in routine work.
There is also a secondary benefit in that data linkage with tools such as MA and CRM, which actually create and manage customer touchpoints, can be performed smoothly, allowing for faster communication between the IT department and the sales/marketing departments.

To solve all "Data Utilization Challenges," it is important not only to organize internal data but also to determine the criteria for selecting third-party data providers. A cloud service that combines "currency" and "comprehensiveness" and has a proven track record of integration with MA and CRM is desirable.

*3: Reference URL *4: Reference URL *5: Reference URL

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[Key Points] Necessary Preparations for Building a Data Utilization Framework

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The following points are considered essential for maximizing the utilization of first-party and third-party data.

First-Party Data

• Maintain data that is always up to date
• Eliminate notation inconsistencies and duplicates
• Manage data using a single tool and a unified format
• Create an environment accessible to everyone in the company
• Identify content prone to data loss

Third-Party Data

• Use to augment first-party data
• Select providers with high update frequency and comprehensive coverage
• Prefer providers that offer cloud-based solutions

By keeping these points in mind, data utilization within an enterprise can be advanced.

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