What challenges must be overcome to advance data democratization? Empowering all employees to master data usage to survive turbulent times

In daily operations, you might find yourself thinking, "I need to quickly reference survey data, but who should I ask?" or "To check ad effectiveness, I have to contact the person in charge..." These minor processes can weigh down your work, and you might feel the stress of not being able to smoothly utilize necessary information.

In today's world where DX and data-driven management are emphasized, many have likely heard the term "data democratization" as a related concept. Until now, analyzing and utilizing big data was limited to specialists like data analysts and data scientists. However, today there is a growing demand for data democratization—empowering everyone to utilize data effectively whenever needed. This article addresses the question: "Is data democratization essential for business growth in the coming era?" We outline the benefits achievable through data democratization and the challenges to its implementation, charting a path for companies to successfully navigate this transformation.

Why "Data Democratization" is Necessary to Respond to a Rapidly Changing Business Environment

Diversifying customer behavior, increasing complexity of field operations, shortening product lifecycles... In recent years, the business environment has been changing at a dizzying pace. Amidst this, the need for "data-driven management" – building business strategies based on data – has grown significantly. How can collected big data be analyzed and utilized to benefit management, without relying on traditional decision-making based on experience and intuition? In today's turbulent times, data-backed management strategies become one of the key factors greatly influencing a company's competitiveness.

Essential to data-driven management is "data democratization." Data democratization means "enabling everyone within the company to proactively and autonomously use data in business activities." Historically, the big data collected by companies was handled only by a select few specialists, such as data scientists. However, this approach prevents rapid, high-precision decision-making and the ability to respond immediately to the rapidly changing business environment. Building a foundation that allows employees without specialized knowledge to freely handle data in their respective workplaces is an urgent necessity.

Even if a company accumulates valuable data, it becomes a wasted resource if employees cannot access it when needed. For example, marketing departments might face issues like, "We need customer behavior analysis data to time product launches, but our department can't access the desired data." Similarly, executives might encounter problems such as, "We need to review critical management decisions immediately, but preparing the necessary data takes too long." To solve these problems, data democratization is essential—enabling many employees within the organization to easily handle data.

That said, very few companies may have fully achieved data democratization. While most companies promote data utilization, they likely haven't reached democratization. If data democratization is the goal, many companies are probably still in the preliminary stages. They might be in a state where a small number of data scientists manage the company's data for limited purposes, or they might not have even started data-driven management at all.

Achieving data democratization enables all employees, including data scientists, to fully leverage data in their daily work. With the necessary infrastructure in place, even without specialized knowledge, every employee can make decisions that maximize the use of data. In fact, alongside leading overseas companies like Google, Japanese IT startups are also establishing such frameworks.

How BI (Business Intelligence) Transforms Work

So, what is needed to establish a framework for data democratization? While various methods and tools are used for data democratization, the core element is the "BI (Business Intelligence)" tool. Many companies collect and accumulate vast amounts of data through systems like CRM (Customer Relationship Management) for managing customer information, SFA (Sales Force Automation) for supporting sales activities, and MA (Marketing Automation) for managing customer acquisition. BI tools are systems that integrate, aggregate, and analyze this big data to support strategic business decision-making.

The benefits of implementing BI tools include enabling easy search and access to data scattered across various locations, and visualizing and presenting analysis results in a way that is understandable to anyone. As a result, the following states can be achieved:

1. Data-Driven Management Through Company-Wide Data Integration

BI tools centralize internal data and enable cross-departmental sharing. This promotes understanding of data utilization among all employees, enabling company-wide data-driven management.

2. Faster Decision-Making

By creating an environment where anyone can access and understand data, decisions can be made quickly without waiting for expert analysis. Even amid rapidly changing environments, you can obtain highly accurate analysis results and form teams with high responsiveness.

3. Providing Products and Services That Anticipate Needs

Centralized management and analysis of customer information allows for the early discovery of potential market opportunities. Releasing products and services that anticipate customer needs based on this analytical insight enhances the company's competitiveness.

The Keys to Successful Data Democratization: "Governance," "Employee Development," and "Field Needs"

We've examined the benefits achievable through data democratization. However, before embarking on this journey, it's crucial to understand common pitfalls. Enabling everyone to access data is essential for data democratization. Yet, simply introducing BI tools and making accumulated organizational data visible to employees does not automatically achieve data democratization. So, what considerations are necessary? Let's learn about the challenges of data democratization and BI by examining three common failure examples.

Failure Example 1: Insufficient Governance Leads to Neglect of User-Friendliness

One reason hindering data democratization is an inadequate governance framework. Without proper control and management of data, problems arise such as unclear data sources, inconsistent data formats, and poor searchability making it difficult to find necessary data. It is essential to establish a unified company-wide mindset for data utilization while designing a data governance foundation tailored to each department's specific needs and challenges.
→ [Challenges to Address] Company-wide alignment on data utilization / Defining departmental needs and challenges and preparing the environment

Failure Example 2: Insufficient skills and literacy among employees handling data

Even if an environment where anyone can access data is built, it cannot benefit the business if employees cannot interpret, analyze, and utilize the data. While expert-level knowledge isn't required, data democratization necessitates the minimum skills and literacy needed to handle data appropriately. Therefore, employee education and development is also a critical challenge.
→ [Challenge to Address] Employee Education and Development

Failure Example 3: Limited Data Access Restricts Utilization of Useful Data Tailored to Operational Needs

Data may contain confidential information, necessitating restricted access rights. However, data often generates greater value when combined with other datasets rather than used in isolation. Excessively strict access restrictions can hinder effective data utilization. It is crucial to set appropriate data access rights based on job position and responsibilities while maintaining flexible operational practices.
→ [Challenge to Address] Setting data access rights according to position and responsibilities

Furthermore, tools combining BI and AI have recently emerged to further enhance the outcomes of data democratization. Visualizing consumer insights and future predictions presented by AI through BI can yield significant benefits not only for CRM utilization but also from the perspective of data democratization, accelerating corporate and business growth.

Moreover, AI excels at processing and analyzing vast amounts of data on behalf of humans and identifying patterns within it. It can also suggest appropriate decisions based on the results of data analysis. In the near future, we may see a division of labor where lower-priority decisions are delegated to AI, while humans make high-level decisions based on BI analysis results. This could accelerate the speed of human-driven high-level decision-making, potentially enabling faster and larger business growth for companies.

Data democratization, enabling all employees to work with data, is an indispensable theme for future business growth. By addressing challenges such as establishing data governance, improving data literacy, and implementing data operations that meet field needs, we can significantly accelerate DX and data-driven management. As a means to achieve swift decision-making, simultaneously advancing data democratization alongside corporate DX may prove highly effective.