Introduction
A common approach in IT strategy and DX (Digital Transformation) is to “start with data integration.” Driven by the awareness of fragmented data and inconsistent numbers across the company, the goal becomes building a unified platform. However, the pitfall many companies fall into is making data collection an end in itself, assuming it will directly lead to improved management decision quality. This article systematically explains why data integration alone is meaningless and outlines the essential “questions” and “unification of meaning” that management must design beforehand.
The Reason Data Isn’t Used Isn’t About “Volume”
Companies already possess vast amounts of data: sales, customers, behavioral logs, inventory, HR, and more. Yet, in most cases, this data is not fully leveraged for management decisions. The fundamental reason is not the “volume” of data, but the lack of a defined purpose: “What decisions is this data for?” In other words, it’s not the data itself that is fragmented, but the “meaning” of the data.
What’s Needed Before Data Integration is “Question Design”
Data only gains meaning when there is a clear “question.” “What does this company want to decide?” “Which decisions need to be faster?” “Which decisions need to be reproducible?” Building a DWH (Data Warehouse), implementing BI tools, and lining up dashboards while these remain ambiguous will not change decision-making. Data integration without questions merely results in visualized confusion.
What Management Must Design is “Unification of Meaning”
Before data integration, there is one core task for management: defining “what constitutes the correct numbers for this company.” For example, what does “sales” refer to? Who is a “customer”? What state defines “success”? If these differ by department, even gathering the same data and looking at the same numbers will not lead to the same decisions. True data integration is not a technical task, but an “integration of meaning.”
Metrics Don’t Exist Just to “Measure”
Many companies have a proliferation of KPIs (Key Performance Indicators) and KGIs (Key Goal Indicators). However, the crucial point is that metrics exist not merely to “measure” the current state, but to “align decisions.” Metrics where it’s not defined “whether they mean the same thing to everyone” or “what action to take upon seeing that number” degrade into numbers for control or numbers for explanation. What’s needed before data integration is practical metric design directly linked to decision-making.
Data is Inseparable from “Organizational Design”
Which data is seen by whom is inseparable from organizational design. The numbers frontline staff should see, the numbers managers should see, the numbers executives should see—integrating data without designing this hierarchy and roles leads to a situation where everyone looks at the same dashboard and “no one decides.” Data integration should not be a mere aggregation of information, but a mapping (reflection) of the organization and its decision-making structure.
Conclusion
What is needed before data integration is neither technology nor tools like SaaS. It is for management to decide “what kind of decisions this company makes.” Specifically, it means clarifying “which decisions to speed up,” “which decisions to align company-wide,” and “which decisions to leave to human experience.” Without deciding this, collecting data will only increase dashboards and thicken meeting materials, while decision-making actually slows down. Data is merely the raw material for reproducing and accelerating management decisions. The responsibility for deciding what to build with it before gathering the material always lies with management.
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