Do Informationization, Digitization, Intelligence, and Data‑Intelligence Really Require Separate Stages?

1. Informationization: Moving Business Processes into Systems

Informationization solves the most basic enterprise problem – whether business processes can run through a system. Historically, many tasks relied on paper forms, phone calls and Excel sheets, leading to low efficiency and opaque processes. The core of informationization is to migrate these workflows into software.

Typical informationization systems include:

ERP for procurement, inventory and finance

CRM for customer and sales management

OA for approval and collaboration

MES for production execution

WMS for warehouse operations

When this layer is in place, the business has system‑backed processes, recorded workflows and traceable data sources.

2. Digitization: Making Data Flow

Digitization is not merely “paper‑less”. It means that data generated during business processes can be collected, connected, analyzed and reused. In other words, informationization focuses on “process online”, while digitization focuses on “data online”.

For example, order data resides in a sales system, inventory data in a warehouse system, and cost data in a finance system. In the digitization stage these silos are linked to form an analyzable, traceable data chain.

The key questions shift from “Can this process be approved online?” to “Can the data produced by this process be stored and used for later analysis?” This enables analysis of order cycles, delivery efficiency, cash‑flow risk and customer profitability.

Challenges include unifying data definitions, mastering master data, integrating system interfaces and establishing data standards. Enterprises often need to build data synchronization, cleaning, transformation and scheduling pipelines before reliable analysis can begin.

3. Intelligence: Enabling Judgment and Prediction

Intelligence adds judgment, prediction and automated decision capabilities to the system. It is not just attaching an AI assistant; the system must be able to make decisions such as predicting which customers are most likely to close, which products may run out of stock, or when equipment might fail.

Informationization solves “Is the process online?”

Digitization solves “Can data flow?”

Intelligence solves “Can the system assist judgment?”

A prerequisite for intelligence is a solid data foundation; without clean, consistent data, even the most advanced algorithms cannot produce reliable outcomes.

4. Data‑Intelligence (数智化): Data and AI Integrated into Management

Data‑intelligence goes beyond simply having data or AI tools; it means that data and AI become core to daily management and decision‑making. The focus shifts to whether the enterprise can continuously use data and intelligence to optimise operations.

Monthly management reviews become data‑driven analyses of problems rather than simple completion‑rate reports.

Inventory management evaluates profit, turnover and age to decide which products to replenish, control or clear.

Data‑intelligence aims to answer questions such as:

Can management spot operational anomalies faster?

Can business units discuss issues using a unified data set?

Can systems move from post‑event statistics to real‑time alerts?

Can decisions be data‑assisted rather than purely experience‑based?

Can data analysis become a routine mechanism?

Self‑service BI tools (e.g., FineBI) can turn integrated data into interactive dashboards that allow both managers and frontline staff to drill down, filter and analyse without repeatedly requesting ad‑hoc reports from the data team.

5. Practical Guidance: Assess Capabilities Before Chasing Stages

Rather than trying to label the current stage, enterprises should conduct a capability inventory based on four questions:

Are core business processes online (no manual hand‑offs)?

Is data across systems integrated and standardized?

Has analysis become part of daily management instead of a monthly report?

Do intelligent models address real business scenarios and earn user trust?

Prioritise fixing the most critical gaps: first ensure processes are recorded, then enable data flow, then make analysis reusable, and finally operationalise intelligence.

The overarching principle is: “record business, enable data flow, reuse analysis, operationalise intelligence.”

Conclusion

Informationization, digitization, intelligence and data‑intelligence each address a different layer of capability—process, data, judgment and decision‑making. Mature enterprises succeed not by chasing terminology but by solving concrete problems, strengthening each capability layer, and turning data and AI into everyday business value.