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Introduction

“We’re interested in Materials Informatics (MI), but our experimental data is still managed on paper or in individual Excel files…” “We probably need to build a company-wide data infrastructure before we can even think about using AI, right?”

When organizations consider adopting MI or advancing Materials R&D DX, many find themselves held back by the “data readiness barrier.”

Large volumes of historical experimental data often remain in analog formats, making it seem like digitization alone could take years. While it’s true that data is the fuel for AI, and having a well-structured data foundation is ideal,

the belief that “MI cannot begin until data preparation is complete” is a misconception—and a missed opportunity. In our previous article, we introduced the six-step framework (CRISP-DM) for executing MI projects.

However, in environments where data is not yet organized, trying to follow this process strictly can result in teams getting stuck in preparation.

In this article, we take a different perspective.

By revisiting the three stages of DX, we introduce a practical “reverse approach” that enables faster progress—even with imperfect data.

1. The Three Stages of Materials R&D DX

DX is often described as a three-step progression. At Polymerize, we adapt this framework specifically for materials R&D

Step 1: Digitization

Converting analog data into digital assets

Converting handwritten experimental notes into Excel or electronic lab notebooks

Converting paper or PDF outputs from instruments into numerical data (e.g., CSV)

👉 Goal: Transform physical information into a format that computers can handle,

and establish it as data assets.

Step 2: Digitalization

Structuring and standardizing data

Organizing scattered data across teams

Standardizing formats and naming conventions

Preparing data for analysis

👉 Goal: Enable consistent and scalable data usage

Step 3: Digital Transformation (DX)

Driving innovation through data and AI

Applying machine learning to predict material properties

Optimizing formulations and processes

Integrating AI into R&D workflows

👉 Goal: Create business value and accelerate innovation

The Common Mistake: Trying to Do Everything in Order

Many organizations attempt to move through these steps sequentially: Digitization → Digitalization → DX

However, in reality, this often leads to:

Spending years on data preparation

Losing momentum before reaching value creation

Delayed ROI from DX initiatives

A Better Approach: Start with MI First

Instead of waiting for perfect data, a more effective approach is to start with MI early—even with limited or imperfect data.

Why the MI-First Approach Works

Clarifies what data is actually needed → You don’t over-invest in unnecessary data preparation

Creates immediate value → Early insights help build internal momentum

Accelerates learning cycles → Trial → feedback → improvement happens faster

From “Data First” to “Use First”

Rather than asking: ❌ “Do we have enough data?”

Shift the question to: ✅ “What can we learn from the data we have today?”

Practical Workflow (Recommended)

Instead of fully completing Step 1 and 2 first, try this:

Start with available data (even if incomplete)

Run a quick modeling cycle

Evaluate results

Identify missing data

Improve data and repeat

👉 This iterative loop is far more effective than waiting for perfection

Conclusion

Start small. Learn fast. Scale gradually.

You don’t need perfect data to begin MI. In fact, starting early is often the fastest path to meaningful results.

By adopting a reverse approach—starting with MI and refining data along the way— organizations can accelerate Materials R&D DX and unlock value much sooner.

How Polymerize Supports This Approach

Polymerize’s platform is designed to support this iterative workflow:

Data structuring: Standardized templates for faster organization

Modeling: Easy-to-use machine learning tools

Application: Built-in forward and inverse analysis

Whether you are just getting started or scaling your DX efforts, the platform helps you move forward without losing direction.

Get Started

Why not begin with your existing data? Start your first cycle today—and experience how MI can drive real progress.

Is It Too Early for MI Because Your Data Isn’t Ready? A “Reverse Approach” to Accelerate Materials R&D DX — The Three Stages and Data Readiness

Introduction

1. The Three Stages of Materials R&D DX

Step 1: Digitization

Step 2: Digitalization

Step 3: Digital Transformation (DX)

The Common Mistake: Trying to Do Everything in Order

A Better Approach: Start with MI First

Why the MI-First Approach Works

From “Data First” to “Use First”

Practical Workflow (Recommended)

Conclusion

Start small. Learn fast. Scale gradually.

How Polymerize Supports This Approach

Get Started

Masahiro Fujita

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