Web分析とAI予測モデルの整合性が問題になるのは、AIが「当たる／当たらない」からではありません。計測と最適化が同時に高度化し、同じ事業を見ているはずの関係者が、別々の“入口の数字”と“意思決定の論理”で動くようになったからです。GAやBIで可視化が日常化すると、会議ではダッシュボードが共通言語になります。一方で広告配信、CRM、価格、レコメンドの領域では、予測モデルが施策の配分や優先順位に影響するようになり、「モデルがどう判断するか」が成果を左右する局面が増えます。ここで分析は説明のために世界を切り取り、モデルは最適化のために世界を抽象化するため、目的・定義・時間軸が揃っていなければ一致しないのが自然です。

整合性問題が厄介なのは、ズレが“事故”ではなく“運用の常態”として発生しやすい点です。ダッシュボード上は改善しているのに予算配分が止まる、モデル提案が通らずPoCが延命する、会議が解釈合わせで終わる、といった兆候が出ます。さらに進むと「説明できる数字」か「声の大きい数字」に意思決定が寄り、分析もモデルも価値を出しにくくなります。だから整合性は、データ品質の問題というより、指標設計・目的設計・会議運用の設計の総体として捉える必要があります。ズレをゼロにするのではなく、ズレが出ても扱える構造にすることが、実務での整合性です。

Dear Valued Customers and Partners,

SY Partners would like to inform you of our New Year Holiday schedule for 2026 as follows:

• Holiday: Thursday, January 1, 2026
• Back to work: Friday, January 2, 2026

During the holiday period, our platform will continue to operate normally. Non-urgent support requests will be handled after the holiday. We sincerely thank you for your trust and continued support throughout the past year.

SY Partners wishes you and your partners a healthy, prosperous, and successful New Year 2026.

Happy New Year!

With the widespread adoption of the internet and the advancement of cloud technologies, web services have become a fundamental infrastructure for both personal use and business operations. Functions such as information access, communication, workflow processing, and online payments are now available regardless of device or location, enabling fast and convenient service delivery. As a result, web services are no longer just IT tools but an integral part of everyday life and modern economic activity.

AI’s ability to understand human language and engage in natural conversation is built on a core technology known as Natural Language Processing (NLP). With the rise of advanced systems such as ChatGPT, the quality of machine-generated text has improved dramatically, enabling interactions that are nearly indistinguishable from human communication.

AI-generated text, images, and audio are rapidly transforming both our daily lives and the business landscape. Yet behind this convenience lies a serious challenge: bias. AI bias refers to situations where an AI system behaves in a way that unfairly favors or discriminates against certain groups—whether based on race, gender, culture, occupation, or other attributes. Such bias can lead to discriminatory outcomes and unjust decision-making.

The volume of data handled in business grows year by year. Sales, inventory, website traffic, customer surveys—new information is generated daily, yet many companies still struggle to leverage it. With data dispersed across systems and time-consuming processes for organization and sharing, it’s common to see valuable information fail to translate into decision-making.

In today’s rapidly digitalizing business environment, many companies face challenges such as information sharing, operational efficiency, and team collaboration. In particular, the growing complexity of inter-departmental information exchange and project management can cause workflow delays and errors—ultimately hindering corporate growth. Under these circumstances, implementing tools that support efficient operations has become urgent.