Before CRM, PA, MMP Tool Implementation, Must-Know Data Taxonomy Design Know-How (with 5 Industry Examples)

If you miss out on designing your data taxonomy now, you’ll fall behind your competitors 🤔

Are you using the vast amounts of data being accumulated in your organization properly?

Companies without data taxonomy tend to only collect data and interpret it incorrectly, analysis time increases, collaboration is misaligned, and eventually automation, personalization, and AI end up being just a pie in the sky. Leading companies at home and abroad are already ahead of their competitors through consistent data language and systems.

We provide everything from real-life cases of 'data chaos' in each business department, such as marketing, CRM, and development, to templates and practical application checklists for each industry, such as e-commerce/travel/platform. If you want to take your organization's data quality and automation competitiveness to the next level through systematic data taxonomy design, now is the time to start.

Features of this course

📌 A complete disclosure of the taxonomy design process used in the field to utilize Braze, Mixpanel, Amplitude, etc.! You can acquire design methods that can be applied immediately to the field with just one listen.

📌 Includes cases from various industries such as e-commerce, travel, and content! It doesn't just contain vague explanations of concepts, but also contains actual cases.

📌 From data naming rules to management systems, a complete A~Z guide! One lecture will take away your fear of taxonomy design.

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After class

• You can build a consistent data system without data duplication, omission, or confusion.

  
  

• It can fundamentally solve the problem of data interpretation inconsistency within an organization.

• You can actually apply the method of designing a customized data taxonomy for each service and industry.

• You will gain the ability to create data definitions and guide documents that anyone can use out of the box.

• It can dramatically increase the efficiency of all data-based practices, including analysis, automation, and marketing.