Solution

Tableau Drive methodology

Moving away from the traditional waterfall development model and switch to a more agile analysis model. Solve the original disadvantages of “requirements and implementation are far apart, requirements are lost during the development process, and the cycle is long”, and create a data analysis culture that is “user-centric, clear business and IT rights and responsibilities, and short cycles and quick results”

Scan code sharing case

Data foundation

four stages of exploring data self-driving force and building self-help capabilities with companionship.

Findings：A measure used to assess whether the company is ready to promote an analytical culture and develop a plan to help the company prepare properly.

Prototype and quick results：A time period for super users to acquire the support and training needed to become a confident analysis promoter. This stage is mainly about formulating “quick results” that can prove the value of business leaders’ analysis and further copy and expand.

Procedures and processes：Prepare process, organizational structure, and technical infrastructure to support widespread deployment. At this stage, mature security, data supervision and other policies will be formulated, and extensive training and activation matters will be prepared.

Expansion：Become a company’s own unique quantitative support analysis capability.

• Findings
• Prototype and quick results
• Procedures and processes
• Expansion

”When we first used Tableau, we only considered dashboards and reports. We never thought that Tableau would transform the entire organization. What it brings is not just a solution or technology, but a change in the culture of data”

⸺Lenovo

Practical journey-ASK empowerment model

DKM annual goal: is to add 100 analysts;

High-tech companies: Let 30% of thousands of financial staff have self-service analysis capabilities, and promote business financial support;

Real estate group in digital transformation: The average daily page views (average daily PV value) of the self-service analysis platform exceed 10,000;

State-owned enterprises into changes: Self-services data analysis to include provincial and municipal assessments, and data sharing implementation.Support Group: Human Resources & ITFind the right personDisseminate relevant information and articlesAttitudesPro-activecuriositysense of responsibilityexploratory spiritopen thinking

SkillsSkills setknowledgeablecommunication skillsSupport group: IT team & DKM team & Tableau teamTableau trainingTableau DayData co-creation workshopSupport group: business users & IT users & core usersIndustry application solutionsInternal application data setKnowledgePractical business practicesindustry experiencesoperational experiences

Socialization ability: Building of socialization capitals through training and empowerment activities – “Data Wisdom, Suitable for Everyone”, 150 sessions have been accumulated, covering Beijing, Shanghai, Guangzhou, Shenzhen and Hong Kong, with more than 3,600 people trained.

Enterprise ability: Building of entrepreneur capabilities by helping companies to build professional data analysis teams by providing in-house company training for organizational capacity building.

Specialization ability:Professional capabilities by cooperating with consulting companies, guiding enterprises in in-depth application exchanges, and building core data applications for enterprise users.

Knowledge base sharing:Knowledge based sharing by optimizing users’ experiences on the product by organizing and sharing the knowledge through sharing platform “Shared a Lead”.

Scenario analysis is being carried out in organization to acquire the real needs of users, and then the bottom layer of the data is improved to form “data standardization”; in this process, through organizational empowerment, “key roles” are cultivated, models are modeled, and common areas are created. The continuous evolution of requirements and diverse scenarios will eventually form a “continuous education” model, forming a new type of organization and team with data creativity.Data standardization

+analysis scenarios

+key role training

+continuous education