DKP 数据搜索是交互式的数据分析工具，用户只需要在搜索框输入需要了解的数据问题，便可快速得到数据见解。

• 极其简单中文自然语言搜索、0 学习成本、企业内部的百度
• 非常智慧自然语言自动转化为 SQL、AI 多维分析预测、快速发现数据异常
• 尤其轻快实时搜索秒出结果、支持跨终端操作、支持嵌入 IM 和业务系统

欢迎体验数据智能搜索和增强型分析引擎

立刻体验 DKP 数据搜索

DKP 数据搜索如何帮助企业洞察数据

自然语言搜索，提升 20 倍效率

• 数据问题直接问自然语言灵活搜索，支持多种问法和模糊问题，支持语音输入
• 智能推荐问题基于搜索习惯和热门问题提供搜索建议，快速上手无门槛
• 自动展示最优可视化结果根据搜索问题自动呈现最合适的可视化方式，支持切换图表类型、自定义外观设计

灵活可视化展示，自动分析满足 80% 需求

• 自动多维度下钻分析基于搜索问题自动展开多维度分析，挖掘数据背后信息
• 智能预测数据走向AI+ 机器学习预测数据未来走向，发现异常数据，及早预警业务风险
• 直观分析复杂问题贡献度基于搜索问题智能拓展贡献度分析，直观掌握重要维度变化和异常数据

个性化编辑和分享报告，10 分钟生成月报

• 搜索结果直接加入报告图表卡片一键加入报告，简单快速沉淀分析思路报告设计自由编辑，专业而不失颜值
• 与团队分享协作分享图表卡片或报告，邀请团队成员协作，提升工作效率设置报告编辑权限，保护数据安全

跨平台无缝连接，随时随地分析

• 自适应多种终端手机、PC、平板使用不受限，随时随地搜索分析
• 支持嵌入多种办公系统支持嵌入企业微信、钉钉、飞书等办公软件及企业内部业务系统，首创BI 嵌入文档
• 首创 BI 嵌入文档无缝集成 WPS，无需切换软件，即可直接在文档中查询和分析数据、制作报告

多管齐下保障数据安全

• 自动数据备份对数据库进行自动备份，最大限度确保发生操作失误或系统故障时不丢失数据
• 不可逆数据加密使用 NIST 推荐的 PBKDF2 算法，对所有敏感的密码字段进行高强度不可逆加密
• 多维度权限验证支持对接企业单点登录系统，支持工作区、表、行和列等多级权限控制
• 全程 HTTPS 传输用户终端与服务器之间的通信全程 HTTPS 加密传输，防止数据传输中被窃取、改变
• 多用户类型设置内置多级用户类型，灵活的权限控制体系，有效防止数据泄露或信息被篡改
• 支持本地化部署在企业本地化部署，产品不接触用户数据，保证数据管理安全、简便、可控

典型应用场景，业务人人可用

• 会议讨论临时灵感立刻验证
  思路不断线
• 日常决策随时搜索数据支持
  立刻知答案
• 周报日报收藏个性化常搜问题
  效率有提升

The post DKP 数据搜索，数据智能搜索和增强型分析引擎。颠覆式搜索交互，人人可用，人人会用。 first appeared on DKM Ecosystem.

优阅达拥有广泛的合作伙伴网络，其中包括基础设施、数据治理与存储、数据消费等领域的一些知名企业。通过集成，我们确保了自己能够提供功能强大的端到端现代分析解决方案，并且让您能够利用现有的技术投资。

技术集成方案

Docusign 与 Google 应用集成： Google Drive、 Gmail、 Google Docs、 Chrome

The post 技术解决方案 first appeared on DKM Ecosystem.

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

The post Moving awayfrom the traditional waterfall development model first appeared on DKM Ecosystem.

Application requirements:

• To disseminate data management concepts and practices to senior management;
• Update data analysis scenarios from time to time, innovation + novelty;

System architecture diagram

TV wall

video signal

• video matrix system
• large screen control room
• RGB matrix system

control host

users’ PCs

Fully connected and reachable at small and medium , enterprises (SME) levelsExtensive data + centralized securityMore and more enterprises are building their information systems towards the mobile end. By integrating the API interfaces of echart, tableau and other visual components, we can widely integrate and distribute the data application content, and ensure the security of the data content through the restriction of trust. For domestic users, the team can quickly complete the app integration of enterprise wechat, Dingding, Feishu, Lanling, fanwei, Zhiyuan, cloud home, self-developed app and other office applications.High-performance massive data applicationAny large-scale enterprise already has a large amount of data. Therefore, with the expansion of the application scope of data consumption, many enterprises are also facing the scenario of efficient processing of massive data. By using the massive data integration scheme provided by youyida (adopting AWS Redshift, Vertica, Kylin and other technical architectures), you can easily expand the data application results to billions of data scenarios, so that your data value can be quickly reflected.Gradually expanded cluster deploymentRealize the linear hardware expansion that gradually expands.Cloud deployment and hostingThe data knowledge platform servers deployed on Alibaba cloud and AWS can be fully hosted by youyida. Customers only need to focus on their core business and will get timely and accurate data reports.

Scan QR Code Sharing Solution

The post Fully connected and reachable at small and medium , enterprises (SME) levels first appeared on DKM Ecosystem.

Group level executives:

• external disclosure of listed companies
• management data reporting
• report to the superior unit
• industry benchmarking
• mobile/apps.usages

Group-level management personnel:

• performance monitoring reports
• budget management reports
• risk management reports

General Manager and Chief Financial Officer of Subsidiaries:

• business data reports
• financial analysis
• special analysis
• key industry index analysis

Scan QR Code Sharing SolutionFlexibility supported with quick and easily reporting formattingMaking use of Tableau’s grouping function to expand the original corporate merger system into a sector merger system, which improve the efficiency and flexibility of holding or consolidated group financial analysis.Flexible and adaptable level drillingWith Tableau’s switching operation function to perform the drill-down path for analysis for decision-making users. It also support the data diversity of decision-making users which enables design users to efficiently and effectively form iterative scenarios.Intuitive and effective disclosure of informationUsing Tableau’s relationship map combined with the large-screen effect, the collective company’s multi-level equity relationship, personnel relationship, investment relationship, and important asset distribution can be formed into an information disclosure scenario, which not only provide intuitive information for the senior management levels, but also all levels of managers are able to provide data information index.

The post Tableau Best Practices-Holding first appeared on DKM Ecosystem.