Big Data Analytics 简明教程

Big Data Analytics - Key Stakeholders

利益相关者将从项目中受益的组织或业务专业人士。在大型组织中，为了成功开发一个大数据项目，需要管理层为该项目提供支持。这通常需要找到一种方法来展示该项目的业务优势。

Stakeholders are organisations or business professionals who will benefit from the project. In large organizations, to successfully develop a big data project, it is needed for the management to set the project back up. This normally involves finding a way to show the business advantages of the project.

我们没有一个通用的解决方案来解决为项目寻找赞助商的问题，以下要点如下 −

We don’t have a unique solution to the problem of finding sponsors for a project, following key points are as below −

Check who and where are the sponsors of other projects similar to the one that interests you.
Having personal contacts in key management positions helps, so any contact can be triggered if the project is promising.
Who would benefit from your project? Who would be your client once the project is on track?
Develop a simple, clear, and exciting proposal and share it with the key players in your organization.

利益相关者包括项目发起人、项目经理、商业智能分析师、数据工程师、数据科学家、数据库管理员和业务用户。这一发现计划的第一阶段将是项目经理和主要利益相关者坐下来尽早协商适当资金的好时机，项目运作而不是被搁置以备以后讨论。

Stakeholders include the project sponsor, the project manager, the business intelligence analyst, the data engineer, the data scientist, the database administrator and the business user. It is considered that the first phase of this Discovery programme will be a good time for project managers and key stakeholders to sit together and negotiate on appropriate funding at an early stage, project functioning rather than being put on hold for later discussions.

文档编写过程是一个关键部分，其中标记了问题陈述、项目目标陈述和目标。该文档包含与主要利益相关者共同实现目标和目标的要求、成功标准以及项目的最低可接受结果。

A documentation process is a critical part in which the problem statement, project goal statement, and objectives are marked. The document contains the requirements to achieve the goal and objectives, the success criteria, and the minimum acceptable outcome for the project with the key stakeholders.

分析挑战应与利益相关者协商并定义。但是，在某些情况下，项目发起人可能有一个预先确定的答案，可能会存在偏差。因此，部署一种更客观的技术比项目发起人可能绕过的预定义解决方案更为可取。在“发现”阶段，应与利益相关者共同提出和评估假设。

The analytics challenge should be clarified and defined in collaboration with stakeholders. However, in some cases, project sponsors may have a predetermined answer that can be biased. Thus, the deployment of a more objective technique is preferable to a pre-defined solution that may be bypassed by project sponsors. During the "Discovery" phase, hypotheses should be produced and evaluated in conjunction with stakeholders.

利益相关者作为领域专家，可以在制定假设时提供建议和要测试的概念。利益相关者还参与项目的成果和发现，这些成果和发现应向利益相关者展示并传达。分析团队在项目的初始阶段协作以掌握项目需求、目标和假设，并在最后阶段协作共享成果和发现。分析团队的目标比利益相关者更多。

Stakeholders, as domain experts, can provide suggestions and concepts to test while hypotheses are developed. The stakeholder is also involved in the project’s results and findings, which should be presented and conveyed to stakeholders. An analytic team collaborates at the initial phase of the project to grasp the project requirements, objectives, and hypotheses, and at the end, a project to share the results and the findings. The analytic team has more objectives than the stakeholders.

几个关键利益相关者在确保任何大数据分析项目成功方面发挥着至关重要的作用。下图包含一些通常参与大数据分析项目的关键利益相关者 −

Several key stakeholders play a critical role in ensuring the success of any Big Data Analytics project. The following image includes some of the key primary stakeholders typically involved in Big Data Analytics projects −

Key Stakeholders of Big Data Analytics

Business Executives/Leadership

他们正在为组织设定总体愿景和战略，其中包括大数据分析将如何与业务目标保持一致。他们为人工智能计划提供必要的资源和支持。

They are setting an overall vision and strategy for the organisation, which includes how Big Data Analytics will be aligned with business objectives. They’re providing the necessary resources and support for AI initiatives.

Data Scientists/Analysts

他们是创建算法、模型和分析工具以从大量数据中提取见解的专家。他们评估数据，并提出可操作的建议以指导公司决策。

These are the experts in creating algorithms, models, and analytical tools to extract insights from large data. They assess data and make actionable recommendations to guide company decisions.

IT Professionals

数据存储、处理和分析所需的技术基础设施由 IT 团队管理。它们旨在确保数据安全性、可扩展性和与当前系统的集成。

Technical infrastructure necessary for data storage, processing and analysis are managed by the IT team. They’re designed to ensure data security, scalability and integration with the current system.

Data Engineers

这些专家设计、实施并维护数据架构和管道，以收集、存储和处理海量数据。他们确保数据准确、一致且易于访问。

These experts design, implement, and maintain the data architecture and pipelines required to collect, store, and process huge amounts of data. They ensure that data is accurate, consistent, and easily accessible.

Data Governance and Compliance Officers

他们制定数据管理政策和程序，以确保按隐私条例法案（如 GDPR、CCPA 和 HIPAA 等）进行合乎道德、安全的数据处理。

They develop data management policies and procedures to ensure that data is handled ethically, safely, and by legislation such as GDPR, CCPA, and HIPAA, among others.

Business Analysts

他们作为业务世界中各利益相关者与合作的数据科学家之间的桥梁，将业务需求转化为分析解决方案，反之亦然。

They serve as a bridge between the various stakeholders in the business world and the data scientists who work together by converting business requirements into analytical solutions and vice versa.

End Users/Domain Experts

这些专家利用从大数据分析中获得的见解，在自己的领域或部门中进行明智的决策。

These are the experts who use the insights gained from big data analytics to make educated decisions in their domain or department.

Finance Department

财务利益相关者关心大数据分析项目是否具有成本效益，并可能提供预算监管和财务分析。

Finance stakeholders care about the cost-effectiveness of big data analytics projects and may provide budgetary supervision and financial analysis.

Marketing and Sales Teams

这些团队采用大数据分析见解来优化营销工作、更有效地定位客户并改进销售方法。

These teams employ big data analytics insights to optimise marketing efforts, target customers more effectively, and improve sales methods.

Customer Experience (CX) Teams

他们使用大数据分析来研究客户行为、偏好和情绪，进而改善整个客户体验。

They use big data analytics to study customer behaviour, preferences, and sentiment to improve the entire customer experience.

Legal Department

法律专家确保按适用法律和法规使用数据，并且他们处理与数据收集、处理和分析相关的任何法律风险。

Legal experts ensure that data is used by applicable laws and regulations, and they handle any legal risks related to data collection, processing, and analysis.

External Partners and Vendors

组织可能与外部合作伙伴或供应商合作，为大数据分析项目提供专门的专业知识、工具或数据。

Organisations may work with external partners or vendors to supply specialised expertise, tools, or data for big data analytics projects.

寻找一个项目的利益相关者的最佳方式是理解问题，以及一旦该项目实施后将形成什么样的数据产品。这种理解将在说服管理层大数据项目重要性方面提供优势。这些利益相关者之间的有效协作和沟通对于开发成功的大数据分析程序以及实现数据驱动决策的全部价值至关重要。

The best way to find stakeholders for a project is to understand the problem and what would be the resulting data product once it has been implemented. This understanding will give an edge in convincing the management of the importance of the big data project. Effective collaboration and communication among these stakeholders are critical for developing successful big data analytics programmes and realising the full value of data-driven decision-making.