为何数据治理对商业变革如此重要

过往的大中型企业的数字化的过程中,我们调研发现,大部分企业的数字化项目,起点都往往选择数据治理((Data Governance) 或者某种程度上的数据治理作为项目的第一步,也是关键性的一步,为帮助最终用户更好地理解数据治理对商业变革的重要性,本文尝试从下列方面来阐述:

  • 什么是数据治理?
  • 为什么数据治理很重要?
  • 有哪些数据治理最佳实践可参考?

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什么是数据治理 What is Data Governance ?

‌数据治理包括管理和保护公司数据资产所需的人员,流程和技术,以保证普遍理解,正确,完整,可信赖,安全和可发现的公司数据。典型的数据治理主题入下图所示:

Data Governance includes the people, processes and technologies needed to manage and protect the company’s data assets in order to guarantee generally understandable, correct, complete, trustworthy, secure and discoverable corporate data. The topics encompassed by data governance are:

数据治理的核心是建立方法,以及一个具有明确职责和流程以标准化,集成,保护和存储公司数据的组织。主要目标是:

  • 降低风险
  • 建立数据使用内部规则
  • 实施合规要求
  • 改善内部和外部沟通
  • 增加数据价值
  • 方便上述管理
  • 降低成本
  • 通过风险管理和优化来帮助确保公司的持续生存

At its core, data governance is about establishing methods, and an organization with clear responsibilities and processes to standardize, integrate, protect and store corporate data. The key goals are to:

  • Minimize risks
  • Establish internal rules for data use
  • Implement compliance requirements
  • Improve internal and external communication
  • Increase the value of data Facilitate the administration of the above
  • Reduce costs
  • Help to ensure the continued existence of the company through risk management and optimization

为什么数据治理很重要?Why importance of  Data Governance ?

数据治理计划持续影响企业的战略,战术和运营水平(如下图所示)。为了在公司范围内并与其他数据项目协调有效地组织和使用数据,必须将数据治理程序视为一个持续的迭代过程

Data governance programs always affect the strategic, tactical and operational levels in enterprises (see figure below). In order to efficiently organize and use data in the context of the company and in coordination with other data projects, data governance programs must be treated as an ongoing, iterative process.

数据治理是众多任务或项目的先决条件,并具有许多明显的好处:

•整个组织中一致,统一的数据和流程是获得更好,更全面的决策支持的前提。
•通过用于更改流程和数据的清晰规则,在技术,业务和组织级别提高IT领域的可伸缩性;
•中央控制机制具有优化数据管理成本的潜力(在数据集爆炸时代,这一点越来越重要);
•通过使用协同作用(例如,通过重用流程和数据)提高效率;
•通过质量保证和认证的数据以及数据流程的完整记录,对数据具有更高的信心;
•达到合规准则,例如巴塞尔协议III和偿付能力II;
•通过监视和查看隐私策略来保护内部和外部数据;
•通过减少冗长的协调流程(例如,通过清晰的需求管理)来提高流程效率;
•通过标准化进行清晰透明的沟通。这是企业范围内以数据为中心的计划的前提;
•此外,每个数据治理计划的特殊性质也带来了特殊的好处。

数据治理对于企业保持响应能力至关重要,这一点比以往任何时候都重要。开拓新的创新业务领域也很重要,例如通过大数据分析,这不允许持久地进行落后的思考和大修结构。

目前,导致公司重新考虑其当前方法的最重要的驱动因素是:

•建立以数据为中心的视图以支持数字业务模型
•企业范围的数据质量和主数据管理
•大数据环境中的数据可管理性
•制定标准以增强对外部影响(例如并购)的反应能力
•自助服务BI(SSBI):用户希望独立于IT进行分析
•合规性:透明且易于理解的数据流程,以符合法律要求

Data governance is a prerequisite for numerous tasks or projects and has many clear benefits:

Consistent, uniform data and processes across the organization are a prerequisite for better and more comprehensive decision support;
Increasing the scalability of the IT landscape at a technical, business and organizational level through clear rules for changing processes and data;
Central control mechanisms offer potential to optimize the cost of data management (increasingly important in the age of exploding data sets);
Increased efficiency through the use of synergies (e.g. by reusing processes and data);
Higher confidence in data through quality-assured and certified data as well as complete documentation of data processes;
Achieving compliance guidelines, such as Basel III and Solvency II;
Security for internal and external data by monitoring and reviewing privacy policies;
Increased process efficiency by reducing long coordination processes (e.g. through clear requirements management);
Clear and transparent communication through standardization. This is the prerequisite for enterprise-wide data-centric initiatives;
Further, specific benefits result from the specific nature of each data governance program.
More than ever, data governance is vital for companies to remain responsive. It is also important to open up new and innovative fields of business, for example by big data analyses, which do not permit the persistence of backward thinking and overhauled structures.

At the moment, the most important drivers that lead companies to rethink their current approaches are:

Establish data-centric views to support digital business models
Enterprise-wide data quality and master data management
Manageability of data in big data environments
Creation of standards to increase the ability to react to external influences (e.g. M & A)
Self-service BI (SSBI): Users want to carry out analyses independently of IT
Compliance: transparent and understandable data processes to comply with legal requirements


                           

数据治理如何入手?How to start Data Governance project ?

数据治理的相关性显而易见。尽管如此,尽管具有优势,但许多公司还是害怕实施数据治理程序,这可能是由于假定的复杂性或总体不确定性。

实施数据治理计划绝非易事。以下是实施阶段的一些最大障碍:

组织

数据治理需要一种开放的企业文化,例如,可以实施组织变更,即使这仅意味着命名角色和分配职责。结果,数据治理成为一个政治问题,因为这最终意味着分配,授予和撤消责任与能力。这里需要一种敏感的方法。

接受与沟通

数据治理需要通过合适的员工在正确的地方通过各方之间的有效通信来接受。项目经理尤其需要了解技术和业务方面的术语,术语,最好是公司的总体概念图。

预算和利益相关者

通常仍然很难说服组织中的利益相关者对数据治理计划的需求并获得预算。另外,变更通常会因根深蒂固而受到阻碍,但是业务部门中不直接可见的资源可以弥补正常运行的过程和信息处理中的不足。

标准化和灵活性

企业需要灵活应对快速变化的需求。但是,至关重要的是,要根据每个公司的业务需求在灵活性和数据治理标准之间寻求适当的平衡。

The relevance of data governance is obvious. Nevertheless, despite its advantages, many companies are afraid to implement data governance programs – either because of the assumed complexity or due to general uncertainty.

Implementing data governance programs is by no means a trivial undertaking. The following are some of the biggest hurdles in the implementation phase:

Organization
Data governance requires an open corporate culture in which, for example, organizational changes can be implemented, even if this only means naming roles and assigning responsibilities. As a result, data governance becomes a political issue, because this ultimately means distributing, awarding and also withdrawing responsibilities and competencies. A sensitive approach is needed here.

Acceptance and Communication
Data governance needs acceptance by means of a working communication between all parties by suitable employees in the right places. Project managers in particular need to have an understanding of the technical as well as business aspects, the jargon and preferably an overarching conceptual view of the company.

Budgets and Stakeholders
It is often still difficult to convince stakeholders in the organization of the need for data governance programs and to get budget. In addition, changes are often hindered by ingrained, but functioning processes and deficiencies in information processing are compensated by not directly visible resources in business departments.

Standardization and Flexibility
Businesses need to be flexible to address fast-changing requirements. However, it is vitally important to seek the right balance between flexibility and data governance standards according to each individual company’s business requirements.


最后附上一个数据治理的框架供参考

How —
Data governance differs from data management -

Data governance = Decision, rules, rights, regulations and policy making for the organisation.
Data management = Execution of all of the governance policies, it is an operational function of an organisation.

What
Data Governance is not —

Data Governance is not a technology function. It is a business function and forms a bridge between business and technology.
Data Governance is a process and not a project. It is a screaming voice of the data.

Who -

The data governance organisation/office structure should be made up of –

  • A cross-functional executive steering committee of executive leaders.
  • A Data Governance Board comprising of data stakeholders that assists to define, plan and execute.
  • Data Stewards kind of new roles responsible for the day-to-day work .

Data Governance Council/Office — This is a centralised, cross-functional decision-making authority and their responsibilities include but not limited to -

  • Direct and strategically align data governance programmes to enterprise vision.
  • Act as a central hub for key data assets and components, such as policies, processes, data standards and compliance.
  • Influence, help and enable organisational strategy for better analytics and decision-making around data.

When -

  • Organisations must assess themselves for market position or performance more frequently. with global competition getting fierce, the current market conditions do not allow for inefficiencies historically tolerated.
  • increased focus on data quality and control procedures for data consistency, accuracy.
  • Need for operational excellence and measurement by clearly defined roles and responsibilities.
  • Mandatory regulation and compliance requirements call for formal data governance.

Finally, some must-have elements that should feature in data governance plan document -

  • Accessibility — Are data available to right people at right time.in right format.
  • Security — Clear guidance on rules and definitions for authorised and non-authorised users of data.
  • Consistent — Single version vs multiple versions of truth.
  • Quality — Conformance to agreed data quality, accuracy and standards.

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