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 What is Data Governance ？
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:
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 ？
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.
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 ？
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:
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.
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.
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.
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.
- 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.