Foreword: As a data service worker---senior "Party B", I have seen, heard or personally experienced many data governance related projects, such as: data exchange and sharing projects, data governance projects, master data projects, meta Data projects... Among these projects, there are very successful ones—very well used; If we review these projects, it may not be difficult for us to find that there are many factors that affect the success or failure of data governance projects, such as management, business, technology, corporate culture, and so on.
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In my opinion, the success factors of a data governance project can be summarized as follows:
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Enterprise Data Strategy Management
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Data Governance Architecture Design
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The Timing and Entry Point of Data Governance
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Organization and security system construction
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Technology and tools - if a worker wants to do a good job, he must first sharpen his tools
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Establish a long-term operation mechanism
In the next period of time, I will share with you the various success factors of data governance projects, welcome to pay attention!
The theme of this issue is [Successful Elements of Data Governance 1: Strategic Management of Enterprise Data], the outline of this article:
1. What is a data strategy?
2. What is not a data strategy?
3. The four elements of a data strategy
4. Three levels of data strategy
5. Methods and tools for data strategy formulation
6. Summary
1. What is a data strategy?
Strategy was originally a special term in the military field, referring to the planning and strategies that guide the overall war. Strategy determines the direction and content of the organization's activities, and solving the problem of "what to do" is a fundamental decision. In DAMA-DMBOK, the data strategy is defined as follows:
Strategy is a collection of choices and decisions that together map out a high-level course of action to achieve high-level goals. Typically, a data strategy is a strategy for a data management plan, a plan for preserving and improving data quality, integrity, security, and access. However, a data strategy plan may also include a business plan to use information to achieve competitive advantage and support corporate goals. A data strategy must come from an understanding of the data needs inherent in the business strategy that drive the organization's data strategy. The components of a data strategy include:
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Create an exciting vision for data management
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Data management business case summary with selected examples
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Guiding principles, values and management vision.
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Data management uses and long-term goals
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Management Measures for Data Management Success
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Short-term (1-2 years, specific, measurable, operable, achievable, time-limited) data management plan goals.
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Describe the roles of data stewardship and an overview of their responsibilities and decision-making rights at the organizational level.
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Components of a Data Management Solution
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Data Management Implementation Roadmap
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Project Charter for Data Management
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Scope Statement for Data Management
In simple terms, enterprise data strategy includes: data management vision (long-term goals), medium-term goals, short-term goals, implementation strategies, implementation plans, implementation roadmaps, etc.
2. What a data strategy is not
Is a data strategy a corporate vision?
It may or may not be, or not quite. Let's take a look at the corporate vision of the major companies.
IBM: Whether it is a small step or a big step, it must drive human progress.
Apple: Let everyone own a computer.
Alibaba: the number one platform for data sharing, the company with the highest happiness index, and a life of "102" years.
Tencent: Technology for good.
Baidu: Become the world's top high-tech company that understands users best and can help people grow.
UFIDA: Use technology and ideas to promote social and commercial progress.
Corporate vision is the integration of the essential demands of corporate stakeholders and the highest guidance of corporate strategy, which can be understood as the long-term strategy of the corporate. In today's wave of disruptive technologies sweeping the world, the market is unpredictable, and the long-term data strategy is relatively distant. I prefer short-term data strategies that have clear goals, clear scope, and clear implementation paths, and are executable and achievable. . The world is changing so fast that no one can predict what the world will look like 10 years from now. The designation of an enterprise data strategy must have the ability to respond to market and technological changes. It is stated here that the author is not opposed to enterprises formulating long-term data strategic plans, but to refine short-term executable, achievable, and effective strategic goals based on long-term data strategies. ". Data strategy is part of corporate strategy, which is the planning and deployment to realize the corporate vision.
Is Data Strategy Data Architecture?
Apparently not, at least not quite. Data architecture is used to define data requirements, guide the integration and control of data assets, and is a set of overall component specifications that match data investment and business strategy. Data architecture includes correct data definition, effective data structure, complete data rules, and sound data documents. Data architecture integrates data, processes, applications, organizations, norms and technologies, and its typical inputs include: enterprise data model, enterprise value chain analysis, database architecture, business intelligence or data warehouse architecture, data integration and integration architecture, data quality management architecture , and document and content management architecture.
Data strategy and data architecture are not the same thing. The decision to define the data architecture is part of the data strategy, and the decision to implement the data architecture is a strategic decision. The data strategy influences the design of the data architecture, which in turn supports the realization of the data strategy and guides its decisions. The author believes that: data architecture focuses on technology, which is the tactical category of enterprise data management. Data architecture undertakes data strategic goals for the upper level, and implements the data strategy plan for the lower Unicom. The implementation of data strategy requires not only the technical support of data architecture, but also the construction of enterprise data culture.
Since data strategy is not a seemingly ethereal vision, but also a technology-related architecture, combined with the definition of data strategy given by DAMA-DMBOK, we try to summarize what an enterprise's data strategy is. In my opinion: data strategy is the data planning and deployment made by enterprises to achieve certain business goals, mainly including: data strategic goals, data strategy scope and content, data strategy implementation strategies, and data strategy implementation paths and plans, This is what we call the "four elements of strategy."
3. The four elements of a data strategy
1. Data strategic goals - vision and goals
The vision is the starting point for formulating the corporate strategy, and it is the long-term strategy of the enterprise, while the goal is the clear goal that the enterprise wants to achieve in the short term, and it is the short-term strategy of the enterprise. The planning and design of enterprise data strategic goals should not only have the fields of "poems and distant places", but also consider the "immediate difficulties" of life . Just like the Alibaba company we mentioned earlier, most people know that Alibaba is an e-commerce company, but Ma Yun said that Alibaba is a big data company, and its long-term data strategic goal is "the first platform for sharing data, The company with the highest happiness index". Alibaba-based products, such as Tmall, Taobao, Alipay, etc., are producing, collecting, and processing a large amount of data all the time, and these data have the ability to be realized. Through the realization and sharing of data, it is hoped that Ali will become the company with the highest happiness index just around the corner. If we understand "the first platform for sharing data and the company with the highest happiness index" as Ali's long-term data strategy, then Ali's vigorous research and development of AliSQL to replace Oracle was Ali's mid-term data strategy at that time. It took 10 years to implement it; and the "data center" that has been fired into the sky is currently Ali's short-term data strategy, and this strategy has already been realized. Here is just an example for easy understanding, perhaps Ali's data strategy is not the case. Regarding the topic of data center, there are too many concepts on the Internet. If I have a chance later, I can share my understanding of data center, which is skipped here.
2. Scope and content of data strategy - strategic positioning
Strategic positioning is to answer the fundamental question of "what to do" and "what not to do". The strategic positioning of enterprise data is to define the scope and content of enterprise data management/data governance. According to DAMA, the scope of data strategy mainly includes: data architecture, metadata management, data standard management, data quality management, master data and reference data, data security management, etc. Each of the above parts can be formed into a system. For enterprises, how to choose the scope and content of data governance is a question that enterprises have to answer. Here, the author suggests that the data governance positioning of the enterprise should fully consider the following factors: what are the pain points and needs of the enterprise, what are the goals that the enterprise hopes to achieve, can the implementation of data governance solve these problems, and the investment plan of data governance (manpower and capital) ), the expected return on investment. After thinking through all the above issues, your data strategic positioning will be clear—either choose global governance, or choose individual topics that need to be governed urgently.
3. Data strategy implementation strategy - winning logic
The winning logic solves the problems of "how to do it", "who does it", "conditions for doing it", "reasons for success", etc. It is the essence of strategy. We all know that data governance projects involve a wide range of businesses, systems, and many participants, and data governance is a process that requires continuous iteration and continuous optimization, and cannot be accomplished overnight. So where should the data governance project start, who will lead, who will cooperate, and how can we ensure the successful implementation of the project and achieve results? This question is not easy to answer. According to the data projects that the author has seen, heard or experienced in recent years, a large part of the factors of success or failure are determined by this "winning logic". Not to mention successful projects, we see that most of the failed projects may have the following characteristics: unclear goals, unclear scope, insufficient leading personnel, insufficient active participants, excessive superstitious technology and tools, excessive reliance on external resources ... . Doing the right thing is far more important than doing the right thing. Thinking about the winning logic of the data strategy beforehand is much cheaper than summing up the lessons afterwards. The success of a data governance project must be the organic integration of the above factors, ignoring any factor may affect the effectiveness of data governance.
4. Implementation path of data strategy - action plan
The action plan is a "coordinated" planning arrangement adopted to implement strategic goals or guidelines. The action plan solves the specific activity plan of "who", "when", "what to do", and "what goal to achieve". The action plan must be executable, quantifiable, and measurable, follow the closed-loop management of PDCA, and conduct regular reviews and reviews. We mentioned earlier that data governance is a process that requires continuous iteration and continuous optimization, and it cannot be completed overnight. Experience tells us that data governance is definitely not something that can be solved by introducing advanced technology and awesome software . The project construction process requires the high attention of the company's top management and sufficient resource support, an experienced consultant team, and the cooperation of the technical department and the business department, so as to improve the success rate of project construction. However, the success of the project construction phase does not represent the success of data governance. The end point of a successful enterprise data governance project in the construction phase is the starting point of enterprise data governance. There is a long way to go, and the long way to go. Enterprise data governance requires continuous operation. Integrating data governance formation rules into corporate culture is the fundamental "way" of enterprise data governance.
4. Three levels of data strategic goals
The three realms of data strategy - there is no official definition of the content in this section, only based on personal understanding, please correct me if there is any bias. The author believes that enterprise data strategy can be roughly divided into three levels: meeting basic management goals and business goals, innovation and entrepreneurship, and defining the role and status in the digital competitive ecology. These three levels are not different data management goals of different enterprises, but three specific forms of enterprise data strategy at different stages and under different maturity conditions.
1. The first level - short-term goals
Satisfy basic management decision-making and business collaboration. By solving various problems in enterprise data management to meet the needs of decision-making analysis and business collaboration, the author believes that this level of strategic goals is the most basic, most urgently needed, and most able to hit the pain points of enterprises. With years of informatization construction, enterprises have installed multiple sets of business systems, and these business systems are driven by business departments, lacking top-level planning for informatization, and each system is independent, separate systems, and information islands..., between systems The data among them is not standard and inconsistent, resulting in difficulties in application integration and inaccurate data analysis. It can be said that most domestic enterprises are in this state at present, and the development speed of information technology is too fast, which has gradually formed a trend of technology-driven digital transformation of enterprises, and high-quality data assets are undoubtedly the key to the digital transformation of enterprises. Cornerstone.
2. The second level - medium-term goals
Innovation and Entrepreneurship. Realizing the upgrading of enterprise management and business innovation based on data, expanding new business, building new formats, and exploring new models through the use of data is the second level of enterprise data strategy in my opinion, and it is also the medium-term goal of enterprise data strategy. Data strategy is no longer the support of corporate strategy, but guidance, or interaction, at this stage "IT is business"! For traditional manufacturing enterprises, the use of data governance and integration can accelerate management innovation, product innovation, and sales model innovation. Optimization, innovative product design based on market forecasts, fast time to market, and more. For the service industry to use big data to explore new service models, data can broaden the horizons of services, realize the horizontal expansion of the model field, and the vertical extension of service precision. The service model is an attempt by the hotel service industry in terms of business innovation, which greatly improves the stickiness of customers and improves the profitability of the hotel. Such cases are happening every day in financial services, catering services, medical services, educational services and other service industries... In the future, the competition in the service industry will become more fierce, and the utilization value of data assets will become more and more obvious.
3. The third level - long-term goals
Define the role and status in the digital competitive ecology, the highest meaning of enterprise data strategy. Wang Wenjing, chairman of UFIDA, predicted: "All enterprises in the future will be digital enterprises." I deeply agree with this point of view. The transformation of science and technology will change the business form and competition mode of enterprises. In the future digital competition, digitalization will be a core factor that cannot be ignored. The deployment and successful implementation of enterprise data strategy will determine your enterprise's future competition and digital ecology. , is leadership, challenger, niche player, or knockout. "What kind of vision determines what kind of future", the planning of enterprise data strategic vision must have the "poetry and distance" of the future . Integrate the data strategic vision into the company's action policy and core values, and outline the future "picture" of the company. For example, Jack Ma described Alibaba's vision: the number one platform for data sharing, the company with the highest happiness index, and a life span of "102" years.
5. Data strategy formulation methods and tools
The formulation of the data strategy is based on the enterprise strategy, the business value chain is the model, the management application is the goal, and the executable activities are the steps. Through systematic thinking, the information and the laws between the information are excavated, and after scientific planning and Design to form a blueprint for enterprise digital operation. For the method of data strategic planning, the industry has not yet formed a mature methodology system. However, the methodology of IT consulting and IT strategic planning is relatively mature and can be used as a reference for enterprise data strategic planning. Let's first look at the major well-known Consulting firm's approach to IT strategic planning:
Accenture IT Strategic Planning Methodology
IBM IT Strategic Planning Methodology
Deloitte IT Strategic Planning Methodology
Regardless of the methodology, it is essentially the same for IT strategic planning, which basically includes three steps:
1. Research and analysis, key activities include: strategic understanding, demand analysis, status quo assessment, industry best practice comparison...
2. Long-term planning, key activities include: business planning, organizational structure, technical structure, data structure, application structure, IT support...
3. Implementation strategy, key activities include: project implementation, progress and quality control, benefit analysis, basic support...
The above IT consulting and planning methods are also applicable to the planning and design of enterprise data strategy, but the following core issues need to be considered when planning and designing data strategy:
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What are the business objectives of the enterprise? How do they relate to data needs?
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What are the core criteria that organizations use to define their data management business objectives?
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What metrics or key performance indicators exist to ensure data management meets business objectives?
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How are the data management components implemented and measured for effectiveness?
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How to determine long-term and incremental results (phased)?
Strategic planning is an art, which is extremely complex and involves all aspects, and requires relatively high compound ability of consulting planners. Commonly used strategic planning tools include strategic map, gap analysis, SWOT analysis, PEST analysis, 5W1H analysis, development driving force analysis, Porter's five forces analysis, BCG matrix analysis, McKinsey three-level method, value chain analysis and basic competitive strategy. The use of tools is to help enterprises better conduct strategic analysis and provide a starting point for the formulation of strategic blueprints.
Note: There are many tools for strategic planning, so I won’t list them one by one here. I will take the time to share with you the various tools involved in strategic planning.
Taking the most commonly used 5W1H method as an example, we use data governance planning as an example to see the questions that data strategy should answer:
what: what to do. The content and scope of data governance.
how: how to do it. Data governance implementation paths, methods, and strategies.
who: Who will do it. The responsible subject of data governance, organizational structure, and job division.
when: what time. Data Governance Implementation Schedule.
why: why do it. Goals of Data Governance.
where: where to apply. Application scenarios of data governance, such as supporting system application integration and supporting decision analysis.
The author believes that in the planning and design of data strategy, compared with what to do (what), how to do it (how), who will do it (who), when to do it (when), and application scenarios (where), why (why) is actually more important. Important issues. Only when the strategic goals are clarified can we guide the direction of follow-up work. If the direction is wrong, it will go further and further.
6. Summary
Data governance goals must be measured against the business value associated with the data. Requiring the direct involvement of the business stakeholders of the enterprise to create and validate, the rules of engagement and an agreed governance framework need to be defined and approved by management to determine how data governance efforts will be put in place. The consistency between the enterprise's data strategic goals and the enterprise's business strategic goals should be established in the governance process to form a mechanism for continuous strategic adjustments as the environment changes. As Jack Ma of Alibaba said: "In the future, there will be no business without data, no data without intelligence, and no intelligence without business." Doing a good job in enterprise data strategy and clarifying data governance goals is the first step for enterprises to implement data governance, and it is also a key factor affecting the success or failure of data governance.
References:
DAMA-DMBOK2.0
IBM IT Consulting Methodology
Deloitte IT Strategic Planning Methodology
Accenture IT Consulting Methodology
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