Data Strategy Maturity Guide (draft)

This guide is mainly referenced from the data management capability assessment model proposed by the NITS in 2018 (GB/T36073-2018)

Last updated: 2021/10/15 By Herbert Chia

1.1 Strategic Data Planning

1.1.1 Overview 

Data strategic planning is a consensus reached among different demands of all stakeholders based on the gap between current situation and goals in order to meet the business vision and business strategic goals in the digital transformation stage of the organization. On the basis of the above, the changes in the internal and external environment are fully considered, i) from external macro policies, market changes, competitive landscape, technological changes, etc. as factors to consider for the feasible path; ii) to put the internal focus on the gap between the current situation capabilities and strategic objectives to supplement,including but not limited to business needs, technical capabilities, resource allocation, talent training, organizational structure, compliance, data security and assets management, perfection of job efficiency mechanism, etc. With full consideration of the above, make resource allocation priorities and trade-offs for data strategic planning based on strengths and weaknesses.

1.1.2 Planning Process Description

The process is described as follows:

a) Affirm the importance of data transformation
In the process of business strategy, affirm the necessity of data transformation for enterprises, identify the opportunities and methods of business data transformation, and prepare for the data capacity and resources of future business

b) Identify entry points for datafication
Select the scope of operations that are suitable for prioritizing data transformation in the organization by identifying opportunities where data capabilities can be used to improve the quality of decision making, achieve operational enhancements, and increase innovation productivity for internal purposes.

c) Analyze datafication tasks
 Assess the urgency of tasks and the current state of datafication, prioritize projects and identifying risk based on the needs of the aforementioned strategic plan.

d) Mission planning roadmap
 Develop a plan for a data strategy that includes, but not limited to:

  • A vision statement containing the principles of data governance (including aggregation, governance, application, etc.), goals and objectives;
  • Planning scope, including data scope, security specifications, quality assurance, etc. within the business, across business areas and external collaborations;
  • The data management model and construction method chosen, including how unstructured data are processed and stored, the need for real-time data, etc.
  • Key gaps in current data resources and capabilities versus targets;
  • Responsibilities of management and functional departments, and a list of stakeholders;
  • Preparation of data management plans and relative quantification and management methods;
  • A roadmap for a continuous optimization cycle;
  • Identify resource assurance mechanisms including data, technology, talent, etc.

e) Internal and external perspectives of strategy implementation 

Understand the aspirations of the stakeholders in the organization (internal and external) and clarify the role of data strategic planning for the stakeholders. Based on the situation (b) Make it easy to share data resource and technology internally, establish a mutually beneficial mechanism, and strive for favorable strategic resource cooperation externally.

f) Datafication goes from local to global
Identify resources that can be shared, including data assets, technical capabilities, talent organizations, etc., and empower data capabilities from local to global through technology and platforms.

g) Documentation, standardization, management practices
 Documentation of the approved strategic data planning and implementation details, including some standardization, principles and management practices for data use, security, sharing, etc..

h) Regular evaluation and reporting on strategy
(i) Make quantitative and qualitative analysis for the strategic strategy according to the released data and strategic focus, and report to the management regularly;

i) Data strategy changes with the business
To review the data strategy and revise it periodically according to changes in business strategy development, information technology development stage, technology trends, etc.

1.1.3 Focus of the planning process

The process is highlighted below:

a) The importance that management places on datafication, which can be reflected in management’s questions about three areas: 1) how data capabilities can contribute to the business; 2) how to improve the speed and success of data transformation; and 3) what kind of data talent and organization is needed.

b) The more clearly targeted the entry point, the easier it is to focus resources and the higher the success rate. However, to balance the strategic needs for short-term and long-term development, more long-term and common needs must be identified, and a data strategy is not only a means to meet short-term interests, but also to build the foundation needed to address future digital transformation.

c) Most enterprises tend to focus on projects that are easy to be concerned when developing priorities, while ignoring projects with low return on investment in a short time but important for future development

d) Allocating the resources, processes, technology, and talent needed for the task around the goal helps focus and save significant costs in the transformation process.

e) In general strategic planning, the external perspective is easily seen as an important element in competing for business, thus neglecting the value and strategic position of big data in the same ecosystem when it comes to collaborative flows.

(f) The key to promoting data from local to global is collaborative development, and the most difficult work is to determine the scope, time and resources of standardization;

(g) If the business is to achieve its mission with individual objectives and at the same time take into account the long-term strategic direction, the organization needs to develop a strategic framework document within the organization so that the majority of people can understand the general direction and purpose of the business.

(h) The quality of planning needs to be verified by practice, so periodic review is necessary, but the evaluation method, in addition to quantifiable indicators, is best to form cases and accumulate experience;;

I) The process of business strategy discussion should involve data issues, and the revision of data strategy should change with the business cycle;

1.1.4 Capacity level criteria

Capacity level criteria are as follows:

(a) Level 1: Initial level

Realize the influence and importance of data to business in the process of business, and set corresponding data management objectives and scope for these data resources and capabilities, as well as relevant safeguard measures.

1.1.2a(F) – Affirm the importance of data transformation

(b) Level 2: Managed level

  • Identify stakeholders relevant to the data strategy ;
  • The data strategy is developed in accordance with the business strategy and the relevant management processes already in place;
  • There are mechanisms to maintain the linkage between the data strategy and the business strategy.

1.12a (F) – Affirm the importance of data transformation , 1.1.2b (F) – Identify entry points for datafication , 1.1.2c (P) –Anaylyze datafication tasks

c) Level 3: Robust management level

  • Develop a data strategy that reflects the needs of the overall organization for business development;
  • Develop systems and processes for the management of data strategies, clarify stakeholder responsibilities and standardize the process of managing data strategies;
  • Provide resources in line with the data strategy developed by the organization;
  • Document the organization’s data management strategy and maintain, review and publish it according to standard processes defined by the organization;
  • Prepare an optimized roadmap of data strategy (from local to global) to guide the data effort;
  • Periodically revise the published data strategy.

1.12a (F) – Affirm the importance of data transformation , 1.1.2b (F) –  Identify entry points for datafication , 1.1.2c (P) – Analyze datafication tasks
1.1.2d (F) – Mission planning roadmap , 1.1.2e (P ) Internal and external perspectives of strategy implementation , 1.1.2f(F) Datafication goes from the local to the global

d) Level 4: Excellent management level

  • Analyze in a quantative way and optimize the management process of the organization’s data strategy;
  • Be able to quantitatively analyze the data strategy roadmap and implementation, and continuously optimize the data strategy;
  • Identify new data resources available in the course of scientific and technological developments;
  • Identify the role of technology such as artificial intelligence and blockchain in data management;
  • Understand the impact of trends in data security compliance in implementing data management.
  • Share best practices in the industry and become the industry benchmark.

1.12a (F) – Affirm the importance of data transformation , 1.1.2b (F) – Identify entry points for datafication, 1.1.2c (P) –Analyze datafication tasks.
1.2d (F) – Mission planning roadmap , 1.1.2e (P) ) – Internal and external perspectives of strategy implementation , 1.1.2f (F) Datafication goes from local to global
.1.2g (F) – Documentation, standardization, management approach , 1.1.2h (F) – Periodic assessment and reporting on strategy , 1.1.2i (F ) – Data strategy changes with the business

1.2 Data Strategy Implementation

1.2.1 Overview

With a clear vision of the data strategy objectives, the organization is enabled to work with each other and with mechanisms to define the implementation path and phased strategy. This includes how to assess the gap between the current state of data management and the goals, and determine the organizational structure, functional development, and resource allocation required to implement the data strategy. Then, during the implementation process, we will observe the output milestones, including the improvement of performance, accumulation of data resources, improvement of technical capability, and progress of organizational innovation, so as to continuously revise and update the entry points, milestones, and task objectives.

1.2.2 Process Description

The process is described as follows:

(a) Plan and breakdown of tasks in terms of importance, urgency, risk control, return on investment and sequential pathways, including the required resource budget, implementation mechanisms and division of responsibilities according to the description of the strategic objectives of the data.

b) Assign management teams with business and technical skills to make judgements on the identification and prioritization of tasks based on the description of the strategic objectives of the data, and to monitor and review them periodically;

c) Provide oversight for the various tasks (business drivers) in the data strategy, including but not limited to:

– Establish business data milestones for the corresponding business, following up on each time stage for completion review and effect analysis.

– Identify the scope of data required by the business and its role in optimizing the business.

– Identify the data governance capabilities required by the business and their role in optimizing the business.

– Identify the technical capabilities required for the business and their role in optimizing the business.

– Identify the requirements of the business for personnel organization and its role in optimizing the business.

– Analyze the discrepancies between the current situation and expectations, and make corrections and iterations in terms of data resources, technology and organization.

d) Provide oversight for projects in the data strategy (data resource development), including but not limited to:

– Establish milestones for the development of the corresponding data resources and following up with completion reviews and analysis of their effectiveness at each time stage.

– Identify the optimal role and relationship played by data resources with corresponding single or multiple business applications.

– Identify the governance conditions and mechanisms required for the development of data resources.

– Identify the technical/platform capabilities required for data resource development.

– Identify the organizational system fit required for data resource development.

– Identify the relationship of data resources to other internal data resources.

– Identify the potential and risks of external data to internal data resources.

– Compare the current results with expectations, analyse the differences, and make corrections and iterations.

e) Evaluate and monitor projects (technical facilities, platforms) in the data strategy, including but not limited to:

– Establish milestones for corresponding data technology and platform development, and review the completion and effectiveness of each time period

– Identify the optimal role and relationship that data technologies and platforms play with corresponding single or multiple business applications.

– Identify the governance conditions and mechanisms required for the development of data technologies and platforms.

– Identify the organizational system fit required for the development of the data technology platform.

– Compare the current results with expectations, analyse the differences, and make corrections and iterations.

f) Provide oversight for risks to the implementation of the overall data strategy, including but not limited to:

– Identify inconsistencies and contradictions between tasks in terms of objectives, organization, resources, and the impact on the implementation of strategic planning;

– Compare the current results with the expected ones, analyze the differences, and ensure the organizational structure, functional development, and resource allocation.

g) Establish a dedicated team for long-term promotion and supervision of data strategy, including strategic planning review, revision, communication, interest mechanism, etc., to deal with the coordination problems in the process of strategy implementation, to balance the common strategic goals and current situation of the organization for the overall stakeholders;  

1.2.3 The Process Concerns are as follows:

a) Enterprises need to have clear business and data strategy objectives as a basis before making good judgement on risk management, importance, urgency, etc. from the overall task as a requirement; secondly, from the overall to each individual task as a requirement and resource planning; Secondly, the demand and resource planning from the whole to each individual task; In this process, the management should pay attention to the implementation problems caused by inaccuracy, repeated construction between tasks, departmentalism and so on.

b) With a clear direction for development, there must be an organization within the enterprise that is responsible for conducting a comparative analysis of the status quo and the goals, and identifying the gaps. This is a dynamic process, which requires a good process and management cycle. If the cycle is too long, it will affect the progress, and if the cycle is too short, it will waste resources; Establish an increasingly sound logic and base system for resource allocation based on experience.

c) Data strategy missions that are business-driven are easier to see the return on investment, so the quantification and monitoring of strategic objectives should be more granular, with the aim of agile revision or acceleration.

d) For the data strategy task driven by data resource development, because there is no direct business return for analysis, the management should pay attention to: I) the relevant investment return of the expected use scenario of data resources, ii) the expected impact of the additional data on stock and how it will help the business, and iii) whether the data resource will be an advantage for competition and external collaboration.

e) Data strategy tasks driven by technology or platform, as there is no direct business return for analysis, so management should focus on: i) the impact of platform technology sophistication on future business, ii) the importance of platform technology sophistication to future data strategy, and iii) whether data resources will be an advantage for competition and external collaboration.

f) The implementation paths and timelines for short-, medium- and long-term goals must be balanced in the organization through thorough internal discussions to identify and reach consensus on common goals, and to develop resources and safeguards for implementation based on that consensus.

(g) In the implementation of the strategy, a dedicated team is an important guarantee of success, and its main responsibility is to address coordination issues, so its membership must be representative and fair.

1.2.4 Capacity level standards The capacity level standards are as follows:

a) Level 1: Initial level Reflects the tasks, prioritization, etc. of data management in a specific project.

1.2.2a (p)

(b) Level 2: Managed level

  • Assess the gaps between key data functions and the vision, objectives and goals in the context of the sector or data functional area;
  • Establish and follow priorities for data management projects within the department or data functional area, taking into account operational factors;
  • Set data task objectives within the department or data functional area, and conducting a comprehensive analysis of all tasks to determine the direction of implementation;
  • Establish guidelines for assessing the achievement of objectives for specific management tasks within a sector or data functional area.

1.2.2a (F), 1.2.2b (P), 1.2.2c (P)

c) Level 3: Robust management level

  • Establish systematic and complete assessment guidelines for the data function tasks;
  • (a) Comprehensively evaluate the actual situation within the organization and determine the gap between various data functions and vision and objectives;;
  • Develop a template for reporting on the advancement of the data strategy and publish it regularly to keep stakeholders informed of the status of implementation of the data strategy and issues;
  • Assess priorities for data management and data applications in the context of the organization’s business strategy, using a business value-driven approach, developing an implementation plan, and providing resources, funding and other guarantees;
  • Follow up and assess the implementation of data tasks and updating of the implementation plan in the light of the progress of work.

1.2.2a(F), 1.2.2b(F), 1.2.2c(F), 1.2.2d(P), 1.2.2e(P), 1.2.2f(P)

d) Level 4: Excellent management level

  • Quantitative analysis could be applied to analyse the progress of the data strategy;
  • Accumulate large amounts of data to improve the accuracy of data task schedule planning;
  • Data management tasks are organized to meet the needs of business development in a timely manner, and a disciplined prioritization methodology has been established.
  • Share best practices in the industry and become the industry benchmark.

1.2.2a(F), 1.2.2b(F), 1.2.2c(F), 1.2.2d(F), 1.2.2e(F), 1.2.2f(F), 1.2.2g(F)

1.3 Strategic Assessment of Data

1.3.1 Overview In the data strategy assessment process, the mapping between strategy implementation tasks and applications and projects should be first established, and progress is tracked throughout the data strategy implementation process and documented for use in the assessment. Estimate and analyze based on the breakdown of tasks in the data strategy implementation, including business lead, data resource lead, and technology lead. Because of the long investment period of the strategic tasks, only a small portion can be directly analyzed in terms of financial results for return on investment. The rest need to be assessed by establishing quantitative metrics over the course of the task to help assess progress and effectiveness as the task is implemented.

1.3.2 Process Description The process is described as follows:

a) Establish a dedicated team to be responsible for the assessment of strategic mission accomplishment and establish a results analysis of the tasks related to the data strategy in terms of time, cost and benefits;

b) Develop a task case library, including past plans, implementation processes, scope of relevant tasks (projects), activities, desired outputs and cost-benefit analysis;

c) Conduct assessments for the various tasks (business drivers) in the data strategy, including but not limited to:

– Establish quantitative indicators, qualitative targets, milestones, etc. needed for the evaluation of the effectiveness of the business application, and set the evaluation process and methodology.

– A comparison of the current situation with expectations based on the assessment methodology, with an analysis of the differences and amendments.

d) Conduct assessments for various projects (data resource development) in the data strategy, including but not limited to:

– Establish quantitative indicators, qualitative targets, milestones, etc. needed for the evaluation of the effectiveness of data resource development, and to set the evaluation process and methodology:

– Compare the current situation with expectations according to the evaluation method, analyze the differences and make corrections.

e) Assess and monitor projects (technical facilities, platforms) in the data strategy, including but not limited to:

– Establish quantitative indicators, qualitative targets, milestones, etc., needed for the evaluation of the effectiveness of technology and platform development, and to set the evaluation process and methodology.

– Compare the current situation with expectations based on the assessment methodology, with an analysis of the differences and amendments.

f) Access the overall data strategy for implementation, including but not limited to:

– Establish an investment model as the basic theory for the investment return analysis of data function projects. The investment model ensures the rational allocation of required capital on the premise of full consideration of costs and benefits. The investment model should meet the information technology needs of different businesses and the corresponding data function content. At the same time, it should be widely communicated to ensure forward-looking support for business or technology, and comply with relevant regulatory and compliance requirements;

– Stage evaluation: in the process of data work, regularly evaluate the benefits of the achievements from the dimensions of business value and economic benefits.

1.3.3 Process Objectives The process objectives are as follows:

a) Periodic reviews for tasks related to the implementation of the strategy, starting with a comparison of results (including phased results) with expectations, provided that the expected results are defined and measured before the task is undertaken, and then there is an opportunity to make a neutral evaluation of the results of the implementation of the task.

b) Considering that the strategic objectives are long term and involve many internal and external factors, a good case base and collection mechanism will help to improve objective judgement in future strategic planning and implementation.

c) Continuous optimization of models for input-output analysis, implementation and evaluation of business-related tasks based on experience;

d) Continuously optimize, based on experience, the model for input, implementation and evaluation of tasks related to data development, with particular attention to the direct and indirect use of data resources by the business after development.

e) Continuously optimize the model for input, implementation and evaluation of platform/technology-related tasks based on experience, as opposed to business and data resources, which cannot be evaluated purely on the basis of input and output, but rather on an in-depth understanding of the platform/technology, including their leadership, compatibility and operational efficiency.

f) In assessing the operations, data resources and technology platforms as a whole, it is also necessary to assess the mechanisms in the organization that allow them to coordinate and work together efficiently.

1.3.4 Capacity level standards The capacity level standards are as follows:

a) Level 1: Initial level

1) Build the business case for data function projects and activities within the scope of the project;

2) Investment budget management for data management projects through basic cost-benefit analysis methods.

(b) Level 2: Managed level

  • Business cases and mission effectiveness assessment models have been developed based on business needs within individual departments or data functional areas;
  • Establish a standard decision-making process for business cases within individual departments or data functional areas, with clear stakeholder responsibilities therein;
  • Stakeholders participation in the development of investment models for data management and data application projects within individual sectors or data functional areas;
  • Within individual departments or data functional areas, relevant data tasks have been assessed against the task effectiveness assessment model.

c) Level 3: Robust management level

  • Build relevant business cases for data management and applications within the organization, based on standard workflows and methodologies;
  • The benefit evaluation model of data task and relevant management measures have been formulated within the organization;
  • Business cases are developed with the support and participation of senior management and business units are within the organization;
  • Adopt cost-benefit guidelines to guide the prioritization of the implementation of data function projects within the organization;
  • The data strategy implementation tasks are assessed and managed within the organization through the Task Effectiveness Assessment Model and included in the audit.

d) Level 4: Excellent management level

  • Build a dedicated quantitative methodology for data management and data applications, measure and assess changes in data management implementation entry points and base implementation, and adjust funding budgets;
  • Analyze cost assessment criteria for data management using statistical or other quantitative methods;
  • Use statistical or other quantitative methods to analyze the effectiveness and accuracy of funding budgets in meeting organizational objectives.
  • Share best practices in the industry and become a benchmark