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Principles of data management
Data management needs to balance strategy and application requirements, and guide data management practices based on data management principles.
(1) Data is an asset with unique attributes
data is a kind of asset in some aspects, but compared to other assets in management Big difference. Comparing financial and physical assets , one of the most detailed features is that data assets will not be consumed during use.
(2) The value of data can be expressed in economic terms
A consistent method should be developed to quantify the value of data, and the cost of low-quality data and the benefits of high-quality data should be developed. .
(3) Data management means quality management of data
Ensuring that data meets application requirements is the primary goal of data management.In order to manage quality, the organization must understand the quality requirements of stakeholders and measure data based on these requirements.
(4) Metadata is required to manage data
Data cannot be held or touched. To understand what it is and how to use it, you need to define this knowledge in the form of metadata. metadata originates from a series of processes related to data creation, processing, and use, including architecture, modeling, management, governance, data governance management, system development, IT and business operations, and analysis.
(5) Data management needs planning
Data is created in multiple places, and because the use requires moving between many storage locations, some coordination work is required to maintain the final result Consistent, you need to plan from the perspective of architecture and process.
(6) Data management must drive information technology decisions
Data and data management are closely integrated with information technology and information technology management. Managing data requires a way to ensure that technology serves rather than drives the organization’s strategic data.
(7) Data management is a cross-functional work
Data management requires technical, non-technical and collaborative capabilities.A single team cannot manage all the data of the organization.
(8) Data management requires an enterprise-level perspective
Although there are many dedicated applications for data management, it must be able to be effectively applied to the entire enterprise.
(9) Data management requires multi-angle thinking
Data is flowing, and data management must continue to evolve to keep up with changes in the way data is created, how it is applied, and consumers .
(10) Data management requires full life cycle management, and different types of data have different life cycle characteristics
Data has a life cycle, and the data will generate more data , So the data life cycle itself may be very complicated.
Different types of data have different life cycle characteristics, so they have different management requirements. Data management practices need to maintain sufficient flexibility based on these differences to meet the life cycle requirements of different types of data.
(11) Data management needs to incorporate data-related risks
Data may be lost, stolen or misused. Organizations must consider the ethical impact of their use of data.Data-related risks must be managed as part of the data life cycle.
(12) Effective data management requires leadership to take responsibility
Data management involves some complex processes that require coordination, collaboration and commitment. In order to achieve goals, not only management skills are required, but also vision and mission from the leadership.
Data management challenges
1. The particularity of data assets is visible, span5 _span5 _span5 _strongp9strong can be touched by mobile assets. They can only be placed in one place at the same time. Financial assets must be recorded on the balance sheet. However, the data is different, it is not tangible. Although the value of data often changes over time. But it is long-lasting and will not wear out. Data is easily copied and transferred. But once it is lost or destroyed, it is not easy to regenerate. Because it will not be consumed while in use, it can even be stolen without disappearing. Data is dynamic and can be used for multiple purposes. The same data can even be used by many people at the same time, which is impossible for physical or financial assets. Multiple uses of data generate more data. Most organizations have to manage ever-increasing data volumes and increasingly complex data relationships.
These differences make tracking data a challenge.Not to mention using monetary value to evaluate data. It also leads to other problems, such as:
1) Count how much data the organization has.
5 2) Define the ownership and responsibility of the data
3) Prevent the abuse of data _span8
p1spanspan_p 4) Risk management p1spanspan_p 4) Data quality standards2. Value of data (Value)
There is no uniform standard for the value evaluation of data.
Data value is the difference between the cost of a thing and the benefits derived from it. For data, there is no uniform standard for data cost or profit, and these calculations will become complicated.
To evaluate the value of data, first calculate the general costs and various benefits that continue to be paid within the organization. The categories are enumerated as follows:
1) The cost of acquiring and storing data.
2) Replace the cost of lost data.
3) The cost of the impact of data loss on the organization.
4) Risk mitigation costs and potential risk costs related to data.
5) The cost of improving data.
6) The advantage of high-quality data.
7) Expenses paid by competitors for data.
8) Data potential sales price.
9) Expected revenue from innovative application data.
To evaluate the value of data, it is also necessary to realize that the value of data is contextual. In other words, data that is valuable to one organization may be meaningless to another organization. The evaluation of the value of data is also time-sensitive. For example, data that was valuable yesterday may be worthless today. Nevertheless, there are some data in the organization that are permanently valuable, such as customer data. Therefore, organizations need to focus on improving the quality of these core data first.
3. Data quality,Low-quality data brings loss
Ensuring high-quality data is the core of data management. The core of data management is to ensure the quality of data. If the data fails to meet the needs of the user-it does not help the user achieve the intended purpose, then all efforts to collect, store, secure, and use the data are useless. In order to ensure that data meets business needs, the data management team must work with data users to define the characteristics of the data and make it high-quality data.
In the use of data, in most cases, it is necessary to learn in the process of using the data, and further create value. For example, understanding customer habits to improve the quality of products and services. Low-quality data can negatively affect these decisions.
Low-quality data is costly for any organization.
Most of the costs related to low-quality data are hidden and indirect, and therefore difficult to account for. Other costs, such as fines, are direct and calculable. The cost of low-quality data mainly comes from: 1) scrap and rework. 2) Solution and hidden correction process. 3) Low organizational efficiency or low productivity. 4) Organizational conflict. 5) Low job satisfaction. 6) Customers are not satisfied. 7) Opportunity costs, including inability to innovate. 8) Compliance costs or fines. 9) Reputation cost.
The effects of high-quality data include: 1) Improve customer experience. 2) Improve productivity. 3) Reduce risks. 4) Respond quickly to business opportunities. 5) Increase income. 6) Gain insights into customers, products, processes and business opportunities to gain a competitive advantage.
4. Data optimization plan
Obtaining value from data is not accidental.Need to plan in many forms.
Better data planning requires a strategic path designed for architecture, models and functions. It also depends on the strategic collaboration between business and IT leaders, as well as the execution of individual projects. The challenge is that there are often long-term pressures in terms of organization, time, and money, which hinder the execution of optimized plans. Organizations must balance long-term goals and short-term goals when implementing strategies. Only with clear trade-offs can effective decisions be obtained.
5. Metadata and data management
metadata management is the starting point for comprehensive improvement of data management.
metadata describes what data an organization has, what it represents, how it is classified, where it comes from, how it moves within the organization, how it evolves in use, who can use it, and whether it is of high quality data. The data is abstract, and the definition of the context and other descriptions make the data clear. They make the data, the data life cycle, and the complex systems that contain the data easy to understand.
6. Data management is a cross-functional work
Data management includes a series of processes related to each other and the data life cycle. Although many organizations treat data management as a function of information technology, it does require many people from various departments with different skills to complete it.Data management is a complex process because it needs to run through the entire organization. Data management is a complex process. In the data life cycle, different stages are managed differently by different teams.
Data management requires: (1) Business process skills that can plan the production of reliable data. (2) System design skills to plan where to store or use data. (3) High-tech skills to manage hardware and build data operation and maintenance software. (4) Analytical skills for discovering data problems. (5) Analytical skills to understand data and solve new problems. (6) The expressive ability allows people to agree on definitions and models so that they can understand relevant data. (7) A strategic thinking that can discover opportunities and use data to serve consumers and achieve goals.
The challenge now is how people can combine the above skills and visions in order to work with others in the organization to achieve a common goal.
We look forward to your comments below.
DAMA series (0)-Introduction to DAMA International and DAMA China
DAMA series (1)-DAMA data management knowledge system guide, three books
DAMA data management knowledge system 2)-DAMA certification (CDMP, CDGP, CDGA)
DAMA data management knowledge system (3)-data and information
DAMA data management knowledge system (4)-the particularity of data assets and data assets
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