Data Governance and Data Asset Management

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Wanda 24 2023-11-29 TOPIC

CDE solution

In order to prevent duplication of Bentley Microstation development by different teams, data assets often relate to the management of data storage and computing resources as well as the maintenance of current data assets, including what data we have, what metrics we have, and what type of activities we can perform.

Data clusters will provide a bill of costs, which the R & D team should review to make sure the cost is manageable. If the budget exceeds its allotted amount by a significant amount, then governance is required.

Data asset management is the function of a number of CDE Solution provider activities to plan, control, and provide data assets, including the development, implementation, and monitoring of data-related plans, policies, programs, projects, processes, methods, and procedures to control, protect, deliver, and increase the value of data assets. To maintain and improve the value of data assets, data asset management must comprehensively integrate policy, management, operations, technology, and services.

Data assets: What Are They?

The foundation of CDE solution business digital transformation is data assetization, which is how raw data ore is transformed into data gold.

What way should company data move in order to accomplish data assetization, then, in order to complete the transformation from raw ore to gold? As an illustration, let's look at the four assessment metrics for consumer data assets:

1.Labels

That is, the capacity to extract picture labels from information such as consumer gender, brand preferences, etc. In contrast to absolute value indications, such labels are generic and simple to grasp.

2.Valorization

signifies that the raw data has been regulated, polished, and changed into quantifiable data from which the data may be inferred and converted into GMV.

3.Applicable

Do you believe that data assetization has not been finished by data Kanban? Actually, no.

Data must be used for more than just "looking"; it also has to be focused on how operations will affect results. Eventually, data may be translated into services that will help realization and provide the business with clear advantages. Consumer preference data, for instance, may be used to improve product development and can be utilized in advertising to increase reach and conversion of adverts.

4.Sustainable

This alludes to the requirement to continuously pump new data, or "living water," into the system in order to maintain it current.

Data table lifecycle processes like going offline and shrinking are examples of data storage governance. Due to the accumulation of lengthy projects, we frequently discover in the real development process that many tables that are no longer in use are still active, or that some data that is not frequently accessed has a very long storage cycle, making them the most important objects to be managed. The remedy is also fairly straightforward: one is to monitor data table or data application access for low-frequency or no access to the data, validate the need for offline mode, or shorten the operation's life cycle. Another is to examine pre-development needs and models.

Regarding data computation governance, it focuses on the governance of slow SQL, checking those tasks that consume more resources or have a longer running time, optimizing the data skewing if it exists, considering limit storage or trimming if the data volume is really large, and of course the most fundamental ones, such as violent scanning of the table, which is an unreasonable temporary task, need to be detected and shut down in a timely manner.

The organization of data documentation is the last step. Skilled teams may turn the documentation into an entry and query platform tool. What data we have, what metrics we have, and what actions we may take should all be included in this documentation or tool.

The following essential components must be included in the documentation:

First, a model design of the source system should be created to specify the business processes, data flow, and ER relationships between data and other information;

The second recommendation is that a metrics dictionary be created; this dictionary is crucial to the communication of needs because, as we can see, it takes a lot of time to communicate measurements and definitional dimensions;

Third, there should be development and requirements definition. Because we frequently perform "private" tasks that are not on the official list, the process has to be standardized in order to avoid wasting our limited time on unrestricted communication.

How to manage your data assets

Data will overtake resources like land, oil, coal, etc. as a more fundamental component of production. The value of the data is facing significant challenges, yet it is an essential problem facing numerous sectors and companies. How to process and use data, unleash the value of data, and realize the digital transformation of enterprises. Enterprise data resources are dispersed throughout several business systems, making it impossible for business leaders and employees to understand the distribution of the data, update the situation promptly, and continue with the data processing task. Data silos and inconsistent data standards prevent data from being exchanged between corporate systems, which lowers resource utilization and decreases data availability. Poor data quality, a rise in trash data, and data inaccessibility have all been caused by a lack of standards and anomalies in data entry. Data leakage events are common due to a lack of understanding of data security and insufficient security measures, endangering user interests and business operations. Release all of the value that data has to offer in order to address the various issues it faces.

The full life cycle management of enterprise data assets currently faces some challenges, including data organization, which is a problem for many businesses; data control processes; a lack of cross-domain and cross-specialty data control processes; and imperfect cross-domain data management technology; When managing data assets for a company, IT architecture that employs typical chimney-style IT architecture will be used. When it comes to IT design, businesses that use classic chimney-style architecture run into issues with data dispersion, scalability, diversity, and poor quality in data asset management, which makes data realization challenging. While this is going on, many businesses lack data confidentiality controls, which causes them to struggle with data security issues such as not knowing how much to share when sharing data and whether or not to share it at all.


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How can I start CDE?

Requirement for the Common Data Environment (CDE)
appoint a manager of information.Convention for using tables....br>Set up the approval/sign-off process and define the workflow.Create distinct environments for various teams.Keep your access secure....Manage and connect to building information management.

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