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What are the most effective ways to document and manage data center knowledge?

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Data center knowledge is the collective information and expertise that data engineers and other stakeholders use to design, operate, and maintain data center infrastructure and services. It is essential for ensuring data quality, security, availability, and performance. However, data center knowledge can be scattered, outdated, or inaccessible, leading to inefficiencies, errors, or risks. Therefore, data engineers need to document and manage data center knowledge effectively. In this article, we will explore some of the most effective ways to do so.

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Mohammad Salehi, PhD C.Eng

Data centre design and construction specialist at Stealth Startup

1 Use a standard framework

One of the first steps to document and manage data center knowledge is to adopt a standard framework that defines the scope, structure, and format of the documentation. A standard framework can help data engineers to organize and align the documentation with the data center objectives, requirements, and best practices. It can also facilitate communication and collaboration among data center stakeholders, as well as compliance and audit processes. Some examples of standard frameworks for data center documentation are the Data Center Infrastructure Management (DCIM) model, the Data Center Operations Standard (DCOS), and the Data Center Information Framework (DCIF).

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Mohammad Salehi, PhD C.Eng

Data centre design and construction specialist at Stealth Startup
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In my experience using standard framework and provide enough teaching to the relevant team is the starting point. It is also imperative to ensure that we are targeting precisely when it comes to collect and store the data. This could be communicated to all different stakeholders beforehand to understand their need and plan accordingly. Framework needs to be user friendly and easy to understand for all stakeholders. Remote+ mobile access to the data providing the right level of security is in place would be a bonus.

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2 Document the data center assets and processes

The next step is to document the data center assets and processes that are relevant for data engineering. Data center assets include the physical and logical components of the data center infrastructure, such as servers, storage, network, software, and security devices. Data center processes include the operational and management activities that data engineers perform or participate in, such as data ingestion, transformation, analysis, backup, recovery, and monitoring. Data engineers should document the data center assets and processes in a clear, consistent, and comprehensive manner, using diagrams, tables, charts, and text. They should also include metadata, such as the asset or process name, description, owner, location, status, dependencies, and configuration.

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Mohammad Salehi, PhD C.Eng

Data centre design and construction specialist at Stealth Startup
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Documenting the data centre assets starts at design stage. Having conversation with operation team right at 60% design stage to get them involved in the design, understand their requirements and their priorities would be very helpful to establish an appropriate procedure for asset documentation. If we get this right and develop it to the certain level, then this system would reduce required time for identifying the operation issues , finding a right spare part and leads to significant time saving when it comes to operation.

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3 Use a centralized repository

The third step is to use a centralized repository to store and manage the data center documentation. A centralized repository can help data engineers to access and update the documentation easily and securely. It can also enable version control, change tracking, and backup of the documentation. A centralized repository can be a cloud-based or on-premise platform that supports data center documentation standards and formats. Some examples of centralized repositories for data center documentation are Confluence, SharePoint, and DokuWiki.

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4 Automate the documentation generation and update

The fourth step is to automate the documentation generation and update as much as possible. Automation can help data engineers to save time and effort, as well as to ensure accuracy and consistency of the documentation. Automation can be achieved by using tools and scripts that can collect, process, and present data center information from various sources, such as inventory systems, configuration management databases, monitoring systems, and log files. Some examples of tools and scripts for data center documentation automation are Ansible, Puppet, Python, and PowerShell.

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Mohammad Salehi, PhD C.Eng

Data centre design and construction specialist at Stealth Startup
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In my experience this is not choice but a must that we need to adapt. Automation should start at design stage and carry on all the way to the project hand over and operation. The beauty of the automation software is to reduce the risk of human error and will cut down the design, operation and documentation time. This is all about how we can transfer our knowledge to the automation software ,test the process ,modify and improve it and reiterate the process to improve the quality.

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5 Review and validate the documentation regularly

The fifth step is to review and validate the documentation regularly. Reviewing and validating the documentation can help data engineers to identify and correct any errors, gaps, or inconsistencies in the documentation. It can also help them to keep the documentation up to date with the changes in the data center environment and requirements. Reviewing and validating the documentation can be done by using checklists, feedback, and testing. Data engineers should review and validate the documentation at least once a year, or whenever there is a major change in the data center.

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6 Share and use the documentation effectively

The sixth and final step is to share and use the documentation effectively. Sharing and using the documentation can help data engineers to leverage the data center knowledge for their data engineering tasks and projects. It can also help them to improve the data center performance and quality, as well as to resolve any issues or challenges. Sharing and using the documentation can be done by using channels and tools that can facilitate access, search, and collaboration, such as wikis, portals, dashboards, and reports. Data engineers should also follow the data center documentation policies and guidelines, and respect the data center documentation ownership and permissions.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most effective ways to document and manage data center knowledge?

1

2

3

4

5

6

7

1 Use a standard framework

2 Document the data center assets and processes

3 Use a centralized repository

4 Automate the documentation generation and update

5 Review and validate the documentation regularly

6 Share and use the documentation effectively

7 Here’s what else to consider

Data Engineering

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What are the most effective ways to document and manage data center knowledge?

1

2

3

4

5

6

7

1 Use a standard framework

2 Document the data center assets and processes

3 Use a centralized repository

4 Automate the documentation generation and update

5 Review and validate the documentation regularly

6 Share and use the documentation effectively

7 Here’s what else to consider

Data Engineering

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