If a business considers improving its data center documentation, new, more comprehensive, and holistic documentation systems, which are to replace existing resources, often stand in the foreground of such considerations. The focus often lies on the introduction of a new software. What is often neglected is that the system, in which infrastructure data are stored, is only one of three steps towards guaranteeing high-quality data center documentation.
If we take a look at the documentation of a data center's infrastructure we can define three major criteria for data documentation: the structure or the systems, in which the data are stored, the availability of these systems, and thus the availability of the data therein, and the quality of the data as such.
Structure
Structure can be defined as the system in which infrastructure data are kept and organized. The range of such possible systems spans from the human brain of a staff member to a holistic system, such as a CMDB or a Data Center Infrastructure Management (DCIM) tool. Between these two poles we find an array of different systems, depending on how well the data are structured within these systems.
Improving structure includes, for instance, writing down "ad-hoc" information provided by staff, digitalizing written records, feeding unstructured information into a spreadsheet or a database, integrating databases with different structures, or migrating all data into a holistic system and replacing other existing sources of documentation.
Depending on a company's core business and the size of its data center, every company will find a different answer to the question of what is the most adequate system. No doubt, not every business will need a DCIM tool or a CMDB to document its datacenter infrastructure. In many cases an integrated documentation system will be sufficient.
Availability
The availability of the desired data is a factor that is often neglected when companies consider improving their documentation system. Of what use are expert systems, for instance, if only one staff member can handle them? Particularly as these staff members are often overburdened with other tasks (e.g. day-to-day routines) and cannot provide urgently needed data on time. And how useful are these systems if only one division is responsible for maintaining these resources and no one else can retrieve information from them?
Improving availability means making data more accessible, no matter which division maintains the databases and whether those staff members, who handle the systems, have enough time resources.
High-price DCIM systems offer an easy-to-use web-based surface ("management tool") for standardized data retrievals. Companies, which need less complex solutions, will need to pay special attention to the availability of their documentation systems.
Quality
Interestingly, data quality is often neglected when it comes to improving data center documentation systems. It is often assumed that a new, modern system will also enhance and guarantee data quality.
This is not true, of course, or only to a very limited degree. If a company plans to migrate all available data sources into a new comprehensive system to replace existing isolated documentation islands it is absolutely necessary to validate all available data with respect to their quality (correctness, completeness, accuracy, up-to-dateness, lack of redundancies, consistency, unambiguity, ...) before migrating them into a new system . If this process of data validation is left to the new system and thus transferred into the migration phase this may entail severe problems for the implementation of the new system - problems which can be avoided if a business opts for a proactive approach.
Some companies, which are aware of their lack of proper infrastructure documentation, plan to take stock before the data migration phase. If we leave aside the fact that stocktaking is sometimes not feasible due to lack of money or time, is also can only solve part of the problem. By doing an inventory you can only record what you see. These new data need to be linked with existing data on power supply or network connectivity - before the implementation of a new software.
Conclusion
All systems and their availability build on the quality of the data therein. It is irrelevant how long I have to wait for accessing false information or how well incomplete data are structured and stored within these systems. What is important is to guarantee that all data are correct, complete, up-to-date, and accessible. These major criteria of data quality are system-independent and can also be guaranteed and validated with distributed data storage systems as long as certain criteria of documentation are fulfilled.
yandree GmbH specializes in data center documentation and offers different tools and services which are necessary and useful for providing an optimal documentation of a company's infrastructure data. Data quality has always been of special concern to yandree.
If we take a look at the documentation of a data center's infrastructure we can define three major criteria for data documentation: the structure or the systems, in which the data are stored, the availability of these systems, and thus the availability of the data therein, and the quality of the data as such.
Structure
Structure can be defined as the system in which infrastructure data are kept and organized. The range of such possible systems spans from the human brain of a staff member to a holistic system, such as a CMDB or a Data Center Infrastructure Management (DCIM) tool. Between these two poles we find an array of different systems, depending on how well the data are structured within these systems.
Improving structure includes, for instance, writing down "ad-hoc" information provided by staff, digitalizing written records, feeding unstructured information into a spreadsheet or a database, integrating databases with different structures, or migrating all data into a holistic system and replacing other existing sources of documentation.
Depending on a company's core business and the size of its data center, every company will find a different answer to the question of what is the most adequate system. No doubt, not every business will need a DCIM tool or a CMDB to document its datacenter infrastructure. In many cases an integrated documentation system will be sufficient.
Availability
The availability of the desired data is a factor that is often neglected when companies consider improving their documentation system. Of what use are expert systems, for instance, if only one staff member can handle them? Particularly as these staff members are often overburdened with other tasks (e.g. day-to-day routines) and cannot provide urgently needed data on time. And how useful are these systems if only one division is responsible for maintaining these resources and no one else can retrieve information from them?
Improving availability means making data more accessible, no matter which division maintains the databases and whether those staff members, who handle the systems, have enough time resources.
High-price DCIM systems offer an easy-to-use web-based surface ("management tool") for standardized data retrievals. Companies, which need less complex solutions, will need to pay special attention to the availability of their documentation systems.
Quality
Interestingly, data quality is often neglected when it comes to improving data center documentation systems. It is often assumed that a new, modern system will also enhance and guarantee data quality.
This is not true, of course, or only to a very limited degree. If a company plans to migrate all available data sources into a new comprehensive system to replace existing isolated documentation islands it is absolutely necessary to validate all available data with respect to their quality (correctness, completeness, accuracy, up-to-dateness, lack of redundancies, consistency, unambiguity, ...) before migrating them into a new system . If this process of data validation is left to the new system and thus transferred into the migration phase this may entail severe problems for the implementation of the new system - problems which can be avoided if a business opts for a proactive approach.
Some companies, which are aware of their lack of proper infrastructure documentation, plan to take stock before the data migration phase. If we leave aside the fact that stocktaking is sometimes not feasible due to lack of money or time, is also can only solve part of the problem. By doing an inventory you can only record what you see. These new data need to be linked with existing data on power supply or network connectivity - before the implementation of a new software.
Conclusion
All systems and their availability build on the quality of the data therein. It is irrelevant how long I have to wait for accessing false information or how well incomplete data are structured and stored within these systems. What is important is to guarantee that all data are correct, complete, up-to-date, and accessible. These major criteria of data quality are system-independent and can also be guaranteed and validated with distributed data storage systems as long as certain criteria of documentation are fulfilled.
yandree GmbH specializes in data center documentation and offers different tools and services which are necessary and useful for providing an optimal documentation of a company's infrastructure data. Data quality has always been of special concern to yandree.
