We now come to an interesting concept and an essential tool used in large-scale organizations having scores of software applications and thousands of employees. This new topic of our study is the concept of a data dictionary. You have by this time appreciated that for both - software developers as well as software users - there are umpteen numbers of data files that are in use. Whenever some new data piece (either a data file or even a field) is to be added, the data dictionary comes in handy.
Data dictionary is a compilation of all the data elements or data fields that are in use. You can visualize it as a two dimensional matrix with data fields occupying one row. In our example, all data fields across all the files, viz. employee number, employee name, department code, department name, qualification code, etc. would each occupy a row. Please also note that fields like department code appear in multiple files (remember this department code appears in the department file as a primary key and again in the employee file as a foreign key?).
Coming to the columns, these columns would describe the properties or details of the concerned field, viz. the class (numeric, alphabetic) width, significance or sematic behind it, the expected values and related validation rules for it, the date when it was brought in the design, etc. perhaps the most important columns would be the last two that tell us:
(i) The file(s) in which this data field appears, and
(ii) Whether it is a primary key, foreign key or just an ordinary/non-key field in that file.
Points to remember about the data dictionary:
Neat: the compliation has to be maintained in a structured manner to reflect class, size, probable values, relevance and history of each data field being used in the company across all info-tech applications.
Systematic: the maintenance of eh data dictionary is so crucial, complex and high-volume that specially designed software is invariably used as an automated tool to add, change, delete in data dictionary.
Concise: if the size of the data dictionary gets unwidely, people tend to overlook it and not use it. Hence the extent/volume of the data dictionary must be kept manageable, and yet without missing out on any of the data fields.
Shareable: since many people from the workface may need to look at the data dictionary, it has to be maintained in a shareable manner.
Controlled: through the data dictionary is available to all employees, access to it for modifications should be confirmed to a privileged few employees who are assigned the task of its maintenance.
Whenever some new data piece is to be added, changed or removed, one has to ensure several points.
Firstly the desired data must not already be in existence, or else adding it would cause duplication.
Secondly, there should not be any other field that runs parallel and similar to the desired one. In that case, merely modifying the existing field to suit the needs is preferred over adding it afresh.
Thirdly, whenever a new field is added, it has to be in synchronization with the other related fields and files, so that the data maintains its integrity even after future updates. For such reasons, a neat track of all the existing data files and fields by way of the data dictionary is inevitable, particularly for those large organizations where tracking of all these is not a compact task to be left for manual work and errors.
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