In our data flow diagrams, we give names to data flows, processes and data stores. Although the names are descriptive of the data, they do not give details. So following the DFD our interest is to build some structured pace to keep details of the contents of data flows, processes and data store. A data dictionary is a structured repository of data.It is a set of rigorous definitions of all DFD data elements and data structure.
A data dictionary has many advantages. The most obvious is documentation: it is a valuable reference in any organization. Another advantage is improving analyst/ user
communication by establishing consistent definitions of various elements, terms and procedures. During implementation, it serves as a common base against which programmers who are working on the system compare their data descriptions. Also control information maintained for each data element is cross- referenced in the data dictionary. For example, programs that use a given data element are cross- referenced in a data dictionary, which makes it easy to identify them and make any necessary changes. Finally a data dictionary is an important step in building a database. Most data base management systems have a data dictionary as a standard feature.
Data have been described in different ways. For example, in tape and disk processing, IBM called a file data set. In data base technology, the term file took on a different meaning IBM’s information Management
FIGURE Project Data Element Form – A Sample PROJECT DATA ELEMENT SHEET
PROJECT NAME __________________________ DATE_______________
DAT A ELE ME NT DES CRI PTI ON DATA ELEMENT ABBREVATION ELEMENT PICTURE ELEMENT LOCATION ELEMENT SOURCE
System’s (IMS) manual defines data as fields divided into segments, which, in turn, are combined into databases. The Conference on Data System Languages (CODASYL) defines data as data items combined into aggregates, which, in turn are
combined into records. A group of related records is referred to as a set. If we choose words that represent the general thinking of common vocabulary. There are three classes of items to be defined:
1. Data element: The smallest unit of data that provides for no further decomposition. For example, “ data” consists of day, months and year. They hand together for all practical purposes.
2. Data structure: a group of data elements handled as a unit. For example, “ phone” is a data structure consisting of four data elements: Area code- exchange – number –extension- for example, 804-924-3423-236. “BOOK DETAILS” is a data structure consisting of the data elements author name, title, ISBN (International Standard Book Number), LOCN (Library of Congress Number ), publisher’s name and quantity.
3. Data flows and data stores.As defined earlier, data flows are data structures in motion, whereas data stores are data structures at rest. A data store is a location where data structures are temporarily located.
6.4.2.1 Describing Data Elements
The description of a data element should include the name, description and an alias (synonym). For example:
AUTHOR NAME –first WHISKEY – name - middle - distiller - last - vintage - alias
The description should be a summary of the data element. It may include an example. We may also want to include whether or not the data elements(s) has:
1. A different name. For example a PURCHASE ORDER may exist as PUR.ORDER, PUCHASE ORD., or P.O. We want to record all these in the data dictionary and include them under the PUCHASE ORDER definition and separately with entires of their own. One example is “P.O. alias of (or see also)
PUCHASE ORDER.” Then we look up PUCHASE ORDER to find the details. It is an index.
2. Usage characteristics, such as a range of values or the frequency of use or both. A value is a code that represents a meaning. Here we have two types of data elements:
a. Those that take a value within a range: for example, a payroll check amount between $ 1and $10,000 is called continuous value.
b. Those that have a specific value: for example. Departments in a firm may be coded 100 (accounting), 110 (personnel), etc. In a data dictionary, it is described as follows:
100 means “Accounting Department” 101 means “ Accounts Receivable Section” 102 means “ Accounts Payable Section” 108 means “ General Ledger Section”
3. Control information such as the source, date of origin, users, or access authorizations.
4. Physical location in terms of a record, file or data base. 6.4.2.2 Describing Data Structures
We describe any data structure by specifying the name of each data structure and the elements it represents, provided they are defined else- where in the data dictionary. Some elements are mandatory, whereas others are optional. To illustrate, let us take “BOOK- DETAILS”. The data elements of this data structure are as follows:
6.4.2.3 Describing Data Flows and Data Stores
The contents of a data flow may be described by the name (s) of the data structures(s) that passes along it. In our earlier example, BOOK-DETAILS express the content of the data flow that lead to process 4. Additionally, we may specify the source of the date flow, the destination, and the volume (if any). Using the BOOK- ORDER example, data flows may be described as follows:
Data Flow Comments
BOOK-DETAILS From Newcomb Hall Bookstore (source) AUTHOR –NAME
TITLE OF BOOK
EDITION Recent edition required
QUANTITY Minimum 40 copies
A data store is described by the data structures found in it and the data flows that feed it or are extracted from it. For example, the date store BOOK STORE- ORDER is described by the following contents:
Comments
ORDER Data flow/data structure feeding date store ORDER-NUMBER
CUSTOMER –DETAILS Content of data store
BOOK- DETAIL Data flow/data structure extracted from data store
This step is the logical description. We want to specify the inputs and outputs for the process and summarize the logic of the system. In constructing a data dictionary, the analyst should consider the following points:
1. Each unique data flow in the DFD must have one data dictionary entry. There is also a data dictionary entry for each data store and process.
2. Definitions must be readily accessible by name.
3. There should be no redundancy or unnecessary definitions in the data definition. It must also be simple to make updates.
4. The procedure for writing definitions should be straightforward but specific. There should be only one way of defining words.
In summary a data dictionary is an integral component of the structured specification. Without a data dictionary, the DFD lacks rigor, and without the DFD, the data dictionary is of no use. Therefore, the correlation between the two is important.