Entity Relationship Diagram (ERD) Final Project

How Data Lineage will Impact the Entity Relationship Diagram (ERD)

Student’s Name

Institutional Affiliation

How Data Lineage will Impact the Entity Relationship Diagram (ERD)

Review of each ERD Element

The main elements of the entity relationship diagram include: employee, customer, product, order, order details, and supplier. Supplier refers to the entity that reflects the suppliers of the commodities bought by the consumers. Order, on the other hand, refers to the entity that deals with the order placed by consumers by an employee. Order details reflect the entity that is also representative of the association between orders and products. Further, product refers to an entity representing the suppliers of the products purchased by consumers.

How the data hierarchies will impact the Master Data Management (MDM) solution

There are various ways in which data hierarchies will exert influence on the Master Data Management (MDM) solution. As indicated in the ERD, one of the most significant components of the hierarchy management basically emphasizes on the lineage and procedures of addressing multiple records within a single representation (Loshin, 2010). Since there are records that represent the unique entity within different application systems, as components of the consolidation, it will be important to document the application data sources that contribute to the master consolidation, in some parts of MDM architectures, to offer links back from the master index to the original source records. This plays an important role in materializing the master information on demand. This is a significant tool during data control in the event that it is determined that there are false matches, where identification of information for two individual objects that are incorrectly resolved into a single entry, or false negatives in which there are more than one master record for the same unique entity.

From the above discussion, hierarchy management is more concerned with data lineage as a tool to avoid the inevitable occurrence of errors within the data integration processing streams. Another element of hierarchy management for MDM is related to the interconnectedness of master objects across multiple streams. For instance, in the above ERD, customers may be related to each other in terms of belonging to the same family, place of work, or similar business. In addition, different master data forms may be related (Loshin, 2010). For instance, the ERD above suggests that there are certain products that may be from one supplier. Thus, such relationships are represented in linkage hierarchies, as well as the hierarchy management layers that also offer service components that support the management of such connections.

How Data Quality would be Measured

There are various methodologies that are going to be utilized to measure data quality. These metrics will serve as important tools in providing some quantitative objective measures of the challenges that may emerge. Two data quality dimensions can be employed. They include consistency and timeliness. In order to explore the metrics, various requirements are needed. They include normalization, cardinality, and adaptivity. Timeliness may also include data currency. Data currency is related to how prompt data is updated. Volatility is also a quality metric that inherently characterizes certain forms of data (Batini&Scannapieco, 2016). A measure of data volatility is weighed in terms of the length of time and its impacts that a given data remains valid. Timeliness suggests that information should not only be current, but should also in time. In view of the above, one effective measurement is comprised of currency measurement and a check that data is present before the planned usageand time. Apart from timeliness, interpretability can be utilized as a quality measure. To maximize its interpretability various documentation forms need to be made available (Batini, Rula, Scannapieco&Viscusi, 2015). These include the conceptual schema of the files or databases availed; the integrity constraints that hold among data, and a collection of metadata for cross-domain information resource descriptions.

References

Batini, C., &Scannapieco, M. (2016).Data and Information Quality: Dimensions, Principles

and Techniques. New York: Springer.

Batini, C., Rula, A., Scannapieco, M., &Viscusi, G. (2015). From data quality to big data

quality. Journal of Database Management, 26(1), 60-82.

Loshin, D. (2010). Master data management.New York: Morgan Kaufmann.