Data means nothing if you can’t properly analyze it. According to big data studies, 95% of organizations believe that their data can yield insight, even though over 30% additionally believe that their data is inaccurate. If you want to get the most out of your data, you need to have proper data modeling techniques. Modeling techniques control how the raw data is actually visualized so that the appropriate conclusions can be reached.
Data mapping is used to integrate multiple sets of data into a single system. Data mapping describes relationships and correlations between two sets of data so that one can fit into the other. Data is then usually migrated from one area to another; an additional data set, for instance, may be brought into a source data set either to update it or to add entirely new information. The setup process is critical in data mapping; if the data isn’t mapped correctly, the end result will be a single set of data that is entirely incorrect.
Relationship modeling consists of an Entity Relationship Diagram, which is utilized to show how elements of data are related to each other. Relationship modeling is commonly used as a broad overview of the database’s structure rather than to clean information from specific data sets. Throughout the relationship modeling structure are three components: entities, relationships, and attributes. These types of diagrams are commonly used not only to analyze a database but also to initially construct a meaningful database. Relationship modeling will often come into play when trying to analyze the structure of your data rather than the data itself.
A data dictionary matrix is utilized to fully outline what each item of data means. Dictionary defining essentially exists one level of abstraction down from relationship modeling. Once you have the relationships modeled, you can look at each point of data and figure out what it consists of and what it does. As an example, a database may have a “Customer Name” as one data type. The dictionary would define “Customer Name” as a required item of plain text that can be up to 50 characters in length. Defining the dictionary in this way is essential for creating standardized data sets that can be later analyzed. For instance, if the Customer Name was set as “HTML” instead of “Plain Text” you could find that the exact same customer was entered in multiple times with different formatting in their name.
Glossaries are used as a form of documentation to describe and define the entire database model. A glossary is often neglected during data modeling because it is assumed that those working on the data models will already be familiar with them or will figure them out. This is a classic mistake and can have widespread ramifications: should a team member leave and be replaced, it could open the door for inconsistent and useless data.
Of course, data modeling techniques cannot draw conclusions from inaccurate data sets. In addition to modeling your data correctly, you also need to make sure that you’re sanitizing your data input and collecting the data that you truly need. Some data sets are simply too big to be properly modeled without significant data mining. These large data sets may need different modeling strategies altogether.