Companies should view data as a type of currency. This attitude lends itself to crowdsourcing information about your data. While it may be viewed as more high-risk than top-down governance and therefore more democratic , it is certainly more high-benefit than the low-risk, low-yield approach of discouraging or partitioning community commentary.
Clearly define each field, and who owns it the BI team, a specific analyst, etc. Ideally, hovering over the field provides not only a definition but also a contact link as well. Be sure to publish the data dictionary as a document easily accessible by anyone within the organization. Describe the data lifecycle, from where it originates to the steps it goes through before arriving at the data warehouse. Keep in mind there is more metadata about the data than there is data itself.
End users should easily comprehend where data values come from. Anything that stands out as unusual must be recognized and recorded. Nuances or aberrations in the movement, characteristics, or quality of the data should be logged in as such, with a record kept within the data dictionary.
Over time, fields will change. Some will be added while others dropped, and certain fields will see new definitions ascribed to them. Also, data corruption can occur with aging storage devices, sudden drops or spikes in power, etc. Logging each change in the data over time helps ensure data consistency. Describe how fields have been populated in the past, with example queries revealing why a data dictionary should be a prioritized component of general business processes.
Analytics Stack Guide. Why Data Definition is Essential As mentioned above, and in our overview of data modeling, database tables without definitions are often counterintuitive at best.
Data Dictionary Roles and Responsibilities Define Ownership When developing an organization-wide data dictionary, integrate common data elements across the entire institution to ensure consistency, as consistency reinforces the objective: quality data interpretation. So how is the data dictionary best created, both organizationally and practically? Without a Data Team And what to do in the absence of a data team? A good three-pronged method for initiating the adoption of a data dictionary from the ground up might be: Build a prototype: This could be as simple as a Google spreadsheet that lists all the fields for the most important reporting tables, including the Minimum Viable Data Dictionary elements listed below.
Data Dictionary Maintenance With metrics in place, clearly defined, and ready to track performance, the data dictionary is off and running. What does the metric measure or the dimension describe? How is the data collected? What instrumentation is used? Are the values calculated or raw? What is the calculation or bucketing logic? Who owns oversees data collection and quality? An individual or team? What is the contact information for the data owner s? The Key Elements of a Data Dictionary A Data Dictionary provides information about each attribute, also referred to as fields, of a data model.
Attribute Name — A unique identifier, typically expressed in business language, that labels each attribute. Attribute Type — Defines what type of data is allowable in a field. Example of a Data Dictionary You are probably wondering how all of this comes together. Before you go, would you like to receive our absolutely FREE workshop? No formal experience required. It describes the meanings and purposes of data elements within the context of a project, and provides guidance on interpretation, accepted meanings and representation.
A Data Dictionary also provides metadata about data elements. The metadata included in a Data Dictionary can assist in defining the scope and characteristics of data elements, as well the rules for their usage and application.
Data Standards are rules that govern the way data are collected, recorded, and represented. Standards provide a commonly understood reference for the interpretation and use of data sets. By using standards, researchers in the same disciplines will know that the way their data are being collected and described will be the same across different projects.
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