Each section has a certain length of film running time, and those sections fit together into the format in a certain order. In a more broader sense, structural metadata records information about how a particular object or resource might be sorted. In the above example of the DVD, structural metadata would inform users of the correct placement of these sections on the disc.
This information may include vital details required for a system to communicate or interact with a specific file. This brings forth common factors important to preservation and maintenance, including information that shows actions taken on a digital file or the rights attached to it. Now, for physical objects this is much less important, as we do not duplicate these as often. However, in the digital realm, this happens all the time.
Things provenance metadata might touch on are the companies or users who impacted a digital object and what types of things they did to it or methodologies they used. Use metadata is data that is sorted each time a user accesses and uses a specific digital piece of data.
To demonstrate, consider a fictional bookstore that records their sales in a software system. This information can reveal telling patterns. They might also make sales more often in the morning and daytime than at night. They could use this type of information to rearrange their store based on these data and encourage the patterns they find out about specific book sales. Administrative metadata informs users what types of instructions, rules and restrictions are placed on a file.
Share on: LinkedIn Twitter Facebook. Defining diverging metadata perspectives Metadata provides a comprehensive understanding of where data resides in an organization and how it is deployed. Physical metadata covers the specifics of: within which system data resides, the schema, table, and column or key-value level of detail. This information is machine generated and automatically pulled from software systems. Logical metadata provides details on how data is linked together to form larger sets.
It also outlines how data flows through systems and processes, from creation, to storage, transformation, and consumption.
It provides critical information about the data usage, including the acquired knowledge of subject matter experts within the organization. This type of metadata is derived from people.
As a result, it is the most difficult type of metadata to collect and update because it requires human intervention and management processes to continuously refresh. Read our eBook Why Metadata Management is an Essential Element of Data Governance To learn more about how metadata is used and why it is key to supporting data governance initiatives read our eBook. Why Metadata Management is an Essential Element of Data Governance To learn more about how metadata is used and why it is key to supporting data governance initiatives read our eBook.
Related posts. Why Your Master Data Management Needs Data Governance In recent years there has been a growing awareness among organizations around their data and the role it plays in the success or failure of their most critical business functions.
Precisely Editor Data Quality November 8, Let's Talk Get in touch. This site uses cookies to offer you a better browsing experience. Not only is the object lost but even if recovered it has lost its provenance or meaning! This anecdote hopefully starts to form an idea that data on the data is as important as the data itself. Without having context, data has little reuse value. Using the context of my job as an archaeologist, an object loses its scientific value if it loses its provenance or metadata.
Every artifact is bagged and tagged using a numerical reference on the bag that corresponds to notes in a log. Often there are photos and sketches made of the artifact in-situ in its original state for future research. Archaeology is not about treasure hunting. Both endeavors are fun and exciting. But the useful side of both open data and Archaeology is about the amount of reuse we can derive from our objects whether they be stones and bones or massive datasets.
Now that we have a more basic answer to our original question "what is metadata", let's take a look at what others have had to say. I use two definitions as a reference: one from the International Standards Organization ISO , the other from White House Roundtables that I attended both on Data Quality and on open data for Public-Private Collaboration , as we co-constructed a definition in the presence of experts.
First, provenance in the White House context is defined as the metadata of a dataset. The second difference is that there is no "timeliness" dimension to the ISO definition of Data Quality. The ISO predates the widespread adoption of open data. Perhaps timeliness will become a part of the ISO in the future.
To make this easier to discuss, we will conflate the definitions of provenance and semantics into a third term called metadata. According to Liu and Ram's " A semiotic Framework for Analyzing Data Provenance Research ", the word provenance used in the context of data has different meanings for different people.
Liu and Ram go on to define the semantic model of provenance in this and several other works as a seven piece conceptual model. Liu and Ram conceptualize data provenance as consisting of seven interconnected elements including what, when, where, who, how, which, and why.
These are elements of several metadata frameworks. Basically, most metadata schemas ask these elements about their data. So, if we conflate these two terms into metadata, we are saying that metadata gives the following information about the data it models or represents:. Opendatasoft natively uses a subset of DCAT to describe datasets. The following metadata is available:. The creation of a fully custom metadata template can also be done.
A lot of the discussions around data quality and data discoverability have revolved around metadata and something called ontologies. Ontologies are descriptions and definitions of relationships. Ontologies help us to understand the relationship between things. As an example, an "android phone" is a subject of an object class, "cell phone". Some refer to an "ontology spectrum" that describes some frameworks as weak and others as strong.
This "spectrum" encapsulates the range of opinions as to what an ontology really is. Imagine we have a dataset about building permits.
We may want to compare the nature of our dataset of permits with another dataset of permits. See Core Permits Requirements.
If our dataset matched the schemas of those 9 municipalities, then we can say they would interoperate. We still need to add some discoverable metadata around them.
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