.jpg)
Previously seen as a highly technical subject which only an organization’s data scientists or IT staff would understand, metadata is increasingly seen as something which everyone in an enterprise should have a grasp of. Understanding metadata is fundamental to any data initiative and should be a cornerstone of best practice in all data-driven organizations. In this blog post, we explore
- what metadata is
- the benefits of using metadata
- why data literacy is crucial to unlocking the full potential of metadata
What is metadata?
Metadata can be defined as data that describes other data. It helps to organize data by providing information about a dataset or a single element of data – such as when it was created and by whom – making it easier to locate, use, and reuse.
Metadata is everywhere. It is fundamental to how social media platforms, streaming services, online retailers and search engines work. The ‘meta’ in metadata refers to the underlying, hidden logic behind data – the ‘behind the scenes’ information about the way data is created, stored and used.
What kind of information is found in metadata?
There are hundreds of options for data attributes which metadata might describe. The metadata of a website might include the title of the page, when it was created, and keywords describing the content of the site. The metadata of a song might include the name of the artist, the album it was originally from, and who owns the copyright.
However, the kind of information metadata contains can be narrowed down to six categories, or six kinds of questions, you can ask about your data:
What – What is the title of the dataset or element of data? What keywords are associated with it? What format is it in?
Where – Where was it created? What geographical area is associated with the data? What language is it in?
When – When was it created? What time period does it cover? When was it last updated?
Who – Who created the data? Who has edited or contributed to the data? Who is responsible for the maintenance of the data?
Why – Why was it created? What was the original context of the data?
How – How can it be used and credited? Are there any rights or publication guidelines associated with the data?
What are the uses of metadata?
Metadata enables users to:
- Easily locate resources and data
- Catalog and organize data effectively
- Put data in context
- See data in relation to other data
- Extract information from datasets
- Allow data from various sources to be used together
- Protect and maintain data
Used correctly, metadata can be an incredibly powerful ally. It allows us to zoom in – to take a closer look at very specific aspects of data. It also allows us to zoom out – to take a big picture look and see data in context with other data.
This twin ability – to look at the workings behind a single data-point and to give us a view of the big picture – is metadata’s superpower.
5 reasons why metadata management is crucial for data initiatives
More and more companies are transitioning to data-driven models and towards making business decisions based on data. According to McKinsey, that’s a decisive factor for organizational resilience in an ever-changing world.
Good metadata management is crucial to such transformations, and to any data initiative, in (at least!) these five ways:
1. Better organization of company data
Ensuring accurate metadata helps users save time when searching for data. Even the most carefully planned business strategies can fail if the right datasets have not been captured, or if strategies are based on outdated or irrelevant data.
2. Deeper understanding of company data
Metadata establishes the origin, the context and the quality of data. It allows users to ask questions about data like: what is the source of the data and can it be trusted? It is only with a deep understanding of metadata that users can get the most out of sophisticated BI and data analytics tools – essential for any data initiative.
3. Extraction of value across company datasets
Metadata allows users to draw conclusions and associations across a wide range of external and internal datasets to obtain high quality results.
4. Ability to locate errors and inconsistencies
Data, especially legacy datasets, can contain errors and inconsistencies. Metadata can help users locate errors to ensure that an organization's data is robust.
5. Extend longevity of datasets
Datasets have a limited lifecycle and can easily become irrelevant, outdated or lost. But when metadata is properly maintained and developed, rather than users frequently rebuilding datasets from scratch, existing datasets can be kept up to date and valuable for as long as possible, saving time and money.
Why metadata doesn’t make sense without data literacy
Data literacy can be defined as the ability to understand, evaluate and apply the structures behind data, including metadata. This allows a data literate person to collect and visualize data, and to explore and draw meaningful conclusions from data.
While understanding metadata is crucial to data-driven strategies, it is often overlooked, according to the Boston Consulting Group (BCG). When it comes to handling metadata without the requisite data-literacy skills, companies may come up against a number of common problems:
1. Lack of knowledge
When users lack knowledge about metadata, they will likely lack the confidence or the ability to make valuable conclusions from data. An organization might have the data it needs, the tool it needs to open or interpret that data, and information about the original dataset, but without a proper understanding of metadata – the data behind the data – a user might not be able to join the dots and draw the right conclusions.
2. Inconsistency of data
When individuals do not possess the right data literacy skills, they may apply different methods or standards when inputting metadata. The resulting data quality may be uneven, resulting in unintentional data silos or difficulty in using metadata to allow other users to work seamlessly across different data sources.
3. Knowledge loss
Several studies have shown that companies are finding it harder and harder to hire and retain data scientists. When data-scientists leave an organization, they may take their knowledge about company metadata with them. It makes much more sense to give metadata responsibility to the same people who are creating the data in the first place, rather than a single data scientist.
On the other hand, when data literacy is combined with increased use of metadata, employees will be able to:
- ask the right questions about data themselves
- ensure that metadata is consistent
- ensure that company metadata is available to all
- take the initiative to get the most out of company metadata.
The benefits of aligning a company’s data literacy strategy with their metadata strategy couldn’t be clearer. But how do organizations implement this strategy in reality?
Innovative software solutions enable companies to reduce knowledge silos and managing the risk of knowledge loss. The Lyntics Data Literacy Platform, for example, screens data and metadata across the whole organization to extract and visualize the underlying logic, relationships and information. By automatically evaluating work results, Lyntics bundles insights of metadata from different parts of the data landscape and makes information available for rapid evaluation.
💡 Do you want to learn more about strategic data literacy, metadata and how they will make organizations truly successful in the data-driven digital age? Download our latest white paper and get all the information you need: https://www.lyntics.com/ebook