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Creating Value from Metadata in Data Solutions
Creating Value from Metadata in Data Solutions
These days, there is so much data that data about data is the new trend. Metadata is just as important–if not more so–than gathering and storing data. As the key to providing necessary context, metadata is important for everyone from data analysts and engineers to executives and decision-makers.
Metadata-driven data solutions provide the following benefits:
- Tracking data from entry to exit, monitoring changes and updates including data extraction and/or transformation, changes in content or structure, and accountability throughout the process
- Ability to discover and analyze trends over time, such as surges, errors, growth, and shifting requirements inside a data storage solution
- Tracing relationships and data sources, from graphics to citations inside data to metadata tags on similar datasets for easy cross-referencing
- Controlling access to different levels of data by setting metadata rules to create a secure data environment
- Indexing existing data in a variety of ways to increase the searchability and efficacy of a data solution
Metadata and the ETL Process
Metadata-driven ETL is currently the most effective way to standardize and organize data as it comes in. Some metadata, such as time and day the ETL process ran, can be created and saved during the initial phase, though there are debates on how to take the next steps. The two main ways to input metadata are manually and automatically.
The benefits of inputting metadata manually include accuracy, relevancy, and reliability. By having a person input only the relevant data and quality check it, the metadata will be more targeted and useful. Unfortunately, this takes time and the quality is only as reliable as the person inputting the metadata.
Automatic input is much quicker and more consistent. Data sources that carry their own tags and metadata are easily processed, and specifics such as file name or size can be easily and correctly gathered. On the downside, automated systems do not deal with unusual situations well and cannot make judgment calls on the amount and quality of metadata to input.
A smart system, however, may use both simultaneously. Data can be processed, gathering the metadata, and any incoming files or data with issues or incomplete tags may be flagged for review. This allows the benefit of human oversight and experience while saving as much time as possible with automated information-getting.
The Metadata-driven Approach vs the Traditional Data Solution
One of the biggest issues in a data-rich environment is finding the correct piece of data. While data warehouses may contain indexes or structures, data lakes, for example, are much harder to find specific data in.
While in both cases the data is present and identical, the process of finding that data is a much different experience for the end user. In a traditional data solution, a user would have to manually search for the data, whereas in a metadata-driven solution a user could pull data based on specific criteria as broad as date-loaded or as specific as the original source of the data. This makes for a much easier and more efficient data sorting process.
It also allows for less technical people to use and retrieve the data they need without having to wait for new sorting functions to be added by a data engineer. By increasing the number of people capable of sorting data and the ease and speed of access, time-to-value is increased across the board.
Metadata also helps with integration of data. By tracking each data packet as it is loaded, the additional layer of data generated by that packet’s journey is tracked in real time. By monitoring this and flagging issues or no-standard pieces of data, problems with integration, sources, and inaccurate data can be caught early or avoided altogether. In a traditional approach, data accuracy is ensured by people, and data issues are monitored by hand.
By integrating metadata, data governance and security also becomes easier. By sorting the data more efficiently and siloing it earlier in the process, access and tracking become more effective for longer. A single packet can also be tracked from load to delivery, allowing for better visibility of an entire data process. Unlike with traditional data solutions, data can be sorted and filtered automatically to improve ease of access.
The Future of Metadata
Metadata is clearly a versatile and useful tool to increase the usefulness of any data solution. The ongoing issue is how to gather, store, and access the metadata once it is collected, as well as the challenges posed by industry-specific data. This is where different data solutions come in as each offers advantages and unique ways of managing metadata. However it is used, it is clear that metadata is the future of data and will only increase in importance as we head further into the digital frontier.
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