5 Mistakes in Metadata Tagging

benefits of storageX

With the increasing number of files and growth of your unstructured data, gaining business insights through deep analytics, artificial intelligence, and machine learning are only becoming more complex. The value of these files is trapped, and as you generate more each day, they become more expensive to store and support every year.

As initiatives for effective data management solutions have been developing across all enterprises, the value of metadata tagging is also becoming a top priority amongst CIOs. With custom metadata tagging, you can expedite access to your files and maximize your investment by uncovering the value found within your data.

Here are the top 5 metadata tagging mistakes you should avoid.



metadata tagging

Standard metadata provides information that is enough for a surface level understanding of your data. In order to go deeper, custom tags are required to enrich your heterogeneous data. Custom metadata tagging resolves storage problems by turning old and historic data into valuable and useable information.

When the time comes to create reports and perform deep analysis, you can use your personally identifiable information (PII) to search all enterprise data assets and run semantic classification for data discovery. For this reason, the custom tagging functionality enables you to easily search, discover, and understand your data relationships to get the most value from your data.



Auto-tagging your data allows flexibility by deciding which data sets should be tagged with personalized information. Custom auto-tagging can be done through highly scalable scanning engines based on industry standards of NFS and SMB. APIs are used to scan and collect data sets which are then tagged with your customized information to support deep analysis using artificial intelligence and machine learning. When auto-tagging your data, you establish a glossary of all your data assets. This creates the foundation for effective data governance which results in efficient data management and valuable business insights.



Knowing what you have is a great start and adding tags to your data encourages efficiency, but the real value of custom tagging is how it enables your business analytics. After you have tagged and moved your data into Amazon S3 for example, it improves the quality of how you search for files across your systems and data lake. Once you have segregated the required data using the tagged policies, you can then run analytics on Amazon Athena to gain valuable business insights based on your enterprise needs and goals. For that reason, your tags should be conducive for analytics to gain the full potential from your data.



Custom metadata tagging enables automated and customized file categorization. By accelerating the mapping of tags to logical data sets, this functionality allows you to classify data according to your enterprise-specific categories. For instance, collections can be associated with a certain project or they can be classified into each line of business such as sales, marketing, or finance.

As a result, it becomes easy and efficient to run analytics on a collection to determine their capacity and the number of resources consumed. Based on the tags for each collection of data, we can analyze the project’s performance or even the output from each enterprise department.



We have established that custom tags are a significant part of any data management strategy. The main value of tags is that they are a gateway for data analysis and business insights which delivers the biggest return on investment. Ensuring that your tags are portable so that they can run on any platform is key for data analysis. Athena, Kibana, and StorageX are a few platforms utilized for various types of reporting through metadata tags.

Each platform should be able to understand and process your tags for you to gain the insights from your data analysis. Since there are numerous platforms available to extract insights from your data, it is important to use portable tags in your data management strategy for compatibility and flexibility when utilizing various software platforms for deep data analytics.



Without metadata and custom tags, enterprises cannot extract the deep insights that big data offers. By streamlining the process to collect, integrate, and analyze big data sources, custom metadata tagging empowers enterprises to achieve greater returns by leveraging their data. Since it is an essential part of the data management strategy, Data Dynamics wants to ensure that you are successful when it comes to your metadata tags.

If you want to start your digital transformation today or have more questions, we would love to help. Request a call today and one of our experts will contact you as soon as possible. Or even better, request a demo and see it all in action.


Data Dynamics is a leader in intelligent file management solutions that empower enterprises to seamlessly analyze, move, manage and modernize critical data across hybrid, cloud and object-based storage infrastructures for true business transformation. Its award-winning StorageX platform eliminates vendor lock-in and provides a policy-based, storage management platform to provide the insight, agility, and operational efficiency to transform your data assets into a competitive advantage.

Used today by 24 of the top Fortune 100 companies, StorageX has optimized more than 160 PB of storage, saving more than 80 years in project time and $80 million in total storage costs.

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