Estimated to grow from USD 2.44 billion in 2017 to USD 5.66 billion by 2022, digital asset management (DAM) has become fundamental in the enterprise tech stack, especially considering the mammoth growth in digital assets and its management across industry verticals. The cornerstone of DAM systems is the metadata that helps identify and define various assets. However, metadata cannot only be limited to mapping every piece of data belonging to these digital assets.
To fully achieve the business benefits of metadata, the linkage between the data classification to the information taxonomy and policies enabled by the metadata in the DAM system is equally vital. When the metadata is managed with both business and technical points of views, organizations no longer need to begin from the scratch during each new data initiative. Effective metadata practices facilitates better efficiency, process transparency, and improved speed across enterprise functions. This also sets the path for the business to accrue bigger benefits like faster time-to-market, brand consistency and governance, improved customer experiences, and revenue generation.
Without metadata, the purpose of a DAM system is almost lost. From understanding if an image can be used in a particular location to complying with any restrictions on a piece of content, metadata travels with the asset, making it easier to ensure assets are not abused.
Let’s take a detailed look at what metadata management can enable in a DAM system.
1. Asset search and identification
Metadata in a DAM system sets the context and provides information about digital assets such as videos, sales collaterals, images, etc., and can include standards and models, as well as tactical elements such as controlled vocabularies and taxonomies. Tagging these assets with just a few basic metadata fields like title, licensing information, and keywords can help in searching and identifying the asset location from the library. Moreover, by tagging underlying metadata, data scientists can analyze the same to draw insights on how to navigate and make further decisions on said datasets.
2. Media library creation
A modern DAM system allows organizations to create custom meta tags and fields, enabling employees across the organization to group up and access content fragments by creating a single media library. This includes adding and editing values in the metadata fields, adding and deleting static renditions, creating asset versions, performing review tasks on assets, annotating assets, and much more. Moreover, a media library is made searchable across different departments through metadata and saves time when identifying or adding new datasets.
3. Digital rights management
Metadata enforces transparency and fairness in organizing digital rights management by addressing intellectual property rights, privacy, licensing, and confidentiality issues; it is done in a manner in which it renders information or makes it accessible over a variety of platforms. Additionally, all asset records in the collective library includes metadata fields to capture copyright infringements. This increases reliability in various versions of datasets by keeping necessary files up-to-date while tracking copyright information.
4. Metadata standardization
As the reuse of existing metadata is essential in achieving interoperability among metadata sets, a well-structured metadata development schema promotes the wider adoption, standardization, and interoperability by facilitating the discovery and re-labelling of datasets across diverse disciplines and communities of practice. Also, metadata ensures that any sensitive data is automatically tagged or flagged with capturing of the flow to facilitate compliance with regulatory requirements, thereby ensuring standardization in the formats that are pushed out to external stakeholders, customers or internally within the organization.
5. Versioning
Since metadata plays an important role in structuring large amounts of files and content, it is essential to include metadata in the versioning of documents as it best describes and records elements like document approval, content descriptions, any stakeholder decisions made over the content, errors avoided previously to make it easier for employees to keep track of asset multiplication and practice version control. Additionally, multiple users can make changes to their data definitions and other assets while avoiding potential conflicts with the main state of the data version.
Every modern-day DAM solution offers the above metadata management capabilities to categorize, store, and search for content across multiple data repositories. It also saves time in checking data compliance, enabling business users to actually use their content by establishing relevance of their metadata via visual guidelines, regulations, brand image, and more. Furthermore, it allows a more in-depth study of distribution and usage of data by enriching the reach or engagement statistics of visual and audiovisual content using information like the type of product, product range, media title, tag, brand, subsidiary, and so on. Ranging from descriptive, structural, administrative to generated, the proper management of metadata is key to improving production costs, streamlining creative collaboration, and better distribution of marketing content.
Implementing a holistic metadata schema to better understand datasets and unlock their value
The success of any DAM-related strategy heavily depends on a holistic metadata schema. It not only increases the return on investment (ROI) of a content system by unlocking the ability to ingest, discover, and share or distribute assets, but also performs unique functions such as file preservation and archiving, accessing and searching, file manipulation, and publishing. This further builds the foundation for a profitable DAM strategy wherein enterprises can categorize, store, and search for content in a governed manner whilst contributing to expanding business needs.
A successful metadata management strategy in a DAM Solution needs to incorporate metadata integration and publication, capture and storage, and governance to ensure there’s an overall consistency in an organization’s entire data ecosystem. Moreover, during the collection of metadata from datasets, identifying all internal and external sources can be achieved via metadata repositories, data modeling, and standardization tools. By ensuring the right metadata governance structure, a business can better understand and unlock the potential value of its datasets with a review of the responsibility, life cycles, and statistics of metadata and how different the metadata integration is across the organizational processes.
Author Bio:
Rakesh Kumar is Chief Technology Officer (CTO) at Pimcore Global Services (A Happiest Minds Company). He is responsible for designing high-performing and scalable cloud applications and managing engineering and pre-sale functions across geographies. Pimcore is an open-source platform for product information management (PIM/MDM), digital asset management (DAM), content management system (CMS), and eCommerce.