Amplify your audio data management capabilities
Make no mistake, the world of work is experiencing a boom in audio content. In addition to the widespread use of workplace video and audio conferencing and collaboration tools, the volume and range of audio assets that organizations now generate and distribute is expanding. As a consequence, audio is playing an increasingly central role in the business communications mix.
Today’s IT professionals now need to manage an expanding array of audio content; everything from corporate podcasts to AI-driven customer training and sales interactions that are delivered via apps or the web. To harness and gain maximum value from this increasingly valuable resource, IT leaders will need to master every aspect of the art of efficient audio data management. By optimizing the organization and storage of audio data, they’ll not only be able to boost its accessibility and discovery so that teams can quickly leverage the assets they need. They will also be able to initiate streamlined workflows that enhance productivity and uncover and unlock the inherent value contained within audio data. Let’s take a look at just some of the ways IT leaders can reinvent their audio data management capabilities.
Rethinking storage with productivity in mind
Optimizing the utilization of audio data depends upon enabling the rapid and easy file access that accelerates the content creation process for creative teams. Designed with the demands of fast-moving files in VFX production and video editing in mind, modern storage solutions built on NVMe architectures are ideal for enabling the shared workspace file access performance that creative professionals depend upon to work collaboratively and accelerate output.
Providing ultra-fast reads and writes and impressive parallel performance, while reducing storage and network infrastructure costs and complexity, these modern storage solutions excel at delivering the rapid response times and bandwidth today’s users need to undertake tasks such as audio content creation and editing.
Optimizing discoverability: adopting a metadata-centric approach
Ensuring that audio file content is discoverable, searchable, and available will be critical for ensuring that users can search for and discover audio assets with minimal effort. The problem is, however, that large and unstructured audio files are often labelled with ambiguous names like ‘sales1.wav’ and that creates a unique storage and retrieval challenge.
Employing a metadata-centric approach to file management that utilizes detailed descriptions, tags, and categorizations will be essential for transforming expanding libraries of raw audio data into an organized and retrievable resource. In the case of audio files, that means that in addition to technical information such as file type, metadata tags should feature valuable information about the content of the file such as a detailed description and keywords that are easily searchable by users.
Elevating audio data retrieval and usage
Today’s media asset management (MAM) platforms make it possible to efficiently organize and undertake the lifecycle management of audio and video data for easy and fast discovery, retrieval, and usage.
Ideal for archives containing years of content featuring a range of different data structures, these unified storage systems integrate audio and visual data on a single platform. These solutions also provide users with powerful keyword and custom search capabilities within the video and audio file playback interfaces they use to undertake tasks. In scenarios where multiple copies of the same content exists, ‘current’ or ‘approved’ tags will quickly clarify decisioning for users.
By making it possible to keep audio files in close proximity to related video files and track these associations, MAM systems enable the highly streamlined and efficient workflows needed to support productive and collaborative content production environments. It also reduces the duplication of files and elevates content integrity.
Power up new capabilities with AI
Users often want to interrogate libraries and conduct searches that will uncover hidden audio content gems without having to undertake a laborious manual data cleansing process.
Today’s AI and machine learning technologies are revolutionizing MAM by automating the analysis and tagging of media assets that makes it easier to search through topics related to a particular theme. For example, categorizing content according to whether audio files feature a specific customer, speaker, or topic. Similarly, AI makes it possible to add additional granularity to how sounds are categorized, so that someone searching for a ‘loud crowd cheer’ can discover cheers associated with crowds that are angry, happy, or disappointed.
While the ability of AI to automate large scale repetitive tasks is well recognized, its integration into data storage represents a game-changing move that significantly enhances metadata analysis and data retrievability.
Future-proofing your audio data management strategy
With audio content being created and distributed at an unprecedented rate, a sound data management strategy will be essential for enabling the systematic organization, storage, and retrieval of audio files and related content.
By deploying NVMe flash-based storage solutions that deliver the speed and scalability needed to support real-time collaboration, organizations will be able to significantly enhance the usability of audio data.
Meanwhile, utilizing a centralized AI-powered MAM and metadata structures will streamline how organizations handle the sheer volume and diversity of their audio files to ensure the consistent organization, standardization, and efficient discovery and retrieval of these assets.
In the future, the integration of cloud with highly sophisticated AI tools look set to advance audio data management capabilities yet further, making it possible to deliver even smarter and more intuitive retrieval options and enhance how value is extracted.
Image credit: Olga Tsyvinska/Dreamstime.com
Skip Levens is Director, Media and Entertainment at Quantum.