You need rich media to engage prospects and customers, and the good news is the global supply of digital content continues to grow. By some estimates, there are 1.72 trillion photos taken every year. Add to this the enormous volume of video, audio, and text content, and there is truly an astounding amount of unstructured data in the world today.
Your company is likely in possession of valuable assets like photos, videos, datasets, and business insights waiting to be discovered. But without a systematic way of organizing, managing, and mining your data, the sheer volume can make these assets inaccessible and useless. Your team plays a critical role in organizing your digital assets, but manual digital asset management is prone to error, lacks consistency from one employee to another, and is very time-intensive.
There are a ton of AI-powered tools that integrate directly with your existing technology stack and automate a lot of the hard work that goes into digital asset management. By configuring the right AI models, you can quickly search, sort, and filter digital assets without spending a bunch of time setting up folders and labeling data.
If you haven’t already integrated AI into your systems and processes, there is no time like the present. So without further ado, let’s take a look at the top 5 ways that AI can make life easier when it comes to digital asset management.
Better content tags (aka metadata) can improve productivity instantly. By tagging your data with the right keywords, you can quickly search, sort, and filter your content. This helps your users find the right content more quickly. You can accelerate the labeling of content so that digital assets are ready to be used internally within your company or published on the web.
With AI automated tagging, metadata is created and added to assets significantly faster and more accurately than you can do on your own. By using AI models, you can classify and tag images, videos, text, and audio content in seconds, freeing up time and resources for higher-value tasks. AI-based tagging also helps you standardize how digital assets are categorized and tagged regardless of who creates them.
With thousands of images to look through, sometimes keyword searches aren’t enough. Clarifai Search, lets users search by keyword or concept, image, metadata, and even geolocation so you can find what you are looking for easily. Locate unlabeled assets and accelerate the discovery of alternatives within your content library. With AI you can also search using similarity to find content that is similar to a given image input or text input. Visual and semantic search tools thus help you quickly gain additional insights from unstructured image, video, graphics, text, and audio data without even searching by keyword.
Preparing data for a specific task is more difficult when you are dealing with unstructured data. By their nature, image/video/text/audio data is heavy (large image files) and difficult to search. If you are preparing your data for a specific task, you will want to prepare your data with consistent formatting and you will want to remove any data that does not help you accomplish your goals.
Also, if your data is collected or labeled by humans, it can be helpful to use this data to ensure that it meets quality standards. AI can help you find, enrich and transform unstructured images, videos, text, and audio assets faster and more accurately so that you can produce trusted and integrated datasets that are ready for analysis. There are a variety of AI models that can automatically tag your data with pre-defined concepts, and you can even train custom models to tag your data with your own custom concepts.
You want to be sure that your brand is being presented correctly. Wherever a brand element appeared—such as a logo, color scheme, or marketing copy—AI can identify the asset and ensure that your digital media follows your pre-established brand guidelines.
You can automatically manage logos, product artwork, packaging, and corporate branding with AI-powered tools that automatically categorize unstructured data. You can even augment version control efforts with tools to identify digital assets with subtle product variations.
Data deduplication is important because it significantly reduces your storage space needs, saving you money and reducing how much bandwidth is wasted on transferring data to/from remote storage locations. In some cases, data deduplication can reduce storage requirements by up to 95%. Data duplication is a very common problem in the world of digital asset management. AI for data-duplication can help you find duplicate data records – even in the absence of unique identifiers and exact data values.
With AI you can eliminate duplicates and near-duplicates in your dataset with automatic search, sort, and filtering tools. Visual and semantic search tools quickly group similar data without the need for special model training or data labeling.
The inability to manage trusted, high-quality media at scale can cause serious problems. Improperly tagging data and poor metadata descriptions will make large volumes of data unmanageable. AI models are trained and evaluated before they are deployed to tag data consistently and predictably.
AI can significantly reduce the work required to manage digital assets so that you can scale from self-service asset management to enterprise-level projects by becoming more operationally efficient.
Media, retail, entertainment, e-commerce companies, and more have come to Clarifai to help overcome bottlenecks in data analytics, marketing, product releases, asset findability, and more.