{"id":3073,"date":"2021-01-28T13:23:26","date_gmt":"2021-01-28T12:23:26","guid":{"rendered":"https:\/\/www.celum.com\/en\/?p=3073"},"modified":"2022-04-29T11:08:29","modified_gmt":"2022-04-29T09:08:29","slug":"dam-obstacles-maintenance","status":"publish","type":"post","link":"https:\/\/www.celum.com\/en\/blog\/dam-obstacles-maintenance\/","title":{"rendered":"The 3 biggest obstacles for a DAM project: Extensive asset maintenance"},"content":{"rendered":"\t\t
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The Digital Asset Management (DAM)\u00a0is successfully implemented<\/a>, the assets are transferred, and\u00a0the users are trained<\/a>.\u00a0You are now expecting the DAM to run and harvest the promised key business benefits<\/a> from such a tool.<\/p>

This is where the last part of the comspace<\/a> guest post series begins. In reality, one or several employees are almost exclusively occupied for a long time with manual tagging and categorization of existing assets. In case of an international company, the metadata needs to be translated as well.<\/p>

Why is this necessary? If the assets are not consistently and precisely tagged and categorized, then a high-performance search in the DAM is not possible. The anticipated simplification of daily work does not take place.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Essential Tagging and Categorization of Images<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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In most organizations, huge volumes of images constitute the bulk of the assets residing in the DAM. These often do not have a descriptive name, but are stored under titles such as 2023948_201365.png<\/em>\u00a0or DSC_9001.jpg<\/em>. How is the DAM supposed to know which images fit, for example, the product category \u201cgarden shears\u201d and for which search query should they pop up?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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A powerful search is only possible with tags<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Without appropriate tags, images get lost in the virtual vastness of the DAM and are very difficult to find. This is especially true, when data volumes increase dramatically and many people use the system.<\/p>

However, manually tagging images cannot be the solution. Tagging a single image can already take several minutes. Who is going to do this tedious work, especially since most companies upload large volumes of new images regularly?<\/p>

Some DAM systems make the work easier by providing a clear capture dialog or bulk tagging of several assets at the same time, but only automatic solutions provide a real remedy.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Automatic tagging solution<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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One of these solutions is our automatic image recognition tool, Autotagger<\/a>. It is based on the AI solution Imagga and handles the automatic tagging and categorization of huge amounts of images in a very short time.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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AI dramatically speeds up tagging and categorization<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tHow does the solution work? Via API, the image files are transferred to the\u00a0artificial intelligence<\/a>, where they are checked by the machine learning algorithm. The AI then reports the tags and categories back to the DAM and the data is stored at the respective image. In this process, the tags are used to quickly find the images, while the categories allow the user to search for abstract collective terms such as animal instead of parrot, or fruit instead of apple.\n\nBy searching for appropriate categories, all users in the company have faster access to suitable images \u2013 whether in the DAM, CMS or in the connected third-party systems. Suddenly, the DAM answers search queries such as \u201ccar blue\u201d, \u201csunset beach\u201d or \u201cwoman laughing\u201d with a selection of suitable assets. The result is a positive experience for all users. They get what they need for their use case quickly and easily.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Custom training thanks to learning AI<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Autotagger can assign many images to suitable keywords and categories by default. When it comes to industry- or company-specific product images, the AI can be trained on new tags and categories in a custom training session. The Autotagger then is also able to recognize the W 6413 drill or the CV312 connector.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Metadata maintenance in multiple languages<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Your assets need to be used on international websites or in stores, but the metadata and tags are only available in German? Then the employees in the foreign country or foreign sales companies cannot use the DAM adequately, because the internal search for tags will not be successful.<\/p>\n

In most companies, translating metadata into different languages is a time-consuming and error-prone process that involves exporting metadata from the DAM, passing it on to a translation agency, and then importing the translated version back into the DAM.<\/p>\n

With an automated translation solution such workflows are drastically shortened. Our integration solution for the DeepL translation software<\/a> is suitable for this purpose. DeepL is one of the best machine translation technologies currently available. Its high quality is based on artificial neural networks that understand words in their context instead of translating them individually.<\/p>\n

In addition, the DeepL service is 100 percent DSGVO-compliant. Texts are deleted after translation. With the DeepL integration into CELUM Digital Asset Management<\/a>, editors can have automatic translations performed in 49 language combinations. This can be done after manually selecting specific assets or asset folders, or automatically when importing new assets.<\/p>\n

This means significant time savings for editors, while maintaining very high translation quality and minimizing errors. In addition, the costs for a translation agency are saved.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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A DAM implementation is work \u2013 but it\u2019s worth it!<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA DAM system brings structure to data chaos, positively influences internal organizational processes, and pays off in terms of a consistent\u00a0corporate and brand presentation<\/a>. Like any new software introduction, a DAM project must be well planned and rolled out in a coordinated manner with all parties involved. Preparation, implementation, and communication take work, require resources, and are not a foregone conclusion.\n\nDAM projects are more difficult, when\n