Making Data the Foundation of Your Digital Asset Management (DAM)

Digital assets have an important place in any organization that is employing a content strategy to increase its outreach efficiency. Digital Asset Management (DAM) systems such as Blueberry DAM are efficient in this regard as they offer a systematic way to store, manage, and distribute digital files. Nonetheless, the way data is modeled and stored significantly impacts a DAM system's performance. A data-driven DAM enables the system to become more intelligent, increases asset discoverability, and can inform better strategic decisions. Read on for a detailed guide to deeply integrating data with your DAM operations.

Data is key to DAM

A DAM system is far more than a glorified hard drive in the cloud, it should be a complex framework that branches out to every single piece of rich data behind an asset. This data can be as simple as the name of the file to more complex metadata with information about copyright, permission, and historical use of the asset. Accurate data helps properly categorize, search, and surface assets to users - this is the lifeblood of any solid DAM system.

Step 1 - Data Standardisation

Metadata Schema Definition: Start by defining a common metadata schema. This schema should contain what data points are required for each type of asset. So in other words, it could be descriptive metadata (concerned with describing an asset i.e. title, tags, description), administrative metadata(rights that are managed by the organization, use falls under copyright), and technical metadata(file format of the asset; resolution).

Use a Controlled Vocabulary: The quick win here is that using a controlled vocabulary or taxonomy of some sort ensures the labeling and organization of content are consistent. This includes mapping a common tool to tag assets such that all assets are categorized and described by a standardized list of terms. An organized vocabulary helps to reduce confusion and makes sure everyone is on the same page with how to refer to things, which in turn can improve search.

Implement data input norms: Establish a uniform process of entering data to maintain consistency across the company. This could include staff training sessions and repeated checks of all data entering the system to confirm it conforms to the standards your organization has set.

Step 2: Data Collection and Integration 

Metadata generation: Automatically generate metadata where feasible to reduce burden and prevent human error. Most DAM systems can extract certain basic metadata from files upon upload (e.g. file size, format, creation date_ More sophisticated systems can tag via a simple AI/machine learning model driven based on the content of your assets.

System Integration: For higher DAM adoption, it is advisable to have a system integration with other customer-rich systems like CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), PIM (Product Information Management), etc. With this integration, the DAM assets can be substantiated with actionable data sourced from these systems offering a larger scope of understanding relative to asset utilization and performance.

Blueberry in action intergrating with other tools.

Step 3: Improve the quality of  data

Put Data Governance Policies in Place:  Data governance is a big deal to keep the data consistent and rightfully optimized. Define policies around data accuracy, privacy, security, and compliance to influence how DAM assets and associated data are managed.

Periodic Data Audits: Regularly monitor your DAM data integrity to check if the truth in it is accurate enough. This is probably the most boring but arguably the most important part of DAM - you must also perform a regular, ongoing asset review to check metadata remains accurate, usage rights are current, and remove/disable redundant or obsolete files.

Feedback User: Allows users to give feedback about incorrect or missing data. This will help to maintain the quality of your DAM data and keep it relevant and accurate for the duration.

Step 4: Data Analytics

Monitor Usage of Assets across Platforms: Leverage the analytics tools available in your DAM solution to monitor how assets are being used and their performance on different platforms. One of the leading sources Mintec used for this information is none other than the State of Inbound report from HubSpot, which is why we felt it would be beneficial to use their formula while disclosing our data to provide more insights into content trends, popular assets, and user engagement.

Prediction: A DAM system with more advanced properties would have predictive analysis to predict trends in their historical data. This can be translated into decisions like what kind of assets to develop more or how you change your content strategy based on user preference.

Provide Reports: Routine reports are essential for stakeholders to know how assets are being used, to manage copyrights and licenses, and to effectively plan their content strategies. Make sure you Retain a DAM system with the ability to create custom reports that align with all of your organization's unique needs as this is crucial.

Summary

Deploying data at the core of your DAM system allows you to go beyond using it just as a repository to a powerful platform that provides visibility, reusability, and impact effectiveness of your digital assets. Your DAM can play a pivotal role in supporting your organization's strategic goals, by establishing robust data standards, layering rich data sources incorporation on top, maintaining high data quality, and having that feed into advanced data analytics. By doing so, you not only get the most potential return on your investment in a DAM system but also equip your team with actionable insights from a source and build experiences that are grounded in data, fostering content invention and operational excellence.

Please visit Blueberry DAM free trial for more information.