As an international DAM consultant, Kristina Huddart helps businesses harness the power of digital asset management technology - to deliver operational efficiencies and unlock the potential of their increasingly valuable marketing content.
Huddart Consulting supports organizations of all sizes and at every stage of their DAM journey. Whether that’s a startup exploring their DAM options or a global enterprise implementing a platform to serve tens of thousands of users. A specialist in change management, Kristina makes sure DAM projects are professionally planned and delivered, to ensure maximum user adoption and ROI.
We caught up with Kristina to talk use cases, enterprise challenges, and why technology is only 20% of the puzzle.
I'm often approached initially by companies who are experiencing problems with their digital asset management practice. They say ‘we can't find our valuable content’ or ‘we seem to be wasting a lot of time searching for or recreating content that already exists somewhere out there in the ether’.
So they’re looking for a DAM system to help with this. Sometimes companies have already been working with a legacy DAM system. Others have been working with the really basic kind of file content management systems like Google Drive or SharePoint. And that's where they've been storing all of their content - plus on hard drives and on local computers all over the place.
One of the really interesting things about working in the DAM space is that all organizations, no matter what their business is, ultimately really need a solution to managing digital rich media and content.
I’ve worked with clients in retail, e-commerce, events, the cultural sector, and universities… they all need digital asset management. It's across the board.
Unexpected companies like banks and insurance companies use DAM too. All businesses need to manage their content somewhere. And that's going to be a DAM - whether they know it now or not.
Some companies are just initially getting into DAM - or what we call DAM 1.0. That's usually when they need a space to manage all of their assets in one place. So kind of like a media library where all of their images, audio, video marketing, and sales assets, all of that can live in one place.
And then from there, it usually expands into brand management. So a lot of companies will use DAM to manage brand consistency and serve their brand assets to partners and collaborators. So those are the more basic use cases for a DAM.
Then we get into some of the more advanced use cases. Companies who want to start delivering omnichannel experiences to customers, who are going to launch to e-commerce next year... Or who need a way to distribute content to distributors or resellers. Or who might be expanding into new markets or thinking about personalizing their content at scale. That's where a DAM really comes into play.
So there's all sorts of different use cases. And the exciting thing about DAM is that it's always expanding. There are always new content problems or pain points that are coming up in organizations - like ‘How do we manage all of these videos that we're creating now?’ - and DAM is there as this amazing tool that can help with all sorts of different problems.
The exciting thing about DAM is that it's always expanding. There are always new content problems or pain points that are coming up in organizations and DAM is there as this amazing tool that can help with all sorts of different problems.
It's usually triggered by some sort of pain point or recognizing the need for digital transformation.
It's about making a lot of the manual processes that all of us do day-to-day - whether it be finding assets, or creating assets, or delivering those assets to our customers in an efficient way - and making all of those processes digital. It's as simple as that.
Companies always have to strike a balance. How many resources do we have? How much budget is there? How much headcount is there? And that's where companies look to technology - like digital asset management platforms - to help to scale.
There’s that initial awareness of what a DAM can do. There's definitely excitement around choosing a new technology and businesses get really excited that the DAM is going to solve all of their problems. So there needs to be some education and understanding of where the scope of DAM starts and where it ends.
And then there’s the discussion about the fact it’s not just about the technology. The technology definitely needs to be an amazing tool. But technology is probably only about 20% of the solution.
There's this golden triangle of people, process, and technology. In fact, I’d make it into a golden square by adding in data as well. And you need to get all the pieces of the puzzle in place.
So imagine a company says we're going to get a new piece of tech, but we're still going to use our old processes. And you've got this 20-step process and you stick it into a new tool… it's going to be as clunky as it was before. Nothing's going to change.
So digital transformation isn’t just selecting the right tool. It’s also about improving your processes and making sure that the people come along with it too. Because you want your people to change the way that they're working, improve the way that they're working, and adopt this new system.
And then you've also got to always make sure that the data stays clean. That's a continual process. So DAM isn’t a plug-and-play tool. It's a practice.
‘DAM isn’t a plug-and-play tool.
It isn’t a case of set-it-and-forget-it.
It's a practice.’
I think the most challenging elements of working with really big companies - at least 20,000 users of the system around the globe - is getting everyone to work in the same way and to adopt this new tool.
But it’s also the huge volumes of data - that's also a real challenge. I support companies with data management, data cleansing and migrations.
It's something I like to start planning with companies as early as possible, even before the DAM is implemented. How are we going to get assets into the system? What’s our main priority?
And also setting up the metadata and taxonomy structure, then applying that to the data before it's migrated. It’s about starting out with clean data and a clear structure - and maintaining good practice from there.
When a company starts to consider implementing a DAM, there's a whole checklist of things to do. But if I had to choose one unskippable step, I would say that it's the business case.
You need to ensure that the documentation from the initial decision-making is fully recorded, so that you can always go back to it. Why are we doing this? How much are we going to invest in it? What are the alternatives? What are the consequences to the company if we do nothing? What are our objectives and KPIs?
These are really interesting considerations and help you to understand why a particular DAM was selected, why the metadata was set up in a particular way, whether the DAM is delivering what you wanted it to…
DAM integration planning and strategy is so critical. And again, I would say start planning that as soon as you can, as part of your vision and your roadmap for the DAM.
I like to start with mapping out the end-to-end content flow. So look at how content flows from the initial creative production team - who are probably using Word documents, InDesign layouts, video editing tools, email, WeTransfer and more - to final distribution.
How do assets get from the DAM onto social media, onto the website? How is your print content stored and updated over time?
That's where I like to start when clients are thinking about their integration strategy. So we can consider how to take this manual content flow and make it easier and streamline it by linking these tools to each other.
So that your web content manager doesn't have to find an asset in the DAM, download it to their desktop, and then re-upload it to the website… That’s not efficient.
AI is interesting because it is actually a collection of tools and each tool can solve for different problems. So when we’re talking about AI, we’re not talking about just one thing.
Artificial intelligence is not a silver bullet. It's not going to fix everything. There is a misconception of how it's going to magically apply all of our metadata for us. But it's not quite like that.
There are use cases where AI can be of real benefit. But you’ll need to train and maintain a machine learning algorithm. It’s an investment and organizations need to think about the business case and whether it will deliver ROI. What is the problem that they're trying to solve with AI right now?
My ultimate advice is that DAM isn’t a project, it’s a programme of work. You need to consider the technology, people, process and data. Also the importance of effective change management - and sustaining change after initial implementation, getting your users to really adopt and love this new system, to make sure people don’t slip back into their old ways of working.
And finally, the importance of doing an annual health check on your DAM. To understand what’s working and what’s not - so you can improve and iterate as you go along. How’s the tech? How are people adopting it? Do you still have a vocal champion in the business? Are you still aligned with business objectives? Are new processes working, and could they be streamlined? Look at the data too - it’s an ongoing process, and that responsibility never ends.