The big question: “What am I trying to achieve?”
Whenever people start exploring martech tools like DAMs, it’s easy to get distracted by the latest buzzy, flashy features. Focus on omnichannel and hyperpersonalization have given way to AI and ML, and those will surely get replaced by the latest, greatest features as time marches on. But Iskander cautioned against getting distracted or fixating on one area in particular.
She likened implementing a DAM to building a house. If you’ve been told the project needs to be completed by Q3, you might rush from room to room so you can hand over the keys in time. But then your first guest comes in and tries to turn on the lights (or, in the case of a DAM, tries to trigger their desired automation). Nothing happens. In your hurry and your focus on what’s most visible (like AI/ML), you forgot to install electrical (the data layer to power your vision).
“In order to build,” she said, “You need to understand the vision behind what you’re doing.” You can’t build an electrical panel unless you know how much electricity you need to produce, she pointed out.
She advised enterprises to slow down and zoom out so they can ask the big question: what am I trying to achieve? That might mean you don’t hit your Q3 deadline. But Iskander shared that our team often sees enterprises paying money to deal with data gaps that they missed because they went too fast, too soon.
In short, companies need to invest early on in the layers they need to achieve success. And identifying those layers — and the requisite data — means first getting clarity on what they want the DAM to accomplish.
Building a firm data foundation in layers
Iskander identified four layers of data that an asset can carry in a DAM:
- The asset is captured in the DAM and is described in some way
- The asset relates to other things (e.g., a product, a project)
- The asset connects to personas (i.e., who should receive it, how they should receive it, when they should receive it)
- The asset holds information (e.g., when delivered to a particular person in a particular way at a particular time, did it result in a conversion?)
All of these data layers are important, and enterprises often move from the top down as they mature. As they tackle the different layers, Iskander laid out a few things to consider:
- Interoperability: Organizations need to slow down and break down any silos. Assets need to interact with other systems and to be distributed among other people or the DAM will never scale. In order to achieve interoperability, your systems need to speak the same data language.
- Completeness: Complete data means that the data is always available when it’s relevant. To achieve that, each time you create content, you need to take the time to classify it. When your data is complete in this way, stakeholders can trust it.
- Accuracy: As Iskander said, “Garbage in, garbage out.” The quality and accuracy of the data you feed into a system directly determines the quality and accuracy of what you get from it. Bad data could mean missed connections with personas, or incorrect ones.
As Iskander said, “Get your data right first. If we want to be able to be responsive, dream up something and then achieve it, we need these building blocks.” She encouraged attendees not to focus on achieving an implementation by a certain deadline. Instead, she said, they should pivot and spend some time really working out the data pieces, then come back to the doing. This way, they stand to gain more from the solution they get up and running.
From implementation to integration
Iskander also moderated a panel with several DAM experts that focused on integration strategy. As Bulent Dogan, Founder & Chief Architect at CyanGate explained, “Integration increases the value of your assets by enriching them while lowering your costs by automating processes.”
The panel discussed companies looking for one tool that can do it all versus choosing several specialized solutions that integrate together. Emily McGuiness, Creative Operations Manager at IMAX, recommended taking a layering approach. While a single solution might work when an organization is small, she said, as it grows and its needs get more complex, most enterprises eventually have to connect specialized tools. Layering them allows the company to create its ideal solution.
The panel pointed out that the need for integrations is ever-growing, and that increases the importance of stakeholders understanding their DAM and other integrated tools. “The end points where digital assets need to be distributed continue to multiply,” Jeff Bridges, VP of Client & Partner Development at Ntara, explained. “[Assets are] going to need to be everywhere and so that balance of governance and distribution continues to be critical.”
To create a long-term integration strategy that factors in expanding distribution channels, governance considerations, evolving tech, and more, the panel underscored the importance of stepping back. Much like Iskander’s individual session, they encouraged attendees to start developing strategy by first understanding what they’re trying to accomplish.
Each panel member offered a tip for setting an enterprise’s martech integration strategy:
- Start with clear direction. “The reason for bad integrations is often that the purpose and the success metrics weren’t defined,” Iskander said.
- Identify and tie to business value. “Your business strategy is going to change,” Bridges said. “But if you can tie your decisions back to business value, that’s the link between those technical things [required for integration].”
- Embrace change. “The challenge of forming the strategy and executing it over multiple years is that shift of helping people understand that change is not scary,” McGuiness explained. “Let’s all go for the ride.”
- Get buy in. “Have all stakeholders at one table when you’re trying to strategize and have them buy into the decision process,” Dogan advised.
Moving DAMs forward with data and direction
Laying a firm data foundation and having a clear strategy behind integrations help marketers do more with their DAMs. Iskander gave a couple of examples to highlight the importance here.
In one instance, she talked about a global consumer packaged goods (CPG) company that approached our team for a DAM. We quickly realized that the CPG’s North American team didn’t speak the same data language as its international teams. The classification of assets may not have seemed hyper-critical, but not being on the same page at a taxonomy level made features like search all but impossible. The client was willing to pause the implementation project to first get aligned on what they wanted to call things. That simple but crucial calibration allowed us to build their DAM on a firm foundation.
Iskander gave another example of a fast food chain that worked with us over multiple years to establish a solid data foundation. When COVID-19 hit, they were able to distribute the necessary materials to all of their locations to reopen with drive-thru-only service in a matter of days. Their response was so well orchestrated that COVID clinics reached out hoping to model their processes.
