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    [Video] Why is Information Architecture so important RIGHT NOW?



    Information Architecture is extremely important today, especially since there's a lot of new technologies, emerging technologies, emerging applications, whether they are traditional tools, or tools that leverage machine learning. Just about every vendor has AI in their technology stack in one way, shape, or form, at least they claim to. However, whether it's a conventional technology, or an emerging technology that leverages machine learning or artificial intelligence, all of these applications, especially when we start getting into complex technology, stacks require consistency of terminology.

    Many applications grow up independently. They're deployed and configured as separate projects in silos. They're built by different vendors. And a lot of times, it's very difficult to have information flowing from one system to another. That's why we're seeing more platform types of applications where we can build lots of different things on the same core infrastructure.

    However, when we look at data sources which power these things such as machine learning and artificial intelligence, they have they rely on good data, we can't fix bad data, we can we can improve it, there are things we can do. We always will need a reference architecture. That's the information architecture. That's the scaffolding for knowledge and content, and information and data.

    So when you build out an information architecture, you're building out standards. And the standards are standards that can help improve both vendor onboarding, or to or technology onboarding or new products if we are a distributor, or help us with getting our new products through the design process and out to manufacturing and out to distribution.

    When you think about a physical supply chain, it's also an information supply chain. So the information that flows through that supply chain from it's very inception to the consumer -- to whoever is using it all has to be structured consistently. And if we don't have that consistent structure - and this especially happens when we're talking about internal processes, when we're trying to create a good customer experience, we have lots of different tools, we have lots of different stakeholders, different groups, different collaboration, different types of information sources -- when those are not architected consistently, it causes friction, it slows down the process. So information architecture, you know, is even more important today than ever.

    And one person observed or stated to me that well wait a minute with machine learning and artificial intelligence there's no need for taxonomy. And nothing could be further from the truth. I wrote an article a few years ago called there's no AI without IA. There's no artificial intelligence about information architecture, and that talks about the fundamental need to have that core architecture and a lot of people have adopted that phrase in the industry to say look, we need to think, IA before AI. That's Jeff McIntyre's incarnation of that that concept. But the point here is that we don't get to make up for our past sins in data curation and data management, by pointing it to pointing a tool to it or using some kind of technology that's allegedly going to fix all the data. The data still has to be good quality, it has to be appropriate, and it has to be structured correctly. This is especially true when we talked about cognitive assistants and virtual assistants and box that are high functionality bots, we have to structure the knowledge we have to structure the content.

    So information architecture is even more critical to the success, successful operationalization and deployment of these types of tools and technologies. Many times people say well, data scientists spend more time being data janitors. And that is a big stumbling block to going from pilot or POC to full deployment and operationalization. We can't spend all that time fixing operational data, as we go, because it moves too quickly. So we have to structure it and we have to fix it. We have to improve the quality of the source we have to have standards, and that will help us be able to deploy these tools and technologies much more effectively.


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    Earley Information Science Team
    Earley Information Science Team
    We're passionate about enterprise data and love discussing industry knowledge, best practices, and insights. We look forward to hearing from you! Comment below to join the conversation.

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