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Digital Asset Management has a particular meaning with regard to classes of technology.  The term usually refers to the management of rich media – images, audio and video as opposed to text assets.   However, who deals with text only assets?  If we did, perhaps Notepad would be enough.  More often than not we are dealing with assets in a much larger context as opposed to simply locating an image. 

Semantic Technologies were recently named to the list of Top 10 Technology Trends in 2013. Join Semantic Technology & Business Conference, June 2-5 in San Francisco and partake in hands-on tutorials and lively discussions led by 130+ experts in the space.

Readers can save 20% off gold passports by entering our discount code, ERY, and registering here.

Keynote speakers include Abhishek Gattani of Walmart, Matthew Degel of Viacom, Nick Holzherr of Whisk, Hans Constandt of ontoforce, and Denny Vrandecic of Wikimedia. The event will also feature a semantic start-up competition and hackathon.

Our CEO, Seth Earley, will also lead a panel session at the conference, on Designing Semantic Search and Search Based Applications with SharePoint 2013, to explore how SharePoint can be effectively deployed as a practical means to semantic applications in the enterprise.

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I was in a meeting with a client discussing the approach to Information Architecture – how to develop scenarios and use cases for a target audience and then transform those details into a usable web site.  At one point in the discussion, the client said “Maybe I am missing something but I don’t get it – I don’t see the value”.  I could not contain my surprise – I exclaimed “Really?? You don’t??”  I was taken aback.

What is a Best Practice?  Some organizations don’t like to use the term.  They are not easy to define or to develop since what works in one circumstance may not work in another.  However it is possible to come up with ways of dealing with particular challenges that work well consistently under the appropriate circumstances.  These are “best” when comparing approaches across multiple organizations and programs.  Common themes emerge and a set of practices appropriate to classes of problem emerge.

Big Data has gotten a lot of buzz these days.  What is meant by Big Data is that it is “more” than we are able to manage, classify and process with traditional database and analytics tools and approaches. In an e-commerce setting, Big Data is comprised of an almost overwhelming variety of outbound channel messages and inbound customer and prospect “signals,” across different audience contexts, channels, marketing activity events and speeds.  Inbound messages might be traditionally structured information like conversions by account or product activity by location, or it might be unstructured content cues like call center reports or social media sentiment.

There is a slide that I like to use in presentations from Scott Brinker (http://chiefmartec.com ) that illustrates the complexity of the marketing technology ecosystem.  It is a couple of years old, but if anything, the landscape as gotten more complex, not simpler.

It’s a Big Market …

…  online advertising.  There are 10,000 stories and data points about it.  Here are two to give some context to the journey below.  First, global online ad spending is projected by ZenithOptimedia to exceed print ad spend by 2015 (note 1).  This 2015 projected spend figure for online advertising is $132.4 billion.  Second, global online ad revenue is projected by another research agency, Digital TV Research, to hit $143 billion by 2017 (note 2).

Making sense of customer data has challenged business leaders and information management organizations from the beginning of information management itself. As the technology landscape has evolved, so has the complexity of the challenge.

If your company is like most, no one is delighted with search.  Users can’t find what they need; search results are crowded with irrelevant documents; and people end up redoing things instead of reusing content.  Improving search at the enterprise level is an enormous challenge. There are too many types of content and too many different contexts to address as a whole.  There is no single intervention, no silver bullet. Consequently, more and more companies are recognizing that they need to incrementally address the search requirements of specific user groups.

I've always been fascinated by how challenging it can be to make analogies between digital content strategies and the "real world." We organize and retrieve things all the time! My go-to model for information organization is the kitchen, especially when explaining taxonomy and content strategy. (For metadata talks, my go-tos are DNA and Legos.)