• Blog
  • Informational

Can Semantics be the Peacemaker between ECM and DAM?

March 3, 2016 3 mins. read Jarred McGinnis

DAM, EMC, Gulliver and Lilliputians

It doesn’t help that Enterprise Content Management (ECM) and Digital Asset Management (DAM) are sometimes used interchangeably and often incorrectly. The world of DAM predates ECM. Digital Asset Management (DAM) is a collective term applied to the process of storing, cataloging, searching and delivering digital assets

DAM vs ECM

Digital Asset Management (DAM) systems centralize assets and establish a systematic approach to ingesting assets so they can be located more easily and used appropriately. ECM systems tend to be large-scale repositories of many types of content held across the entirety of an organization. An ECM will hold materials other than just digital assets such as operational documents and files.

An ECM central principle is to offer a single interface where employees can gain access to all of an organization's data. Click To Tweet

Although, technically, an ECM can function as DAM, in practice most organizations and departments find they need a separate system as ECM tends to be too broad for anything but basic activities such as searching.

Added to this is a cultural divide of the practitioners not seen since Swift’s Lilliputians. Those close to the coalface of digital asset management find it hard to appreciate why they should care about all the other data across a vast organization when they have plenty to do managing the digital assets of their purview.

Those at the enterprise level who are concerned with the increasing demand for platform agnostic content pulled together in real-time that is 100% relevant for the consumer should be forgiven if they fail to appreciate the quirks and peccadillos of each and every data source across the organization.

Let’s not further complicate the picture by adding another acronym of Web Content Management (WCM) by considering content taken from 3rd party sources across the vast and changing world wide web.

Given this state of affairs, it is easy to understand why the battle continues as one side suggests an ECM (one system to rule them all) is fine and DAM practitioners point out the lack of optimization of ECM systems for certain content types and the lack support for functional requirements or specific business needs.

The Role of Semantics

The addition of semantic metadata represents content at a higher-level of abstraction enabling the creation of a programmatic approach to cross-departmental use of assets as content that more closely resembles how humans understand and use the content. It becomes the ‘lingua franca’ between the two opposing worldviews of DAM and ECM. Content is dealt with because of the people, places, organizations, brands, topics that it mentions rather than just structural metadata (e.g., file format, file size, creation date etc.)

The ability for systems to recognize automatically the topics and categorizations of content and the ability to infer context from that information enables different systems to treat content in dynamic ways. Concepts, entities, topics and categories are already useful fulcrums for moving content but it is just the start.

As an organization's understanding of semantics moves beyond faceted search, it can begin to leverage the relationships between concepts themselves and the content. Click To Tweet

This has a number of benefits such as enabling contextually-aware search, improved automated tagging and dynamic digital products.

At the heart of the semantic approach is the concept of graph-driven databases such as GraphDB, which enables flexibility and responsiveness that traditional databases are incapable of. As the organization grows and changes or as the business need changes and grows so can the metadata model that is being used.

 

          New call-to-action

Article's content

Jarred McGinnis is a managing consultant in Semantic Technologies. Previously he was the Head of Research, Semantic Technologies, at the Press Association, investigating the role of technologies such as natural language processing and Linked Data in the news industry. Dr. McGinnis received his PhD in Informatics from the University of Edinburgh in 2006.

Human-computer Collaboration with Text Analysis for Content Management

Read about how knowledge-driven computing such as Ontotext’s content management solutions are essential for closing the semantic gap between humans and computers.

RDF-Star: Metadata Complexity Simplified

Read about how RDF-Star brings the simplicity and usability of property graphs without sacrificing the essential semantics that enables correct interpretation and diligent management of the data.

Knowledge Graphs for Open Science

Read about how knowledge graphs model the relationships within scientific data in an open and machine-understandable format for better science

Knowledge Graphs and Healthcare

Read about how industry leaders are using Ontotext knowledge graph technology to discover new treatments and test hypotheses.

Does Your Right Hand Know That Your Left Hand Just Lost You a Billion Dollars?

Read about how by automatically identifying and managing human, software and hardware related outages and exposures, Ontotext’s smart connected inventory solution allows banks to save much time and expenses.

Data Virtualization: From Graphs to Tables and Back

Read about how GraphDB’s data virtualization allows you to connect your data with the knowledge graph regardless of where that data lives on the internet or what format it happens to be in.

Throwing Your Data Into the Ocean

Read about how knowledge graphs help data preparation for analysis tasks and enables contextual awareness and smart search of data by virtue of formal semantics.

Ontotext Invents the Universe So You Don’t Need To

Read about the newest version of Ontotext Platform and how it brings the power of knowledge graphs to everyone to solve today’s complex business needs..

From Data Silos to Data Fabric with Knowledge Graphs

Read about the significant advantages that knowledge graphs can offer the data architect trying to bring a Data Fabric to their organization.

What Does 2000 Year Old Concrete Have to Do with Knowledge Graphs?

Read about how knowledge graphs provide a ‘human-centric’ solution to preserving institutional memory and avoiding operational mistakes and missed business opportunities.

Three’s Company Too: Metadata, Data and Text Analysis

Read about how metadata grew more expressive as user needs grew more complex and how text analysis made it possible to get metadata from our information and data.

The New Improved and Open GraphDB

Read about Ontotext’s GraphDB Version 9.0 and its most exciting new feature – open-sourcing the Workbench and the API Plugins.

It Takes Two to Tango: Knowledge Graphs and Text Analysis

Read about how Ontotext couples text analysis and knowledge graphs to better solve today’s content challenges.

Artificial Intelligence and the Knowledge Graph

Read about how knowledge graphs such as Ontotext’s GraphDB provide the context that enables many Artificial Intelligence applications.

Semantic Search or Knowing Your Customers So Well, You Can Finish Their Sentences For Them

Read about the benefits of semantic search and how it can determine the intent, concepts, meaning and context of the words for a search.

The Knowledge Graph and the Internet’s Memory Palace

Learn about the knowledge graph and how it tells you what it knows, how it knows it and why.

The Web as a CMS: How BBC joined Linked Open Data

Learn what convinced the skeptics on the editorial side of the BBC to try the simple but radical idea of ‘The Web as a CMS’.

Can Semantics be the Peacemaker between ECM and DAM?

Learn about how semantics (content metadata) can give peace a chance and resemble how humans understand and use the content.

The Future is NOW: Dynamic Semantic Publishing

Learn how semantically annotated texts enhance the delivery of content online with Ontotext’s News On the Web (NOW) demo.

Introducing NOW – Live Semantic Showcase by Ontotext

Discover interesting news, aggregated from various sources with Ontotext’s NOW and enjoy their enriched content with semantic annotation.