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May 15, 2012 in Curation, Information Lifecycle Management | Tags: Attribution, Brian Solis, British Museum, collaboration, commentary, Content, Curation, Curator, Data Administration, disposition, evaluation, exhibition, Information Lifecyle Management, Management, Network Weaver, Openness, organization, Pinterest, scoop.it, Social media, Social Network, Transparency | 3 comments
A recent post by Brian Solis “The Curation Economy and the 3 C’s of Information Commerce” neatly deconstructed the information flow within the Social Network. The 3 C’s are creation, curation and consumption, and while consumption remains the largest activity he correctly identified curation as a vital part of the social information chain, as it is the intermediary and often principle connecting service between the authors and readers of content
There are many curation tools available (@williampearl Shirley Williams’ blog post references 40). Most serious Social Media participants use one or several of them to save interesting content discovered or referenced in their daily pursuit of engagement.
Though the name curation is applied to such tools as scoop.it list.ly Pinterest and others all too often these tools act as nothing more than scrapbooks, with photos and articles appended to pages because they caught our imagination, piqued our interest or satisfied our desire to be seen as a member of a community of interest.
It is true that many curating users perform a rudimentary evaluation to classify the curated content and to position it within a relevant category; an even smaller number provide some commentary on the content. But like a scrapbook these collections remain static with a last-in first-presented view of the collection that has been assembled. Content that was first collected generally remains buried under more recent entries, and interactive commentary is almost non existent. As a result the value of such collections is greatly diminished and the prime activity of social media curators appears to be browsing the curated pages of others in search of new content to display on their own.
This observation may be harsh, yet I believe that there are many curators who do far more than I have indicated here, however the current tools have limitations. Furthermore to raise curation to the level required to act as the intermediary between creation and consumption, as indicated by Brian Solis, we need to bring aspects of Information Lifecycle Management disciplines and processes to bear on the problem. In a previous post on the network weaver I had already identified curation as one of the 5 major components of the social networking architecture. It is notable that it takes up to 2 years for a post graduate to obtain an MFA in curatorial studies or a Curation Diploma from the British Museum. I have used the British Museum course curriculum as a basis for identifying the sub components of Social Media Information Curation.
- Attribution – The first step on receiving any new content it to examine its provenance, determining source and history (journey) to the curation site. Part of this is validation, in social media terms checking that is not spam or spoofing, and part of it is ensuring the links and references are still active and, if not, refreshing them or marking them inactive. Once validated it is important to attribute the content to the author (direct) or those who have shared the content (indirect). The reason for doing this extends beyond mere politeness as it promotes the contributors and increases their relevance as possible collaborators in this or any related collection.
- Evaluation – the analytical step in the process and one that should not be embarked upon lightly, as it takes a high level of expertise to properly evaluate content. It is not just determining classification and category, it involves going several layers deeper to ascertain the nature and value of the content. Is the content authoritative, supportive, contrary, derivative, anecdotal or coincidental for example and, as a lead in to the next step, what is the etiology of the content and how is it related to other content entities?
- Organization – as with any information repository the key to consistent value is the way the content is organized, and the flexibility of the structures that support it. The value of content is greatly increased if the relationships between entities can be indicated and that links are flexible enough to be easily orchestrated when new content or understanding modifies the relationship.
- Commentary – Curators are also creators of content, a slight divergence from the Solis model which limits the curation role to an intermediary who is not part of the digirati (his description of the authoring elite). Commentary is an essential part of curation as it explains and amplifies the content and the relationships of content in any collection. However in an open collaborative environment commentary is not limited to just the curator or curation team. It can and should be as interactive as comment sections on blogs or message boards, with the curator as the default moderator. This is the activity that augments the content and extends the knowledge and value of the information.
- Exhibition – First and foremost the purpose of curation is to care for and promote the collected content and bring it to the attention of the consuming public. This is more than just broadcast and communication it is preparing and mounting a rich and informative display of connected artifacts, which illustrate the themes, dimensions and complexities of the subject at hand. Successful exhibitions are compelling, relevant and often topical. They also do not last forever, but can be dismantled and recreated with fresh insight and perspective at a later date.
- Disposition – unlike transactional data that needs to be aged and archived, social data is more like the objects in a museum, they are never destroyed or deleted, and rarely put into forgotten repositories. They are stored and maintained as objects with variable value and possibly potential future reuse, they are out of immediate sight but always available for reference or inclusion in other contemporary collections.
As can be seen from the diagram the information lifecyle has no end. Disposed (ie stored) information still needs to be maintained and re-evaluated and this is the task I have described as Collaborative Husbandry or collective farming. This is equivalent to the constant reexamination of requirements in The Open Group Architecture Framework (TOGAF), as current and new information can change curated landscape very quickly, and skilled curators should be able to adjust the curated content to accommodate this. The more sophisticated and comprehensive the collection the more curating resources are needed to maintain the information quality, which leads me to believe that enterprises will seek and appoint skilled curators and possibly even a Chief Curation Officer as they become increasingly dependent on external information and resources.
I would be interested to hear of additional requirements for Social Media Curation, as I believe we are still in discovery mode on what is needed to better identify, collect, discuss and exhibit the knowledge that is cascading through the global Social Media.
- Are You A Content Curator? 5 Best Practices For Content Curation (mithuhassan.wordpress.com)
- PSU-Art Curator Job Description (pluginin.org)
- 8 Social Curation Tricks For Pinterest And Beyond | iMediaConnection (mithuhassan.wordpress.com)
- A Marketer’s Guide to Content Curation (hubspot.com)
- “New Exhibit! Native American Cultural Objects at the CHP «.” New Exhibit! Native American Cultural Objects at the CHP «. n.p., n.d. Web. 9 May 2012. http://centerhistorypsychology.wordpress.com/2012/04/27/new-exhibit-native-american-cultural-objects-at-the-chp/.
May 1, 2012 in Big Data, Information Lifecycle Management | Tags: Analytics, Application programming interface, Architecture, Big Data, collaboration, Data Administration, Data Mining, Definition, Global, Information, Networks, Open, Signal, Social media, Standards, Streams, Walled Garden | 3 comments
How does one define Big Data and is “big” the best adjective to describe it? There are many voices trying to come up with answers to this topical question. Gartner and Forrester both agree that a better word would be “extreme”. Between the two major consulting firms they have determined four characteristics that extreme can qualify: they are agreed on three: volume, velocity and variety. On the fourth they diverge, Forrester postulates variability while Gartner prefers the word complexity. These are reasonable contributions and may form the foundation for the definition of big data that the Open Methodology Group is seeking to create within their open architecture Mike 2.0.
However the definition still falls short of the mark, as any combination of these characteristics can be found in many of today’s large data warehouses and parallel databases operating in outsourced or in-house data centers. No matter how extreme the data eventually Moore’s Law* and technology will asymptotically accommodate and govern the data. I could suggest that the missing attribute is volatility or the rate of change, but that too can be applied to current serviced capabilities. Another important attribute that is all too often missed by analysts is that Big Data is world data, it is data in many formats and many languages contributed by almost every nationality and culture and the noise generated by the systems and devices they employ.
Yet the characteristic that seems to address this definition shortfall best is openness, where openness means accessible (addressable or through API), shareable and unrestricted. This may be controversial as it raises some key issues around privacy, property and rights, but these problems for big data still need to be resolved independent of any definition. Why openness? Here are six observations:
- Any data that is not open, ie that is private, covert or obscured is by default protected and confined to the private architecture and data model(s) of that closed system. While sharing many of the attributes of “big data” and possibly the same data sources at best this can only represent a subset of big data as a whole.
- Big data does not and cannot have a single owner, supplier or agent (heed well ye walled gardens), and is the sum of many parts including amongst others social media streams, communication channels and complex signal networks
- There will never be a single Big Data Analytic Application/Engine , but there will be a multitude of them , each working on different or slightly different subsets of the whole.
- Big Data analysis will demand multi-pass processing including some form of abstract notation, private systems will develop their own notation but public notation standards will evolve, and open notation standards will improve the speed and consistency of analysis.
- Big Data volumes are not just expanding, they are accelerating especially as visual/graphic data communications becomes established (currently trending). Cloning and copying of Big Data will expand global storage requirements exponentially. Enterprises will recognize the impractical economy of this model and support industry standards that provide a robust and accessible information environment.
- As enterprises cross into crowd-sourcing and collaboration in the public domains it will be increasingly difficult and expensive to maintain private information and integrate or cross reference with public Big Data. The need to go open to survive will be accompanied by the recognition that contributing private data and potentially intellectual property is more economic and supportive of rapid open innovation.
The conclusion remains that one of the intrinsic attributes of Big Data is that it is and must be maintained as “open”.