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July 19, 2012 in Curation, Information Lifecycle Management, Social Media | Tags: browsing, Content, Curation, Data Mining, Definition, Information Lifecyle Management, Marketing, Networking, Pinterest, poll, Sharing, Signal, Social media, Social Network, Streams, visit | 24 comments
In a very short time curation has evolved from a minor supporting role to a major or even leading role in Social Media engagement. It is no longer sufficient to just share items of interest, breaking news and opinion, not if you want to be regarded as authentic and taken seriously.
Curation has many definitions, including my own: “Curation is the acquisition, evaluation, augmentation, exhibition, disposition and maintenance of digital information, usually centered around a specific topic or theme”. The Digital Curation Center (DCC) in the United Kingdom puts it more succinctly
Digital curation, broadly interpreted, is about maintaining and adding value to a trusted body of digital information for current and future use. (DCC)
Both definitions infer an information lifecyle process, that manages the digital objects from creation to deletion. Both suggest that capturing and adding value, whether by commentary or related material, is vital to the end product which is knowledge or information that can be referenced now and in the future.
However the evolution of digital curation is experiencing some fragmentation. Not that this is bad, but it does suggest the differences should be understood as curation tools will differ in features and capabilities as each tries to satisfy its target customer base. So far I have identified 3 major distinctions in curation:
- Marketing Content: comes in several forms as marketeers move away from landing pages on Facebook and web sites, and seek to amplify brand presence through curated content.
- Information (or Knowledge Content): More focused on collecting and condensing information to support a topic or subject. Most commonly a reference site usually set up for either internal or external collaboration
- Personal Content – less dependent on content management features and capabilites: can either be used for amplification (self-branding) or condensing (information).
The question I would like to pose is who visits these curated sites and what are their preferences. The following poll offers choices in the style and content of curated sites. Please let me know which sites you prefer to access for either information or shareable content. I have made a further distinction for sites that are the result of either employee or community collaboration as they possibly differ from information sites in the degree of social participation (ie more social).
June 12, 2012 in Big Data | Tags: Analysis, BigData, collaboration, Curation, Data Definition, Data model, ILM, Knowledge, Knowledge Management, Lifecycle Management, Meaning, Networking, Open Source, Peter Drucker, Semantics, Social media, Social Media Value, Social Network, Tony Walker, Unstructured data | Leave a comment
The more we know the less we understand. Nowhere is this more true than on the Social Network, where volume, velocity, volatility and variability are increasing on a daily basis. Those 4 V’s are part of a definition of big data, which includes both structured and unstructured data. We may have a reasonable chance of obtaining valuable information from the structured data population. That depends, of course, on the extremity of any single one or combination of the 4 Vs, yet author, time stamp, location or any other tag that accompanies a communication is easily identifiable. Howerver unstructured data poses a challenge several orders of magnitude greater. Structured data benefits from data models, data definitions and rules that enable us to extract reports and analyses even to the point of discovering new relationships and information from the regimented data. To do so, we need to nurture and maintain these structures, to prevent a degradation of data quality and avoid conflicts and loss, a goal that often eludes the best efforts even in mature IT shops. However this is not the case for unstructured data.
In general there are no data models, no data definitions, no rules and no discipline of housekeeping for unstructured data in Social Media. At least nothing that is commonly held. Individually, of course, we have an idea of what we are communicating, and we probably use both our own data definitions as well as those we assume are being used by others in any conversation; but these are amorphous concepts and certainly nothing that can be referenced by others or by cyber analysis. The same is true to a lesser degree in IT organizations and the worlds behind the firewalls. At least in those environments best practices such as change management and planned organization of unstructured data (viz Sharepoint) should ensure some semblance of control and order if not insights into hidden information.
We do however have some rudimentary tools at our disposal, but like early man our technical bows and arrows are a poor match against the stampeding herd of beasts that is the social network stream. So like our ancient ancestors we have to develop strategies and skills that help us survive and thrive in this world of pervasive communications. Tony Wagner, author of “The Global Achievement Gap” identified three such skills that he believes are fundamental for us to foster and teach. He calls them the “three C’s – critical thinking, effective oral and written communication, and collaboration.” He also believes that this should be the prime focus of our educators, and that we should establish “a new National Education Academy, modeled after our military academies, to raise the status of the profession and to support the R and D that is essential for reinventing teaching, learning and assessment.”
Knowing how to perform the three C’s is therefor one of the keys to success. Being able to put this knowledge into practice, and bring organization and governance to bear on the resources and data requires additional skills if enterprises plan to approach and consume the labor and thoughts of distributed social resources.
Taking these observations a little further I believe the following 5 components are necessary in order to navigate, participate and collaborate in world of social information.
1. Understanding – we need a better understanding of what we are dealing with in the social media so that we can properly distinguish and farm target crops whether they are preferences, demographics, opinions, gossip, information, knowledge, wisdom. or something altogether different. However to improve that comprehension we need to be more aware of the dynamics of how we think, analyze, and communicate effectively. What, for example, is a thought, and what are the attributes of thought that make it consumable? We have a notion of answers to those questions but they are personal and subjective. Yet we cannot rely solely on subjective interpretation, so we need a shared and objective framework or model of knowledge. Knowledge is the loadstone of the social community, and the more we understand it, its nature, behaviors and properties the more we can improve the discovery, sharing and use of valued information in the social stream.
Peter Drucker(1909-2005), one of the most respected commentators on management theory and practice, believed that “knowledge worker productivity” would be the next frontier of management. Drucker was also famous for his quote “If you can’t measure it, you can’t manage it”, to which I would add the following prefix, “f you cant understand it, you can’t measure it.” Building a common understanding and framework(s) for knowledge management is essential in determining meaning, relevancy, relationship or other characteristics of information within contextual and cultural settings. We need to be able to detect when ambiguities and obfuscations are intended and make a documented judgement on meaning when they are not.
2. Networking – it might be stating the obvious to point out that people, individually and collectively, lie at the heart of the global social community. And it stands to reason that knowing who is who, and what they know is another fundamental layer needed for success. The size and complexity of big social data demands a superior set of skills that can identify, analyze, classify and then connect individuals to each other and their knowledge sets. I described this in my previous post Network Weavers which attempted to define the needed attributes (acquisition: filtration/review: association: curation: construction). As the dimensions of the network, the participants and their contributions grow so will the level of skills, and proficient network weavers will become more of a premium resource than they are today. It is likely that networkers will depend on directories, personal or even corporate at first, but increasingly the directories will become more public and entries will contain more social information such as skills, contributions, preferences and factors that others will be able to use to determine relevancy and fit for purpose.
3. Analytics -With improved understanding of knowledge and how we use and abuse it, we can approach analysis with a higher level of confidence in the accuracy of our observations. There are techniques and technologies that attempt to extract meaning from unstructured data but they still fall short of the human computer that is the brain when it comes to analyzing written and visual communications. As with humans machine semantics are bounded by self imposed rules and definitions, and like humans, communication is improved if there is an agreed set between participating bodies. If those rules and definitions remain hidden and obscured then the output can only be regarded as personal opinion. Rating the relevancy or social worthiness of an individual or entity against undisclosed rules and definitions has as much value as the street corner tipster who whispers a sure fire winner for any given horse race. Consequently social media demands semantic definitions that are shared amongst correspondents and a semantic analysis engine with the flexibility to parametrize selected characteristics so that relevancy can be tuned to group or community objectives.
4. Curation – In an earlier post, Curation – In Need of a Cure I raised the need for knowledge workers to approach the care and maintenance of Social Media information in the same way that enterprises manage their data through Information Lifecycle Management. It is not enough just to store knowledge as we do currently with Pinterest, Tumblr, scoop.it and others: beyond catching the item in our personal butterfly net, our efforts resemble little more than childhood scrapbooks of things that caught our interest and appetites. Curation is an excellent term for the housekeeping that needs to be performed on the captured knowledge data. In museums and art galleries curation is a highly sophisticated skill set that seeks to first isolate the item of knowledge, then to expand it with information about its provenance (where it came from) and pedigree (eg what school of thought), augment it with related content (supporting and detracting) and finally exhibit it to educate and edify an interested audience. Curation is an essential component in building a rich and relevant knowledge base, and can and often does lead to new insights and innovations.
5. Collaboration – Unlike “Field of Dreams” you can’t just build a field and expect the games to begin. All the understanding, networking, analyzing and curating will bring but small value if you keep it all to yourself. The key to success lies in participation. The more you contribute, the greater value you generate both for yourself and for your correspondents. The root of the word collaboration is “labor” , meaning work or effort, and the prefix “Co” means sharing. The more you share and contribute the more you will be rewarded by your involvement with the social network. You will be further rewarded as others do the same, whether its contributing common rules and definitions, understanding of knowledge and thought, the names and skills of great social network participants, or exemplary curation of well defined and related content. It is the act of collaboration that provides the secret sauce of success and bridges the resources and knowledge in the social stream. This is not theory: this is proven without any shadow of doubt by the open source community. If you get the opportunity, interact with an open source contributor, and ask them for guidance; they have been doing it effectively, efficiently and profitably for more than a decade.
WARNING: Please don’t attempt any of the steps above without clear and careful planning
- Search is Not Enough: Using Solr for Analytics (architects.dzone.com)
- Examples to help clarify what’s unstructured data and what’s structured? (parasdoshi.com)
- Tackling that unstructured data mess, practically (infocus.emc.com)
- Visualising The Future – New Techniques will revolutionise understanding and interpretation of ‘big data’ (blog.bt.com)
June Holley, author of “The Network Weaver Handbook”, was the guest on a recent #ideachat , hosted by @blogbrevity, where she conducted a spirited and vigorous discussion on the role of the network connector and collaborator whom she describes as a network weaver. June believes that this is something we all do, often without realizing it. The skills can be learned and improved, it’s all about how we are aware of and relate to each other. Ultimately we should be able to transform the world we live in. To a large degree this is true especially in small to medium sized communities. However scaling to the immensity of the Social Media Universe requires those skills to be refined, amplified and extended to the point where the role is highly specialized and potentially very much in demand.
The chart above is an attempt to summarize the collective input from the participants in #ideachat, none of whom contested the notion that network weaving was learn-able, necessary or trans-formative. Indeed the flow of positive thinking provided a tsunami of skills and activities that were deemed necessary network weaver attributes.
The receptor phase of weaving represents the intake of content, context and resources. This includes searches and information gathered from multiple sources in monitoring and participating in community conversations and chats. Acquisition is equivalent to sourcing in a supply chain and represents the raw intelligence needed to fuel productivity.
The review phase is the first stage of refining the raw intelligence. Analysis is the primary activity and is applied to understanding the meaning, authenticity and importance of content and resource.
The second stage of refinement is curation, taking the analyzed information and making it transparent to the served communities and the world at large. The refinement includes categorization (ie topics), classification, (eg value and relevancy) and commentary.
The third stage of refinement is associating resources with communities, content or most importantly with each other, understanding how to apply the relevancy of information and resources to each other. The third stage is also the mapping stage of the process, and is vital to the success of the network weaver. As the weaver’s reach extends to national or even global scale other maps from trusted weavers can be incorporate into the weaver’s sphere of connectedness.
Construction is the implementation phase of network weaving. It is establishing connections based on the refinement process, closing the triangle as June Holley describes it, between resources, communities and other network weavers. Here the weaver is more than just a connector they are catalysts to action and innovation, whether directly contributing or standing back and monitoring the resulting activity.
Central to all these activities and processes is the governing principle of Cultivation. This is the set of nurturing skills that separates the good network weavers from the great ones. Cultivation is farming or husbandry in its highest form, not just building connections but feeding them, nurturing them, strengthening them and understanding their needs. It means being endlessly curious, constantly vigilant and forever questioning to ensure that the woven networks are as efficient and healthy as possible.
It is within nearly everyone’s reach to acquire, analyze and curate information on the social network; billions of Tweets, blogs, circles and walls are testimony to these skills being learned and practiced on a daily basis. Everyone has the capability to imitate the African Weaver bird and weave their own network of resource and content. But it takes special skills to associate, construct and maintain vast networks of content and resource. It takes a proficient weaver to connect each nest in the tree, and a master weaver to connect all trees within a region, all regions within a country and so on across the language and geographic divides that impede global connectivity.
- Why A Community Manager Is The New Enterprise Rockstar (inforganiclabs.com)