Discovery is the Next Major Step of Awareness Beyond News Search

We are now entering the era where discovery is the next major step of awareness beyond search in our information economy.  Discovery enables a superior competitive awareness within a very small window of user/client attention.  The "routine" search for news and social media is transcended by real-time and actionable discoveries for individual decision makers.

Search has been the natural first step in Internet awareness.  Information utilities created massive databases of news, blogs, social media and tweets that were indexed for ease and speed of searching via keyword queries.  Search is easy and direct, and as time progressed, analytics became enhanced summaries of pre-loaded search queries for public relations and media monitoring dashboards - not in order to support decision makers & discovery.

News search is self limiting for decision makers:

• Search could never enable surprises, as it presumes that the searcher already knows for what he/she is searching.
• Decision makers do not know the scope/definition of what their interest universe should be.
• PR and media monitoring, often the source for organizational news awareness, are not tasked to discover strategic competitive insights.
• PR and media monitoring are often barriers to keeping unfavorable & important information from decision makers.
• Superior competitive discoveries are found in a large universe of potentially interesting topics - too many for any one decision maker to track alone.

In a natural progression to solve the limitations of search, most Internet utilities are adopting discovery as part of their user value.   Google, Amazon, Facebook, Twitter, LinkedIN, Apple and QUALCOMM are recent examples of major efforts to deliver some form of discovery to users.  One definition of discovery is "unexpected" information not the result of specific searching.   My definition of discovery is this:

Discovery - the measured and prioritized alerting to targeted recipients of relevant "news" and trends pertaining to their intelligence interests. 

This definition also includes situations where recipients do not know their own interests or are not actively seeking information on those interests.  Some further clarifications are useful:

• news - traditional real-time news articles, blogs, videos, audio, social media and tweets
• trends - emerging or dissipating patterns that are relevant for competitive success
• relevant - having a significant and demonstrable bearing for the matter at hand. Merriam Webster Dictionary
• interests -  participation in advantage and responsibility. Merriam Webster Dictionary
• intelligence - information pertaining to survival or competitive response
• targeted - discovery based on individual identities
• prioritized - highlighting in descending order of urgency or relevancy
• measured - limited and in proportion to urgency and attention capacity of the receiver
• alerting - interrupting the attention of the receiver in the minimal time required for a message to be delivered

From the perspective of our Discovery Patterns (aka News Patterns), we are transcending what can be searched from hundreds of millions of expert and community content creators.  We are furthermore transcending the incredible machine content from news database that enable ever more powerful searches and analytics of those searches.  With our unique pattern seeking algorithms, we are enabling our free network members and our private clients to discover relevant news and trends for which they could not or would not be searching.  Discovery enables a superior competitive awareness (situational awareness) within a very small window (bandwidth) of user/client attention.

Content Will Always Be King Among News Creators

News Patterns has been a pioneer in the realm of discovery for over eight years. In my next blog I will give a detailed description of discovery versus search. This blog post will first set the stage defining the different types of news content creators.

Expert and Community Content are obvious to most information professionals.  Simply stated, Expert Content is created by authors and organizations whose business is to professionally inform target audiences either as subscriptions or advertising driven business models.  Community Content is the socially driven content that creators write or record as part of relationships with other community members.

Machine Content is often not considered, but nonetheless invaluable in the news content universe.  Machine content essentially synthesizes new content with algorithms and database processes out of original content from Experts and Communities.  Here are some examples of machine content:

• Search indices with built in relevancy  (made famous by Google and "page rank")
• Aggregation pioneered by LexisNexis and Factiva
• Low latency feeds that supply instant stock trading black boxes
• Media monitoring dash boards that track public relations interests
• News Patterns Discovery - algorithmic pattern seeking of trends or events of competitive interest  (More on this in next blog addition.)

The above venn diagram illustrates that there are many overlaps among the news content categories.  Here are some examples:

• Expert journalists often search machine Google as part of researching articles.
• Members of community Twitter often notify their followers of interesting expert articles.
• Public relations professionals use their machine media monitoring dashboards of expert and community content to research their next press release.
• Expert journalist use community Facebook as an alternative mode of article distribution.
• Community LinkedIN redistributes expert articles in an attempt to increase site page views
• etc.

I have several concluding thoughts:

• Expert and Community content could operate as viable, and independent categories.
• Machine Content is completely dependent on Expert and Community Content.
• Simple forms of machine content (as in better searches and monitoring analytics) can easily be adopted by expert and community content creators; whereas the reverse of expert or community content cannot be readily created by the machine content category.  Therefore for machine content creators to maintain their market viability, they must be increasingly innovative in the user value that they add to the "content is king" news universe.

Update: Risk Topics Under the Surface of Financial Meltdown Radar

Earlier this year (February 5th of 2013) I wrote that our news patterning process had discovered the threat topics of Capital Controls and Bank Runs. With the recent events in Europe - Cyprus more specifically - our financial meltdown radar continues to track the growing disruption of these two threatening topics to world financial stability.

The swarm intelligence of thousands of news content creators, as discovered by our news patterning algorithms, saw these threats coming long before Cyprus became a news headline.