Search vs. Discovery: How Are They Different?
Google, Microsoft, et al continue to perfect their search engines
– but too often search is not enough. The watchword today is “discovery” –
where you don’t just search for information, but information finds you.
Why is discovery better than search? One reason is because of the
overload of data – both online and in-house. Searching for something, whether
via Google or using scripts written by programmers that peruse huge data
storage repositories, requires you to invest a great deal of time and effort in
order to ferret out the results you really need from a huge amount of
non-relevant data – because the context of what you are searching for is not
always clear. Discovery – where a system proactively presents to you what you
are really looking for based on the context of what you are doing or searching
for – is the Next Big Thing in data usage.
When you add context to search, you get discovery – a system where
the data you need is delivered based on materials you are examining, where you
are, what app you’re using, the type of device you are using (i.e. screen size
and input tools), whether you are moving or stationary, etc. Done this way,
discovery saves a huge amount of time, effort, and resources over search.
Search is fine for quick, specific answers, but awful at discovering and exploring
new ideas. Discovery reveals worlds you didn’t know existed.
What Discovery can Discover
Practically speaking, how are search and discovery different? One
way of discovering information is via “recommendations,” such as those provided
by Netflix and Amazon. Often, these are just “other people who bought
what you bought also bought this” types of recommendations – but when machine
learning is applied to the process, the potential accuracy of a recommendation
engine is enormous, with the engine figuring out that if you bought one product
(ie, hot dogs) you would likely be interested in others (hot dog rolls,
charcoal, soft drink six packs, beach toys, etc.).
The heart of such a discovery/recommendation engine is the
“knowledge graph” which is a data graph that exhibits the relationships between
topics. By examining the context, the engine “knows” that if you buy product A,
there’s a good chance you will need product B, C, etc. – and it will bring
those results to you. Business intelligence tools, like PowerBI, also enable
people to discover things from large amounts of data by using visualization to
show patterns where just searching for data wouldn’t reveal ‘the big picture’.
A discovery system would also respond to location/usage context.
For example, if you are using an AR headset (like Hololens) to repair something
like an elevator, the system projects information relevant to the repair on the
screen without having to search for it specifically. Other examples and
contexts would include conversations on Slack or other connectivity
applications: If you are engaged in a marketing discussion on an “upcoming
company meeting” with a client, and someone in another department had a conversation
on the same topic, the discovery system could suggest checking out that
conversation.
The same could apply to any other activity in an organization –
sales teams discovering work done by others on the same account in the past,
engineers getting insight from other teams working on product design, finance
departments being informed of the newest regulations regarding salaries and
benefits, etc. Instead of being passive and becoming activated only when called
upon – like search – discovery is proactive, delivering desired information
when needed. That is the power of discovery – and that is what search should
eventually evolve into.
Moving Towards Discovery: Practical Steps
How can that evolution take place? To “discover,” a system needs
to be able to understand what we are looking for and how it is going to be
applied – supplying relevant information when it is called upon to do so, or
even automatically, such as in the airplane repair context mentioned. To do
that, a number of strategies could be applied, such as artificial intelligence
or natural language processing.
With the latter, for example, an analysis of the use of language
in a document, conversation, script, or any other context would enable the NLP
system to “understand” what is being discussed based on language, phraseology,
sentence patterns etc. At that point, the discovery system just needs to parse
through data repositories for the relevant information, based on the criteria
the NLP system described as being relevant. An AI system using machine learning
would work the same way; analyze what is being discussed or written about and
look for similar patterns of content or links used previously in other
documents, files, conversations, videos, etc., presenting the relevant
results and ignoring the rest.
Figuring out how to navigate data has become a major challenge in
organizations today. According to a study by
RingCentral, employees lose as many as 32 business days a year just
switching between applications, folders, windows, and databases in order to
find the information they need. If things continue as they have – with
companies stockpiling even more data, spread out over more and bigger data
repositories – expect that number to balloon.
Organizations really have no choice: With the amount of data set
to grow to 175 zetabytes by 2025 – 61% more than in 2018 – finding data is
going to become a greater challenge than ever. To keep organizations
functioning, search must enter a new phase of presenting information before users
even think to look for it: discovery.
Source | https://dataconomy.com
Regards
Prof. Pralhad Jadhav
Master of Library &
Information Science (NET Qualified)
Senior Manager @ Knowledge
Repository
Khaitan & Co
Twitter
Handle | @Pralhad161978
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