The “database of now” demands a quick understanding of
data, particularly in context. There are many opportunities in understanding or
misunderstanding data in terms of the contexts they participate in, or are
connected to, or connected at the edge of a data neighborhood. Because each
context has its own unique vocabulary, you can see the opportunity for misconnects
in meaning by not understanding the full context of any statement or set of
proven facts.
If someone says, "I like the blue one", how can
you evaluate what that means? If it is a swimsuit on the beach, it means one
thing, if it's a lobster from the same beach, that means a totally different
thing. Context is what gives data real meaning. There are three primary forms
of context that help understand the true meaning of the base data. One is the
real world contextual meaning, the other is the contextual business meaning,
and the other is the technical contextual meaning. Obviously, finding meaning
in big or monster data is a challenge, but that difficulty increases as the
speed increases, particularly if the data is hard to manage or access.
Figure 1
Representation of Interconnected Contexts.
Real-World Context
Data has meaning in terms of its definitional
domain. When you mention "blue", usually comes from the color domain.
However, in the world of mental health, it means a kind of feeling or mood. So
understanding the base context in which a data element exists is essential. If
blue is associated with a human context, it could be physical and mean a lack
of oxygen. It could also mean that the person is adorned in something blue.
This is usually cleared up by understanding the base subject or entity that the
data element is associated with by having a precise name, meaning, and basic
subject area association. Underlying meaning can be tricky when just
looking at the data value alone. Having proper meta-data and associations is
the ideal solution to this problem.
Business Context
The contextual areas that relate to business fall into three
basic categories of meaning. Every data item or group of data items needs to be
viewed in terms of the context they are being viewed in or from. Internal
contexts where the vocabulary is understood in the context of the internal
organization and defined within a particular organization. These internal
contexts usually revolve around the organization and skill definitions. There
is also the external context that represents the outside world irrespective of
the organization itself. The third context is where the outside world touches
the internal world. Listed below are the typical contexts in each of these
categories:
Common External
Contexts:
Communities, Brand/Reputation, Public, Legal Frameworks/Courts,
Geographical Regions, Countries, Local Culture, Governmental Agencies, Industries,
Dynamic Industry 4.0, Value Chains, Supply Chains, Service Vendors, Markets,
Competitors, Prospects, and Competitors Customers.
Common Internal
Contexts
Organizational Culture, Goals, Constraints, Boundaries, Actual
Customers, Products, Services, Suppliers, Employees, Contractors, Departments,
Divisions, General Accounts, Contracts, Physical Infrastructure, Technical
Infrastructure, Properties, Investments, Intellectual capital, Business
Competencies. Knowledge, Skills, Patents, Success Measures and Statements
Common Interactive
Contexts:
Marketing Channels, Advertisements, Customer Journeys,
Customer Experience, Loyalty, Satisfaction Scores, Processes, Applications,
User interfaces, Websites, Webpages, and System Interfaces.
Technical Context
Data must also be understood in terms of physical contexts,
limitations, and potential lag times. Data sources need to be understood in
terms of their currency and ability to be integrated easily with other sources.
While many views, interactions, and integrations work well at the logical
level, physically, they may not be ready in terms of near-real-time
capabilities, transformation potential, or performance levels on or off-prem.
While meta-data may exist to understand possible joins and combinations,
executing them fast enough to be useful in multiple business contexts may not
be possible. The physical data types and file storage mechanisms may not be
conducive to the demands of new usage scenarios. New low lag databases that are
near real-time will become the standard, going forward.
Net; Net:
Data, information, knowledge are quite dependent on the
context(s) they participate in or the perspective they are viewed from. Often
Knowledge worlds interact; therefore, meanings can overlap and connect in ways
that are essential for ultimate understanding, manipulation, or utilization.
Knowing the context of your data is absolutely critical for leveraging
understanding. All of this is happening
at greater speeds approaching the “database
of now” speed necessary to make critical decisions, actions, adjustments or
improvements.
