Information and analytics hub
transactional data stores. While those traditional
systems are and will remain important, retail
organizations are working to manage data flows
that are growing in speed, volume, and complexity.
but thus far the efforts to do so have been
fragmented and less than comprehensive.
Retailers must handle information from familiar
sources such as point of sale and enterprise
resource planning systems. Those traditional
sources are now augmented by information
flowing from customer sentiment research,
social media sites, customer service interactions,
and proliferating digital marketing channels.
Handling unstructured data presents unique
difficulties. Previous-generation centralized
enterprise data warehouses are not well
suited to the mining of unstructured data.
Instead, unstructured data should be housed
in federated information locations, then
harmonized and assimilated on the fly���after
which it can be mined to produce insights and
actionable intelligence in a retail context.
Those latter sources typically contain large and
growing volumes of unstructured data, often
in the form of chat, voice, and video files, and
other non-standard forms. Research suggests
that as much as 85% of information available
to a typical organization is unstructured data.
Retailers certainly want to access and understand
this growing store of unstructured information,
The now-emerging information and analytics
hub model was developed specifically to meet
those next-generation data management and
analysis requirements. A modern information
and analytics hub consists of separate tiers,
which manage data, business intelligence, and
delivery, and elements to coordinate business
rules and metadata.