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FINAL WORD
data virtualisation technologies. Data
virtualisation can be used to implement a
data access layer, which gathers together
the underlying data and prepares it for
analytics tools. This is a technique that
offers accurate, reliable data in real time
with no unnecessary data replication,
reducing the cost of out-of-sync reports. In
this way, organisations can achieve greater
productivity and efficiency, unlocking data
from silos into unified information at the
speed of business.
A great example of a business taking
advantage of its wealth of data is the Royal
Bank of Scotland (RBS), which implemented
a data access layer to support real-time
data decisions. Many financial services
organisations are faced with needing to
capture more sophisticated information from
different systems such as transactional, risk,
ledger and static and be able to deliver it
over the right channel in the right way. As an
outcome, business intelligence teams with the
right tools can mine this data and gain insights
that can add great value to the business and
the experience delivered to customers.
The way I see it, integration middleware
is the key for a modern architecture and
the organisations that recognise its strategic
value can gain a head-start on the road
to digitalisation. n
takes 60 seconds. For Telegraph Media
Group, a middleware-powered integration
platform has simplified the launch of
compelling new digital content to audiences
on desktop, tablet and smartphone
devices. And for King’s College Hospital,
the NHS Foundation Trust brought in a new
integration platform to enable more than 50
hospital systems to exchange critical patient
information quickly and reliably.
Let’s talk about data
The API economy is helping democratise
access to data and services, presenting a
great opportunity to better connect people
and enterprises. When CIOs are looking at
becoming a connected enterprise, they should
be starting to think about data as a first-class
citizen. Many organisations are sitting on
treasure troves of information, if they can
only process and contextualise it across a
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variety of platforms. No longer suffering from
a lack of data, the challenge for businesses
is a lack of the right data at the right time;
they are dealing with spiralling bits and
bytes generated from multiple sources such
as social media and connected devices. As
such, those responsible for IT or applications
should start treating data less like a static
warehouse and more as a dynamic data
fabric. Traditional integration approaches
involve the cost and complexity traditionally
associated with data warehouses or data
pipeline techniques (traditional ETL – extract,
transform, load – processes).
However, with a data fabric, organisations
get just-in-time processing capabilities
that can bring together data from multiple
sources, easily accommodate new sources
and move away from traditional silos.
Again, they can look to use middleware
technologies to do this, in the shape of
“
THOSE
RESPONSIBLE
FOR IT OR
APPLICATIONS
SHOULD START
TREATING DATA
LESS LIKE A STATIC
WAREHOUSE
AND MORE AS A
DYNAMIC DATA
FABRIC.
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