Technology Decisions Jun/Jul 2013 | Page 27

bridge the gap between insight and action
actionable meaning from reporting and
by supporting peoples’ natural decision-
analytics.
making processes.
2. Increasingly dispersed workforces have
heightened the need for, and benefit of,
Gartner has suggested that around 15 % of
fast information sharing and collabora-
BI deployments now combine traditional
tive decision-making.
BI and collaborative components in a single
3. The need to provide context to action-
decision-making environment. I believe this
able information to underpin accurate,
figure is extremely conservative. Or if it’ s ac-
fact-based decision-making.
curate, far too many vendors are hopelessly
behind the fast disappearing‘ ball’.
I’ ve also found that clients who embrace best
practice collaborative BI have the ability to
This demand for better enterprise collabora-
improve productivity and visibility across
tion, and collective decision-making capabili-
the breadth of organisational operations,
ties, is part of a wider spike in demand for
via enhanced knowledge sharing.
© iStockphoto. com / Er Ten Hong
CDM software. Analyst firm IDC reported a 5.1 % uptick in CDM software sales across 2012, making it the fastest growing global software segment last year.
More specifically, Wayne Eckerson’ s‘ Collaborative Business Intelligence: Optimizing the Process of Making Decisions’ report found that 87 % of BI professionals believe that“ col-
The need: why is collaboration important when using analytics?
The demand and drivers for collaborative BI
seem clear. But why is conducting effective
collaboration so important when attempting
to use BI content to make better decisions?
Information sharing and discussion allows
laboration tools can have a positive impact
human insight and understanding to be
when drawn together, enable a collective
on analysis and decision-making activities”,
added to data, making it more meaningful
decision to be made.
while“ Fifty-eight percent of BI professionals
and actionable.
plan to evaluate collaboration features when
BI veterans Claudia Imhoff and Collin
they purchase their next BI tools.”
Collaboration exposes the‘ why’ and‘ how’
White provide a useful definition of col-
behind data, helping us move beyond the
laborative BI,“ BI software enables business
According to InformationWeek’ s 2012 Busi-
simple‘ what’ and onto a deeper level of
users to report on and analyse business
ness Intelligence, Analytics and Information
analysis and comprehension. For example,
processes and associated activities, while
Management Survey, 59 % of IT pros list“ the
you might work for a sporting goods com-
collaboration tools enable the results of BI
ability for users to share and collaborate” as
pany assessing the monthly sales report. In
queries and reports as well as other related
the most important feature to consider when
one instance, that report might indicate a
information to be accessed and shared by
purchasing a BI solution. Only mainstays
massive jump in the sale of boxing equip-
multiple users.
“ ease of use”( 63 %) and“ fast data explora-
ment. By sharing data with all the relevant
tion”( 62 %) ranked higher.
business groups- Sales( did we have a focus
“ The ultimate goal of any BI environment is to make timely and better business
Drivers
on clearing boxing equipment this month?),
Marketing( did we run a campaign?), IT
decisions. A collaborative BI environment
Over the years I’ ve found three key factors
( do we have errors in our data sets relat-
supports team efforts to assess situations
driving client demand and interest in col-
ing to boxing equipment?)- we’ re able to
and make decisions.”
laborative BI:
determine not just what happened, but why
Demand for collaborative BI
1. The increased demand for real-time information, in conjunction with the
and how it happened. Collaborative BI is
about connecting the right people with
If executed correctly, collaborative BI has the
rapid expansion of corporate data assets,
the right data to maximise the effective-
ability to form crucial connections between
has resulted in organisations searching
ness of data analysis and achieve better
people, process, data and technology- to
for faster methods to share and derive
decision-making.
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