Redefining the Role of IT in a Modern BI World
IntroductionSince
its
inception
decades
ago,
the
primary
objective
of
business
intelligence
has
been
the
creation
of
a
top-down
single
source
of
truth
from
which
organizations
would
centrally
track
KPIs
and
performance
metrics
with
static
reports
and
da
Introduction
Since
its
inception
decades
ago,
the
primary
objective
of
business
intelligence
has
been
the
creation
of
a
top-down
single
source
of
truth
from
which
organizations
would
centrally
track
KPIs
and
performance
metrics
with
static
reports
and
dashboards.
This
stemmed
from
the
proliferation
of
data
in
spreadsheets
and
reporting
silos
throughout
organizations,
often
yielding
different
and
conflicting
results.
With
this
new
mandate,
BI-focused
teams
were
formed,
often
in
IT
departments,
and
they
began
to
approach
the
problem
in
the
same
manner
as
traditional
IT
projects,
where
the
business
makes
a
request
of
IT,
IT
logs
a
ticket,
then
fulfills
the
request
following
a
waterfall
methodology.
While
this
supplier/consumer
approach
to
BI
appeared
to
be
well-suited
for
the
task
of
centralizing
an
organization’s
data
and
promoting
consistency,
it
sacrificed
business
agility.
There
was
a
significant
lag
between
the
time
the
question
was
asked,
and
the
time
the
question
was
answered.
This
delay
and
lack
of
agility
within
the
analysis
process
led
to
lackluster
adoption
and
low
overall
business
impact.
The
emergence
of
self-service
BI
in
recent
years
has
challenged
the
status
quo,
especially
for
IT
professionals
who
have
spent
the
better
part
of
the
past
two
decades
building
out
a
BI
infrastructure
designed
for
developing
top-down,
centralized
reporting
and
dashboards.
Initially,
this
self-service
trend
was
viewed
as
a
nuisance
by
most
IT
departments
and
was
virtually
ignored.
The
focus
remained
on
producing
a
centrally-managed
single
source
of
truth
for
the
organization.
Fast-forward
to
today
and
IT
finds
itself
at
a
crossroad
with
self-service
BI
as
the
new
normal
that
can
no
longer
be
ignored.
The
traditional
approach
to
BI
is
becoming
less
and
less
relevant
as
the
business
demands
the
agility
that
comes
with
self-service
to
drive
adoption
and
improve
organization
outcomes.
This,
paired
with
the
continued
exponential
growth
in
data
volume
and
complexity,
presents
IT
with
an
important
choice.
Either
the
demand
for
self-service
BI
is
embraced,
and
IT
evolves
to
become
the
enabler
of
the
broader
use
and
impact
of
analytics
throughout
their
organizations,
or
it
is
ignored
and
IT
continues
as
the
producer
of
lower-value
enterprise
reporting
stifled
by
the
limitations
of
traditional
tools.
IT
professionals
who
are
ready
to
serve
as
a
catalyst
and
embrace
this
opportunity
will
deliver
far
greater
value
to
their
organizations
than
those
who
choose
to
ignore
the
real
needs
of
their
business
users
and
analysts.
As
organizations
begin
the
transition
from
a
traditional
top-down
approach
driven
by
IT
to
a
self-service
approach
enabled
by
IT
and
led
by
the
business,
a
new
framework
and
overall
strategy
is
required.
This
means
that
past
decisions
supporting
the
core
foundational
components
of
a
BI
program—people,
process,
and
platform—must
be
revisited.
Adjustments
are
needed
in
these
three
core
areas
to
support
the
shift
from
a
model
of
top-down
BI
development
and
delivery
to
a
self-service-based
modern
BI
model
which
is
driven,
and
primarily
executed
on,
by
the
business.
People
Self-service
analytics
does
not
mean
end
users
are
allowed
unfettered
access
to
any
and
all
data
and
analytic
content.
It
means
they
have
the
freedom
to
explore
pertinent
business
data
that
is
trusted,
secure,
and
governed.
This
is
where
process
comes
into
play
and
represents
the
component
that
requires
the
most
significant
shift
in
traditional
thinking
for
IT.
A
successful
modern
BI
program
is
able
to
deliver
both
IT
control
and
end-user
autonomy
and
agility.
A
well-established
and
well-communicated
process
is
required
for
an
organization
to
strike
this
delicate
balance.
A
top-down,
waterfall-based
process
only
addresses
the
IT
control
part
of
the
equation.
A
traditional
BI
deployment
focuses
primarily
on
locking
down
data
and
content
with
governance.
This
means
limiting
access
and
freedom
to
only
a
few
people
with
specialized
technical
skills
who
are
expected
to
meet
the
needs
and
answer
the
questions
of
the
many.
This
typically
involves
developer-centric
processes
to
design
and
build
the
enterprise
data
warehouse
(EDW)
model,
build
the
ETL
jobs
to
transform
and
load
data
into
the
model,
construct
the
semantic
layer
to
mask
the
complexity
of
the
underlying
data
structures,
and
finally
build
the
businessfacing
reports
and
dashboards
as
originally
requested
by
the
business.
The
unfortunate
reality
is
that
this
approach
often
fails
to
deliver
on
the
vision
and
promise
of
BI—to
deliver
significant
and
tangible
value
to
the
organization
through
improved
decision
making
with
minimal
time,
effort,
and
cost.
Top-down,
IT-led
BI
models
often
have
an
inverse
profile
of
time,
effort,
and
cost
relative
to
the
value
they
deliver
to
the
organization.
A
modern
analytics
solution
requires
new
processes
and
newly-defined
organizational
roles
and
responsibilities
to
truly
enable
a
collaborative
self-service-based
development
process.
IT
and
users
must
collaborate
to
jointly
develop
the
rules
of
the
road
for
their
secure
environment
that
each
other
must
abide
by
in
order
to
maximize
the
business
value
of
analytics
without
compromising
on
the
governance
or
security
of
the
data.
IT’s
success
is
highlighted,
and
its
value
to
the
organization
realized,
when
the
business
can
realize
significant
value
and
benefit
from
investments
in
analytics
and
BI.
Should
IT
still
be
considered
successful
even
if
not
a
single
end-user
utilizes
the
BI
system
to
influence
a
single
business
decision?
Traditional
processes
intended
to
serve
top-down
BI
deployments
are
too
often
measured
by
metrics
that
are
not
tied
to
outcomes
or
organizational
impact.
If
the
ETL
jobs
that
IT
created
ran
without
failure
and
the
EDW
was
loaded
without
error
and
all
downstream
reports
refreshed,
many
IT
organizations
would
consider
themselves
successful.
Merely
supplying
data
and
content
to
users
without
any
regard
for
whether
or
not
it
is
adopted
and
provides
value
through
improved
outcomes
is
simply
not
enough.
Modern
BI
requires
updated
processes
to
support
self-service
analytics
across
the
organization.
It
also
requires
the
definition
of
new
success
metrics
for
which
IT
and
the
business
are
jointly
accountable
and
are
therefore
equally
invested.
Where
processes
and
technology
intertwine,
there
is
tremendous
opportunity.
Technical
innovations,
especially
with
AI,
will
continue
to
make
it
easier
to
automate
processes
and
augment
users
of
all
skill
levels
throughout
the
analytics
workflow.
And
while
process
can
accelerate,
rather
than
inhibit,
successful
analytics
outcomes,
it’s
important
to
recognize
that
this
relies
on
a
system
and
interface
that
people
are
eager
to
use.
If
processes
aren’t
supported
by
the
right
platform
choice,
they
will
stifle
adoption.
Platform
Since
BI
has
been
historically
viewed
as
an
IT
initiative,
it
is
not
surprising
that
IT
drove
virtually
every
aspect
of
platform
evaluation,
selection,
purchasing,
implementation,
deployment,
development,
and
administration.
But
with
drastic
changes
required
to
modernize
the
people
and
process
components
of
a
BI
and
analytics
program,
IT
must
change
the
criteria
for
choosing
the
technology
to
meet
these
evolving
requirements.
Perhaps
the
most
obvious
change
is
that
IT
must
intimately
involve
business
users
and
analysts
from
across
the
organization
in
evaluating
and
ultimately
deciding
which
modern
platform
best
fits
the
organization
and
addresses
the
broad
needs
of
the
users.
For
more
information
on
selecting
the
right
analytics
platform,
check
out
the
Evaluation
Guide.
A
modern
platform
must
address
a
wide
range
of
needs
and
different
personas
as
well
as
the
increased
pace
of
business
and
the
exponential
growth
in
data
volume
and
complexity.
IT
requires
that
the
chosen
platform
enables
governance
and
security
while
end
users
require
easy
access
to
content
and
the
ability
to
explore
and
discovery
in
a
safe
environment.
The
chosen
platform
must
also
be
able
to
evolve
with
the
landscape
and
integrate
easily
with
other
systems
within
an
organization.
A
centralized
EDW
containing
all
data
needed
for
analysis,
which
was
the
cornerstone
of
traditional
BI,
is
simply
not
possible
in
the
big-data
era.
Organizations
need
a
platform
that
can
adapt
to
an
evolving
data
landscape
and
insulate
users
from
increased
complexity
and
change.
The
most
critical
aspect
is
the
ability
to
meet
these
diverse
needs
in
an
integrated
and
intuitive
way.
This
integration
is
depicted
on
the
following
page
as
the
modern
analytic
workflow.
The
diagram
highlights
the
five
key
capabilities
that
must
flow
seamlessly
in
order
for
the
three
personas
depicted
in
the
center
to
effectively
leverage
the
platform.
The
BI
and
analytics
platform
landscape
has
passed
a
tipping
point,
as
the
market
for
modern
products
is
experiencing
healthy
growth
while
the
traditional
segment
of
the
market
is
declining
with
little
to
no
net
new
investment.
IT
leaders
should
capitalize
on
this
market
shift
and
seize
the
opportunity
to
redefine
their
role
in
BI
and
analytics
as
a
far
more
strategic
one
that
is
critical
to
the
future
success
of
the
organization.
Adopting
a
collaborative
approach
to
recast
the
foundational
aspects
of
the
BI
program
and
truly
support
self-service
is
the
key
to
changing
the
perception
of
IT
from
a
producer
to
a
strategic
partner
and
enabler
for
the
organization.
Promise
In
today’s
era
of
digital
transformation,
IT
leaders
are
increasingly
expected
to
take
on
digital
business
initiatives
in
their
organizations,
including
identifying
cost
savings
and
finding
new
revenue
streams.
Realizing
the
value
of
data
for
these
transformational
efforts,
many
businesses
are
modernizing
and
increasing
their
analytics
investments
to
innovate
and
accelerate
change.
Everyone
agrees
that
putting
data
at
the
center
of
conversations
promises
change.
However,
most
organizations
are
failing
to
successfully
implement
an
enterprise-wide
analytics
program.
IT
is
well
positioned
for
a
leadership
role
in
these
efforts,
and
is
essential
for
the
task
of
giving
people
the
relevant
data
they
need
for
decision-making.
Modern
analytics
shifts
IT’s
role
to
a
more
strategic
partner
for
the
business,
empowering
users
to
navigate
a
trusted,
self-service
environment.
But
beyond
access
to
the
data,
everyone
needs
the
motivation
and
confidence
to
properly
make
decisions
with
it.
You
need
to
identify
the
relationships
between
job
functions
and
data
and
change
behaviors
that
run
deep
into
the
fabric
of
your
organization’s
culture.
This
also
means
expanding
your
definition
of
self-service
so
that
business
users
participate
in
some
of
the
traditionally
IT-led
responsibilities
associated
with
data
and
analytics—like
administration,
governance,
and
education.
With
the
right
processes,
standards,
and
change
management,
business
users
can
help
manage
data
sources,
analytics
content,
and
users
in
the
system,
as
well
as
contribute
to
training,
evangelism,
and
the
internal
community.
When
users
value
and
participate
in
these
efforts,
IT
can
manage
strategic
initiatives
like
business
SLAs
and
ensuring
the
security
of
company
assets.
Although
every
organization’s
journey
to
building
a
data-driven
organization
will
differ,
achieving
your
transformational
goals
requires
a
deliberate
and
holistic
approach
to
developing
your
analytics
practice.
Success
at
scale
relies
on
a
systematic,
agile
approach
to
identify
key
sources
of
data,
how
data
is
selected,
managed,
distributed,
consumed,
and
secured,
and
how
users
are
educated
and
engaged.
The
better
you
understand
your
organization’s
requirements,
the
better
you
will
be
able
to
proactively
support
the
broad
use
of
data.
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