INTHEBLACK September 2021 - Magazine - Page 43
Right: Sanjay Panjabi,
Deloitte & Touche Singapore
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Employee-led adoption: Panjabi says that,
although commitment from the leadership is one
of the key drivers in adoption of data culture, what
has really helped his firm to execute the strategy
successfully was to bring in different skill sets and
capabilities across all functions in the organisation
that meet the requirements and challenges.
“I usually use the concept of QEV, which stands
for establishing quality, efficiency and bringing
value, not just to ourselves, but also to our clients,
stakeholders and employees.”
He recommends looking beyond solving client
problems to pay attention to the needs of employees
as well.
“If employees see how data can help them do their
work better and more efficiently, and they can see an
immediate benefit to it, then they will get on board
immediately.”
Start small, then scale up: While organisations
should set a long-term vision on how data can be
used across the business, they also need to take
a step-by-step approach to implementation.
“Starting small allows organisations to gradually
roll out the use cases across the organisation,
measure the success of these cases, refine the existing
cases, up-skill talent and move on,” Panjabi says.
“In addition, with current technology capabilities,
such as cloud analytics, organisations do not need to
allocate a huge sum of investment up front for the
analytics platform – they can also gradually
scale up the platform as the use cases grow.”
Tell a story: Wise says building a data-led
culture requires constant storytelling and relentless
communication of the value of data up and down the
organisation and across departments.
“You’ve got to frame an abstract statement like
‘Data literacy is important’ to something more
concrete, like ‘Solving business problems with data’.”
When people begin to believe in the data, it is a
game changer. Wise recommends using messaging
that makes data literacy personally relevant to
individuals and teams.
“Unless the connection back to the ‘Why’ is
understood by everyone in the business, business
leaders are going to have a hard time encouraging
people to adopt more data-led insights into their
decision-making habits,” she says.
Wise says business problems need to be front of mind,
because making sense of data can get very convoluted,
very abstract and very technical quite quickly.
RIGHT PEOPLE, RIGHT SKILLS
Panjabi recalls that when data analytics was first
applied in the audit and assurance function at
Deloitte’s Singapore practice, they were the early
adopters. “Being auditors, we were not data science
people. We made sure that we had a team of people
who had the necessary skills that complemented
each other,” he says.
“When we started out many years back, we
merged two sets of people – technical people, and
business and audit people. As we move into the
future, these two sets will become just one person
having these multiple skills.”
Ong says empowering in-house talent with data
capabilities such as robotic process automation, data
analytics and visualisation tools helps kick-start the
data-driven culture.
“When people have the skills, you can drive
innovation initiatives that encourage talent to
consider how these capabilities can improve their
work performance or drive insights.”
TOOLS FOR
THE TASK
Business intelligence (BI)
tools are types of
application software that
collect and process small
or large amounts of
unstructured data from
internal and external
systems, including
documents, CRM, images,
files, email, video and
other business sources.
Giving teams access to
the right BI tools makes
it simpler and less
intimidating to gather the
right data and visualise it
in meaningful ways.
“Selecting the right BI
tool depends on the volume
and complexity of data, size
of the organisation, skill set
and the problems you are
trying to solve,” says
Chelsea Wise.
Sanjay Panjabi adds,
“For small organisations,
or ones in the early stages
of data collection, tools
like Excel analytics and
customer analytics
platforms such as Google
Analytics can probably
achieve the objectives
in an affordable way."
Popular BI tools:
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M
icrosoft Power BI
T
ableau
C
ognos Analytics
Q
lik
T
houghtSpot
S
isense
G
oogle Analytics
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