Analytics Culture: The Secret to Success

Analytics Culture: The Secret to Success
It’s not just about data, technology, and quants

Given the buzz around analytics, why are so few
organizations getting it done? According to a recent
Accenture survey of 600 executives, 8 out of 10
companies have not achieved their goals in analytics.
And only 1 out of 12 respondents expressed
satisfaction with the return on their investments. 1
2

A quick scan of how high-performing
organizations use analytics answers
that question. At Harrah’s, now
Caesars Entertainment, service
delivery metrics, such as the average
time it takes to greet a customer or
to deliver a drink, are reported to
management on Sunday. The very next
day, the property’s general manager
can expect a call if the revenues are
down compared to the same week the
previous year. 2
After an analysis of its loyalty
program data showed that 7% of
customers were responsible for 43%
of its sales, Best Buy redesigned its
store layout to meet the needs of
those loyal customers. Further, this
giant retailer quantified the value of
employee engagement to customers’
in-store experience — a 0.1% increase
in engagement is worth more than
$100,000 in a store’s annual income —
to inform its investments in the
workforce. 3
Quick action is a distinguishing
feature of examples such as these.
High performers do not simply gather
and analyze data; they use the
resulting insights to make smarter
decisions faster. Their leaders are in
synch on how to employ analytics
in the service of their strategy, and
that vision percolates down through
the organization. As a result, middle
managers measure the right metrics,
make decisions based on the best
data available, and understand the
significance of immediate action on
the basis of those decisions.
No technological solution, simply
layered on top of existing processes
and culture, can achieve these results.
Further, existing analytical talent in
organizations rapidly grows frustrated
with the added complexity. As one
market researcher said, “We buy tons
of data on the consumer products
market. We analyze the hell out of
it. The problem is, we don’t change
anything as a result of it.” 4
Access to robust data is essential to
progress toward analytical maturity.
The value of the data is unlocked,
however, when companies develop
the capabilities to analyze what
they gather. Do your employees
know how to use scenario-based or
workflow analysis tools? Can they
overcome organizational barriers
and build momentum behind their
positions? Capabilities such as these
are necessary to drive transformative
change fueled by analytics.
As talent is hired and capabilities
expand, pockets of analytical
excellence develop in companies.
Typically, however, these pockets
produce little of strategic value
because their scope is bounded by
their unit or department. Opportunities
that require multifunctional
involvement are missed.
Contrast that scenario to one of a
high-performing organization with
a culture that understands and
celebrates the capabilities required to
win with analytics. In a culture such
as this, respect for data coupled to a
pervasive curiosity results in people
asking questions such as, “Do we think
this is true, or do we know?” 5 This
is the key to success — the “secret
sauce” — of analytics competitors. Due
to the hard work required to build and
maintain such an analytics culture,
succeeding in this endeavor raises the
table stakes in the market. Accenture
uses the term “organizational
effectiveness” to structure the
multifaceted endeavors required to
foster an analytics culture.
One facet of organizational
effectiveness, for example, is
promoting and reinforcing top leaders
with analytical vision, passion, and the
ability to nurture leaders at all levels.
Another is developing, engaging, and
organizing talent with the right skill
sets. The design of the operating model
and the processes used to problem
solve and execute at a strategic level
are covered by this term, as are the
processes and software required to
embed analytical tools, methods, and
behaviors. The magic occurs when the
interpersonal and process strengths of
an innovative, results-focused culture
are combined with the technical and
data-mining skills required to deliver
high performance.
3

Fostering a High-Performing
Analytics Culture
How do you get it done? What steps will help you
progress along the path to analytic maturity?
Three areas — leadership, breaking down silos, and
developing and keeping talent — are fundamental to
fostering an analytics culture.
Leading from the top and
the middle
The single most important step you
can take is to promote leaders with a
passion for data analysis at every level.
Leaders in the C-suite need to model
appropriate behavior, but they do not
own analytics in the organization. Every
manager and leader in the middle ranks
has to take responsibility for creating
a more fact-based culture because
through ownership comes commitment.
It is also important for executives to
take a hard, honest look at how in touch
they are with the existing culture before
they attempt to drive transformative
change. On a recent Accenture survey,
leaders in 400 organizations responded
favorably to statements such as “This
organization places a high value on
collecting objective data to improve
the quality of decision-making” and “In
this organization, you get ahead based
on merit and objectively demonstrated
performance rather than political
behavior.” In all but 2 of these 400
organizations, employees answered
4
these questions in a very different
way from their leaders. 6 This is no less
true for changes driven by analytic
transformations.
Having an accurate understanding of
their organization’s readiness allows
senior leadership to assess gaps and
define a path forward to creating an
analytics culture. And this effort, in
turn, helps them to get in synch with
each other regarding how analytics
will be used to support their strategic
vision — the value they want to gain.
By translating that consensus down
through the middle ranks, leaders can
confer ownership of analytics to the
appropriate people and thereby avoid
what we call “the frozen middle.”
Often an effective approach to achieving
analytics goals is to recognize how
factors play together. As Tom Anderson,
CEO of Integrity Interactive, has said,
“The beauty of analytics is that you find
lots of things that can be incrementally
improved. If it’s a multiplicate business,
[like] medical finance, and you can
improve each factor—the number of
doctors times the number of patients
times the percentage that seek
financing—by 10 percent, it’s huge.” 7
Harrah’s took a similar approach by
recognizing the role of a number of
service delivery factors in a customer’s
satisfaction. Tracking each of these
factors, such as the time required to
greet a customer or deliver a drink,
allows them to be targeted separately if
revenues slip.
Breaking down silos
Like unengaged leaders, organizational
silos are quick killers of an analytics
culture. Silos naturally develop as
organizations grow. Analytics in the
service of the enterprise, however,
requires cross-functional collaboration
— what one UK-based healthcare
company calls “boundaryless”
collaboration. To address this tension,
organizations need processes to
facilitate people working together
from all parts of the organization. For
example, they need to decide who will
lead these cross-functional analytics
efforts. And ask, “How do we help our
people balance the interests of their unit
versus the interest of the enterprise?”

Figure 1. Closed loop decision making process
Performance
monitoring
Weekly
forecast
adjustment
Monthly
business review
Questions on
key metrics
Quarterly
health check
Marketing Supply chain HR Manufacturing Finance
Analytics center of excellence
Core analytics
Functional analytics
Cross functional analytics
Technology Enablers
Bland packaged workbench Root cause analysis tools
Statistical models
optimization tools
Insight generation
Value realization Execution
Insight validation
The products of collaboration can then
be applied in different parts of the
organization. Procter & Gamble is an
example of an analytics high performer
that has established a central team
to contribute to the bottom line in a
variety of disciplines. This team, called
Global Analytics, tackles challenges
such as manufacturing site location,
inventory management, supply chain
design, and strategic decision making. 8
Creating a single repeatable process for
integrating analytics into everyday work
would be a powerful way to counteract
the rigidity of “silo-think.” A closed
loop decision making process such as
that shown in Figure 1 defines how
data is leveraged to test hypotheses
and support decisions anywhere in the
enterprise. Recent Accenture research
shows that only one in five companies
currently has such a repeatable process
in place. 9
Breaking down silos also facilitates the
collaboration required to stay ahead
of the competition. For example, as a
strategic partner rather than an order
taker, IT can help business units access
the quality data needed to forecast more
accurately, price more appropriately, and
tailor offerings to customers or citizens
more effectively. Just as important
is evaluating how other companies,
even in different industries, are using
analytics. Agile organizations know
they don’t have to do all the analytics
heavy lifting themselves — there is a
global analytics community brimming
with experts, peer discussions, and case
studies.
Selecting, motivating, and
retaining talent
A smattering of quants around an
organization is not an analytics culture.
Upskilling the workforce in analytic
capabilities is quickly becoming essential
just to keep pace with market.
As Tom Anderson has said, “You have to
become a teacher. Some people already
have the problem-solving capabilities,
and you have to teach them the math.
Others know the math, but don’t know
how to apply it to business problems.” 10
Training workers in IT skills has
consumed the organization’s training
departments in the past; the next 20
years will be about integrating analytics
into everyday work.
The bar is also rising for new hires. One
financial services company, for example,
requires all potential employees,
including senior executives, to take a
series of tests to determine analytical
and financial aptitude. One successful
hire joked that he might have been “the
only HR guy who could pass their math
test.” 11
Analyzing the talent in the organization
is as important as hiring talent with a
passion for analytics. This application of
analytics can provide a clear advantage
to companies that use customer
satisfaction as their differentiator.
Harrah’s, for example, analyzes the
effects of its health and wellness
programs on employee engagement.
In this way, the company showed that
a rise in preventative care visits to its
on-site clinics resulted in an annual
decrease (by millions of dollars) in
urgent-care costs. 12
5

One reason Harrah’s chose to capture
wellness metrics is because its
leadership team understands that
happy, healthy employees provide
better customer service. Gathering
this data provides insights on revenue
growth as well as on health insurance
and sick days. It is an example of the
value of selecting the right metrics,
however seemingly unrelated, to
help make decisions that support the
corporate strategy.
Another use of talent analytics is to
retain high-performing employees.
Google has placed sufficient strategic
importance to talent retention that
its people analytics function has a
staff of 30 researchers, analysts, and
consultants. Among the tools that this
function has provided Google is a list
of eight managerial behaviors that
help them inspire and develop their
employees.
6
As Laszlo Bock, Google’s VP of
people operations, says, “It’s not the
company-provided lunch that keeps
people here. Googlers tell us that
there are three reasons they stay: the
mission, the quality of the people, and
the chance to build the skill set of a
better leader or entrepreneur. And all
our analytics are built around these
reasons.” 13
An analytics culture is necessary
to drive transformative change,
and deliver on the full potential
of your analytics investments. A
prerequisite for high performance
is being organizationally ready to
make faster, smarter decisions and to
drive cross-departmental ownership
of the implementation of those
decisions. While there are multiple
facets involved in organizational
effectiveness, getting the right leaders
in place, breaking down silos, fostering
the necessary culture and developing
your talent are good places to start.

References
1 Dave Rich, Brian McCarthy, and
Jeanne Harris, “Getting Serious About
Analytics: Better Insights, Better
Outcomes.” Accenture, 2010.
2 Walter E. Shill and Robert J. Thomas,
“Exploring the Mindset of the High
Performer,” Outlook Journal, October
2005.
3 Jeanne Harris, “How to Turn Data into
a Strategic Asset,” Outlook Journal,
2010.
4 Thomas H. Davenport, Jeanne G.
Harris, and Robert Morison, Analytics
at Work: Smarter Decision, Better
Results. Harvard Business School
Publishing 2010.
5 CEO Gary Loveman, Harrah’s
Entertainment, a casino and hotel
empire, quoted in Dave Rich, Brian
McCarthy, and Jeanne Harris, “Getting
Serious About Analytics: Better
Insights, Better Outcomes.” Accenture,
2010.
6 Accenture’s High Performance
Culture Research.
7 Quoted in Thomas H. Davenport,
Jeanne G. Harris, and Robert Morison,
Analytics at Work: Smarter Decisions,
Better Results. Harvard Business
School Publishing 2010.
8 Jeanne G. Harris, “Research Note:
How Consumer Goods Companies
Compete on Analytics to Achieve High
Performance,” Accenture Institute for
High Performance Business, 2007.
9 Dave Rich, Brian McCarthy, Jeanne
Harris, “Getting Serious About
Analytics: Better Insights, Better
Outcomes,” Accenture, 2010.
10 Quoted in Thomas H. Davenport,
Jeanne G. Harris, and Robert Morison,
Analytics at Work: Smarter Decisions,
Better Results.” Harvard Business
School Publishing 2010.
11 Thomas H. Davenport and Jeanne
g. Harris, Competing on Analytics: The
New Science of Winning. Boston, MA:
Harvard Business School Press, 2007.
12 Thomas H. Davenport, Jeanne Harris,
and Jeremy Shapiro, “Competing on
Talent Analytics.” Harvard Business
School Publishing, 2010.
13 Quoted in Davenport, Harris,
and Shapiro, “Competing on Talent
Analytics.”
7

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