Advanced analytics: Nine insights from the C-suite PART II
“How do I know that the
investment I’m making in analytics is worth it? What are the metrics? How do I
attribute value to analytics versus all the other things my teams are doing?”
These questions, from a senior executive at a large insurer, are typical.
What’s also typical is that few of the executives with whom we spoke can answer
them.
If the value of
analytics is not explicitly measured and then communicated, it will be
difficult to build support and thus justify investment. This is not always
easy, because analytics is often used to support decisions, and therefore the
value cannot always be isolated from other initiatives.
In a successful
measurement strategy, the metrics are detailed and logically connected to
business outcomes. For each analytics use case in production, review the
associated outcome metrics, and ask how they contribute to business outcomes.
If the use of analytics decreases customer churn by 2 percent, how much savings
does that translate into?
Recommendations
Create a dashboard that
incorporates all performance indicators of interest and features automated data
feeds, so that it is easy to stay on top of what is going on. Then, trust the
message that the data tells. “By relying on the statistical information rather
than a gut feeling,” said a CEO of an investment bank, “you allow the data to
lead you to be in the right place at the right time. To remain as emotionally
free from the hurly-burly of the here and now is one of the only ways to
succeed.”
With automation and
digitization, it is possible to see changes in real time, rather than waiting
for the end of the month, quarter, or year. And because it is possible to
measure more often, there is no excuse not to do so. Numbers only have value
when they are put to work. Businesses should decide what the best cadence is,
and do it.
There is no universal way to organize an analytics
operating model
There are, however, two
general truths. First, there should be a central function to maintain best
practices and capitalize on economies of scale for hard assets. Second,
accountability for value capture rests with whomever owns the bottom line. Once
solutions are developed, with business input, business leaders need to be held
accountable for capturing the value.
What is the best
operating model for analytics? The tension is between what the center of
excellence (COE), a central function for data science, should be responsible
for, versus where the business units are. Each model can work, if used
correctly. Recent McKinsey research has found little correlation between how analytics
is organized and how successful it is. What matters is that the operating model
should be consistent with the business model, so that it can take advantage of
the successful elements of the existing culture and practices while still
promoting the cross-functional practices that any analytics effort needs to
succeed.
Recommendations
Leaders should assess
where the decision-making power sits in their organization—in the center or in
the business units—and then design an analytics organization model that
leverages the strengths of existing structures. If there is already an
analytics COE, it is important to assess its effectiveness. Among the questions
to consider: How fast can decisions be made? Is there sufficient business input
into analytics solutions? Am I capturing the expected value from these
solutions?
The talent challenge is not only to find data scientists
but also to groom ‘translators’
While the talent market
is still tight, most CEOs we spoke to said their companies already employ data
scientists. What they need is more business experts who are also proficient in
analytics—translators who can spot opportunities, frame a problem, shape a
solution, and champion change. “I have lots of people who speak the language of
business, and I have no problem finding software engineers who speak the
language of technology,” one CEO told us. “But I can’t find translators who
speak both languages.” The key is to find people who can take the numbers, and
then work them for the benefit of the business.
Recommendations
Identify high
performers with a quantitative background, such as statisticians and
econometricians, then design a capability-building program to extend their
analytics skills. The curriculum should include not only data science but also
the leadership skills required to lead the identification and implementation of
a use case end-to-end, and the change-management skills required to spur
culture change. Make use of adult-learning principles when designing these
programs, combining methods like on-the-job training, in-person learning, and
online refresher courses. Consider designing formal certifications to those who
successfully complete these courses. This provides recognition and creates a
common language and set of standards.
The fastest way to a big idea is to cultivate a
data-driven, test-and-learn culture
Every company is happy
to celebrate success, which is fun and easy; but many are not so keen to
communicate bad news. Many companies also have a hypothesis bias, shaping data
to an existing agenda.
In many start-ups and
other agile businesses, on the other hand, there is a data-driven,
test-and-learn culture. Once the high-level vision is set, employees are
encouraged to identify where the opportunities are, quickly develop proofs of
concept, and then let the data speak to the situation. The emphasis is on
generating counterintuitive insights and new ideas swiftly, testing them, and
then either going ahead or tossing them out. Bad news is communicated early and
without shame because mistakes are seen as sources of improvement for the next
iteration. While not all parts of the organization may need to fully adopt this
culture, analytics centers of excellence, as well as business units and
functions that need to stay on the cutting edge, do.
Recommendations
The sandbox is a place
of playful creativity in which what is built can also be quickly torn down.
That is the atmosphere to aim for: provide the right tools, technology, and computer
power needed to discover new features, run correlations, and perform analyses.
Then, make it possible to tear it down as new information and needs supersede
the old, without having to go through a lot of data security, compliance, and
cleanup.
This is all part
of building a culture in which data, not guesses, are brought to bear on problems, and
where people are comfortable with constant change. Delivering, and hearing, bad
news has to be seen as part of business as usual. Set clear stage gates for
investment, even while accepting that most efforts will fail, and then increase
investment size as milestones are achieved. Emphasize the need for speed. “We
fail more often than we succeed in analytics,” noted the leader of a business
unit at a consumer-goods company. “But we are trying to move more quickly in
learning from failures and moving to the next iteration.”
Many sectors are not getting the most out of data and analytics. Doing better requires bringing a
sense of urgency to the challenge, and then a willingness to do things
differently. The executives we spoke with, on the whole, understand this.
Completing a full
transformation means aligning the business around a common strategic
aspiration, establishing the fundamentals, and generating momentum. This
typically takes two to three years. Organizations therefore have only a narrow
window in which to work. Otherwise, they will fall behind—and may never catch
up. As one CEO mused, “It’s no longer the big fish eating the small, but the
fast ones eating the slow.”
By Jit Kee Chin, Mikael Hagstroem, Ari Libarikian, and Khaled Rifai
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/advanced-analytics-nine-insights-from-the-c-suite
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