Advanced analytics: Nine insights from the C-suite PART I
Conversations
with hundreds of business leaders reveal nine ways that they are—and are
not—adapting to the analytics revolution.
Data and advanced
analytics have arrived. The
volume of available data is growing exponentially, with more added every day
from billions of phones, sensors, payment systems, and cameras. Machine learning is
becoming ubiquitous, but organizations are struggling to turn data into value.
The stakes are high.
Those who advance furthest, fastest will have a significant competitive
advantage; those who fall behind risk becoming irrelevant. Analytics cannot be
the sole province of the chief information officer (CIO), as is sometimes the
case. The CIO may not understand the business as a whole well enough to spot
opportunities and threats, or be influential enough to ensure that the company
addresses them appropriately. While the expertise the CIO brings is of course
essential, business-unit leaders and CEOs must be in charge of analytics to
accelerate the pace of change and to ensure intelligent investment. This is
beginning to happen: McKinsey has found that more than 50 percent of CEOs
consider themselves the primary leader of the analytics agenda, and that figure
has been growing steadily.
With this in mind, we
spoke to more than 300 top executives of major companies. Here we offer nine
insights based on these conversations, and suggest actions for business leaders
to take.
Analytics can create new opportunities and disrupt entire
industries. But few leaders can say how
“Where do we want to be
in five years as a result of advanced analytics? What are the implications to
our business model, culture, portfolio mix, and value proposition?” CEOs all
over the world are asking these questions—for good reason. Analytics has the
potential to upend the prevailing business models in many industries, and CEOs
are struggling to understand how. The need is urgent.
Beyond reorienting the
existing business models, analytics leaders are also learning how to create and
capitalize on new opportunities. Organizations are moving from hoarding data to
sharing it. Some are pooling data as part of industry consortia, increasing
their comprehensiveness and therefore their value. Product-based organizations
are adding data and analytics to their offerings as value-added services. Some
have gone further, charging for the analytics-enabled service rather than
directly selling the product. For example, some jet-engine manufacturers now
sell flight hours instead of the engines; this is only possible because sensors
provide the data that help them understand usage and required maintenance.
Recommendations
There are two areas to
explore. First, to understand how analytics can disrupt existing business
models, set aside the time to focus on the long term. What can be learned from
other industries that are farther along? What customer needs can be better met
through new business models?
Second, to capture new
opportunities, start with the data, analyzing what they are worth, how distinct
they are, who would find them valuable, and how they can be combined with other
sources to increase their value. Then, think through the business model. A
simple way to get started is to conduct a market scan of the data and analytics
players, as well as a competitor scan to understand what others may be doing.
Identify where and how to play within this ecosystem.
Surprisingly few companies know where and how analytics
can create value
Analytics create value
when big data and advanced algorithms are applied to business problems to yield
a solution that is measurably better than before. By identifying, sizing,
prioritizing, and phasing all applicable use cases, businesses can create an
analytics strategy that generates value. For example, a CEO of a global
consumer-packaged-goods company told us that the application of advanced
analytics and machine learning to business functions such as revenue-growth
management and supply-chain optimization uncovered as much as $4 billion in
benefits.
Few executives, however, have such a detailed view of value across their business units
and functions. More typical is this kind of comment: “Sometimes I feel we are
doing analytics for the sake of doing analytics. We need to have more clarity
on what business value we are trying to create,” one senior executive said.
Most have experimented with a handful of use cases, but lack a comprehensive
view. Even fewer have considered how analytics can create new sources of
revenue. Lacking an enterprise-wide view of opportunity, business leaders
struggle to make a considered business case for analytics. They may also
struggle to communicate why analytics matter—and that is essential to get the
organization committed to change.
Recommendations
Start a rigorous
process with the executive team to decide where the most promising sources of
value exist. To start, identify which functions or parts of the value chain
have the most potential. For consumer-goods companies, for example, it could be product development or
inventory optimization; for insurance companies, it may be risk models. Then
come up with possible use cases—as many as 100 for a large company—and how new
data and techniques could be applied to them. Using outside benchmarks can be
useful to get a sense of how valuable a given use case might be. Finally,
decide the order of priority, considering economic impact, fit with the business,
feasibility, and speed.
Data science is the easy part. Getting the right data,
and getting the data ready for analyses, is much more difficult
As data science enters
the mainstream, commercial analytics platforms and code-sharing platforms are
providing algorithm libraries and analytics tools. For most organizations, this
simplifies the practical application of data science. But that still leaves the
matter of what to do with it. In our conversations, we heard a familiar
refrain: “The majority of our time is spent getting the data,” said a senior
executive at an advanced-industries company. “Once we have that in a good
place, the modeling is quick.”
Each data set is
unique, and it takes time to prepare it for analyses. One major issue is that
it can be difficult to agree on a “single source of truth,” because different
departments often use different ways to measure the same metric. For example,
the sales function may measure the volume of goods sold by transaction, while
operations may measure by inventory movement. Most companies have not yet
incorporated real-time data into day-to-day business processes. Many also
struggle to identify what data are needed to improve competitive advantage, and
therefore what they need to create. Other common challenges are implementing a
unique identifier to link different data sets (such as transaction data and
customer profiles) and filling in gaps to increase quality and usability.
Recommendations
The sea of data
is vast and growing exponentially. To avoid drowning, executives must connect the data strategy to the analytics strategy. When exploring new data sources,
it helps to have specific use cases in mind and to reflect on how data are
acquired—whether through commercial vendors or via open sources. Know what data
the business owns; this can become an asset to monetize. To continuously
improve data quality, put in place governance and processes, and ensure that
the rightful owners have direct access. Mandate good data and metadata
practices and build automatic data-reconciliation processes that constantly
verify that new data meet quality standards. To drive new insight, interconnect
different data sets, potentially in a centralized repository (or “data lake”).
Resist the temptation of complexity. Rather than building a data lake for all
legacy data—a project that can take years—fill the lake gradually. Start with
data required for priority use cases, and gradually add to it. Get started with
what you have, and don’t let perfection be the enemy of the good.
Data ownership and access needs to be democratized
The most common excuse
that businesses roll out for refusing to adopt counterintuitive analytics
insights is that the underlying data are not valid. This claim is much more
difficult to make if accountability for data quality rests with the business,
and if business leaders have ready access. Successful analytics organizations
give as many people as possible access to the data, while making sure there is
a single source of truth, so that employees can play with them and come up with
new ideas, or discard old ones that are past their prime. “The way we are
thinking about eliminating the finger-pointing between business and IT on
data,” said the CIO of a large pharmaceutical company, “is by making data
available to everyone.” By doing so, a data-driven decision-making mind-set
gets infused throughout the organization.
Recommendations
Design effective data
governance, specifying who is responsible for data definition, creation,
verification, curation, and validation—the business, IT, or the analytics
center. Embrace the dual principles of business ownership and broad access. Hold the business accountable for data, even if the IT
department houses and supports them. Create data-discovery platforms, such as
web-based self-serve portals that allow frontline staff to easily extract data.
Host data-discovery sessions to build data literacy.
Embedding analytics is as much about change management as
it is about data science
Old ways of working are
deeply ingrained, especially if there is an underlying distrust of analytics.
Another question, then, that executives are asking is how to influence
frontline staff to use the insights delivered by analytic tools to change how
to make decisions. The CEO of GE, Jeff Immelt, told McKinsey: “I thought if we
hired a couple thousand technology people, if we upgraded our software, things
like that, that was it. I was wrong. Product managers have to be different;
salespeople have to be different; on-site support has to be different.”
There are some success
stories. One common and essential factor is that leadership has to commit to
analytics, visibly. One executive told us how the head of a business unit used
analytical tools to crunch the numbers regarding stock levels. He then
presented the results to the weekly leadership meeting and required each
channel manager to take action.
It is also essential to
integrate insights into the daily work flow. Another executive spoke about how
the sales staff resisted using leads generated by the analytics model,
preferring to rely on their instincts. His team was able to engineer the work
flow so that the recommendation engine was “invisible”: the sales team was
simply presented with leads, and then acted on them—successfully.
Recommendations
People buy into change
when they understand it and feel they are part of it. The design of analytics solutions therefore needs to be user led and have
business-process participation from the start. Have a “translator”—someone who
not only understands the data science but also how it can be applied to the
business—lead use-case development from start to finish. Match the talent to
the task. The business identifies the opportunity, the data scientists develop
the algorithm, the user-experience designers shape the user interface, the
software developers run production, the process engineers reengineer work
flows, and the change agents do the implementation. Develop a playbook for each
use case, making sure critical adoption elements such as training and
communication are not neglected. Beyond individual use cases, design a broader
change program that builds analytics literacy and shifts the organization toward a data-driven culture. Organizational change management
is generally well understood; it is a matter of applying these principles to analytics.
CONTINUES IN PART II
No comments:
Post a Comment