Big Data is quickly becoming a critically important driver
of business success across sectors, but many executives
say they don’t think their companies are equipped to
make the most of it. Bain & Company surveyed executives
at more than 400 companies around the world, most
with revenues of more than $1 billion. We asked them
about their data and analytics capabilities and about their
decision-making speed and effectiveness.
he results were surprising: We found that only 4% of
companies are really good at analytics, an elite group that
puts into play the right people, tools, data and intentional
focus. These are the companies that are already using
analytics insights to change the way they operate or to
improve their products and services. And the difference
is already visible. These companies are:
• Twice as likely to be in the top quartile of financial performance within their industries
• Three times more likely to execute decisions as intended
• Five times more likely to make decisions faster As we describe in a companion brief, “Big
Data: The organizational challenge,” achieving competency in Big Data is a three-part process that requires setting the ambition, building up the analytics capability and organizing your company to make the most of the opportunity. This brief looks more closely at the second step—building up the analytics capability—to see how leaders use Big Data to get ahead.
• Twice as likely to be in the top quartile of financial performance within their industries
• Three times more likely to execute decisions as intended
• Five times more likely to make decisions faster As we describe in a companion brief, “Big
Data: The organizational challenge,” achieving competency in Big Data is a three-part process that requires setting the ambition, building up the analytics capability and organizing your company to make the most of the opportunity. This brief looks more closely at the second step—building up the analytics capability—to see how leaders use Big Data to get ahead.
Leaders build up their analytics capabilities by investing in four things: data-savvy people, quality data, state-ofthe-art tools, and processes and incentives that support analytical decision making
Leaders build up their analytics capabilities by investing
in four things: data-savvy people, quality data, state-ofthe-art
tools, and processes and incentives that support
analytical decision making (see Figure 1). About a third
of companies don’t do any of these well, and many of the
rest excel in only one or two areas. But to build a highperforming
analytics machine, you need to do all four well.
Success in each capability depends on strength in the others.
Companies need a strategic plan for collecting and organizing data, one that aligns with the business strategy of how they will use that data to create value. In our analytics survey, 56% of the companies didn’t have the right systems to capture the data they needed or weren’t collecting useful data, and 66% lacked the right technology to store and access data. A good data policy identifies relevant data sources and builds a data view on the business in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good data policy is to focus on identifying relevant sources of data. For example, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends; however, keeping detailed logs of requests that were easily handled might be less valuable.
Companies need a strategic plan for collecting and organizing data, one that aligns with the business strategy of how they will use that data to create value. In our analytics survey, 56% of the companies didn’t have the right systems to capture the data they needed or weren’t collecting useful data, and 66% lacked the right technology to store and access data. A good data policy identifies relevant data sources and builds a data view on the business in order to—and this is the critical part—differentiate your company’s analytics capabilities and perspective from competitors. A critical aspect of good data policy is to focus on identifying relevant sources of data. For example, capturing all queries made on the company website or from customer support calls, emails or chat lines, regardless of their outcome, may have significant value in identifying emerging trends; however, keeping detailed logs of requests that were easily handled might be less valuable.
Data Visualisation
Committing to excellence in each of these four categories
can require dramatic changes, significant investment
and occasionally a change in leadership. But it’s no good
focusing on one of these four areas without the other
three. Tools won’t help if the data is of poor quality,
and talent will walk if the company isn’t committed to
benefiting from the insights. Like an engine that must
be firing on all pistons, all four areas must be tuned
for peak performance.
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