Scaling API-first – The story of a global engineering organization
Building an analytics culture: a best practices guide
1. CONCLUSIONS PAPER
Insights from a webinar in the 2012 Applying Business Analytics
Webinar Series
Building an Analytics Culture
A Best Practices Guide
3. 1
Building an Analytics Culture
Introduction
Common definition for business analytics:
“The practice of iterative, methodical exploration of an organization’s data with an
emphasis on statistical analysis.”
This common definition doesn’t even hint to the real power of analytics. By applying
analytics to the seemingly unlimited flow of data available today, organizations can
understand and address complex business issues in ways never before imagined.
Basing decisions on facts rather than on intuition pays off. Companies that use business
analytics in a strategic way gain a competitive advantage, improve productivity and
boost the bottom line.
Organizations have gotten this message and have upped their games in recent years.
According to a 2011 study by Bloomberg Businessweek Research Services, the
percentage of companies using some form of business analytics rose from 90 percent
in 2009 to 97 percent in 2011 (Source: The Current State of Business Analytics: Where
do we go from here?, Bloomberg Businessweek Research Services, August 2011,
sponsored by SAS).
However, what some organizations – and even some software vendors – call “business
analytics” is often little more than sorting, filtering, slicing and dicing information for
hindsight reporting or a snapshot view of the present. That is far too limited a definition,
considering what is possible. Companies that rely on simplistic forms of analytics are
missing out on the full value of the insights hidden in their databases – and so are the
companies that don’t establish an environment where analytics can really perform.
What do companies have to do to get their money’s worth out of their data assets and
analytic investments? That question was the topic of a companion set of webinars in
the SAS Applying Business Analytics Webinar Series, where three experts from SAS
addressed this question as it relates to business managers, IT leaders and executives.
Progress Toward the Data-Driven Ideal
“Across the board, companies vary in their level of analytical maturity, but the idea
is they all want to gain competitive advantage using data-driven decision making,”
said Kathy Lange, Senior Director of Consulting at SAS. “They’re trying to improve
productivity, increase revenue, decrease costs, and manage risk and uncertainty in the
decisions they’re making.”
The potential benefits warrant attention, and companies are making investments to
improve their analytic strength. According to joint SAS and Accenture research, 45
percent of businesses increased their spending on analytics in 2011 and even more of
them – 65 percent – reported plans to increase spending in 2012.
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4. 2
SAS Conclusions Paper
This attention is not just focused on technology. Eight in 10 respondents whose
organizations were investing in business analytics said they planned to upgrade their
analytical strengths by improving the skills of existing staff (69 percent) and hiring new
analytical talent (55 percent). This focus on human resources is important, because by
2018, the demand for deep analytical talent in the US could be 50 to 60 percent greater
than the supply (Sources: US Bureau of Labor Statistics, US Census, Dun & Bradstreet:
company interviews, McKinsey Global Institute analysis).
The gap between analytical potential and reality is already being felt. In the joint SAS
and Accenture analytics study, only one in four respondents indicated that their
organization’s use of business analytics was “very effective” in helping them make
decisions. Only one-third reported that they had achieved or exceeded return on their
business analytics investments. However, the leaders have achieved some truly notable
returns, repaying their investment many times over.
The Four Dimensions of an Effective Analytics Culture
Organizations with the strongest analytical performance are those that focus on
best practices in four key areas: business analytics skills and resources; information
environment and infrastructure; internal processes; and organizational culture. Our
webinar presenters shared some best practices in all four areas – guidance for creating
an environment where analytics can flourish and deliver on its potential.
Focus Area 1: Business Analytics Skills and Resources
Success with business analytics is about more than technology. Organizations must
upgrade their business and technical analytical skills to make full use of the available
technology and to apply the results of analytics to the appropriate business issues.
Provide the Right Balance of Resources
“Of course we need the hardware, the software and the information, but one of the key
challenges is having the right [human] resources in place – analytical resources, domain
expertise and IT resources” said Aiman Zeid, Principal Business Consultant at SAS. “A
lot of organizations don’t pay enough attention to this aspect.”
Talent upgrades can come by way of training current employees or hiring new analytical
workers. A key is to attract, retain and continuously refresh the knowledge of team
members who understand the use of analytics in technical and business contexts.
Make Analytics More Approachable
Acknowledging that analytical skills will be in short supply, how do you manage for that?
“Organizations can’t get all the resources they need, so they need to figure out a way to
make analytics more approachable, so we can get more business analysts using more
analytics,” said Lange. For example:
Companies making a foray
into business analytics face a
learning curve. Moving to fact-
based decision making requires
the right technology, talent
and processes – as well as
a cultural shift.
Companies that want to
improve the effectiveness of
their analytics should consider
four key focus areas: Find the
right talent mix, get the data
in order, establish internal
processes to support analytics,
and foster a culture of fact-
based decision making.
5. 3
Building an Analytics Culture
• Business visualization can transform how you see, discover and share insights
hidden in your data. Graphical presentations enable nontechnical users to
experience – and share – the aha! moments with an impact and immediacy that
cannot be achieved with static graphs, spreadsheets or reports.
“Data visualizations can summarize billions of rows of data into the key information
that an executive could use on an iPad®
,” said Lange. “Unlike a static report, it’s
interactive, so users can drill down into the data detail and see the factors that are
influencing the outcomes. The idea is to get analytics out of the back room and
into the decision-making process – and to spread it across the organization to
make it more available to the people making the decisions.”
Figure 1. Data visualization empowers more users to use more analytics.
• Visual programming streamlines and simplifies the process of creating analytical
workflows. Simply drag and drop options or nodes into the workflow and connect
them to the data as appropriate. In the Gantt-type display, it is easy to add steps
to the workflow and track the steps that have been taken.
Wizard-driven analytical processes improve upon traditional coding, empower
more people in the organization to generate analytic insights, free quantitative
specialists to focus on the most complex and critical questions, and improve
information governance.
“By making it intuitive – even wizard-driven – to create analytical process flows and
explore the data, more users are empowered to take advantage of analytics, analysts
can focus on tougher analytical questions, and we encourage broader use of analytics
within the organization,” said Lange.
“The idea is to get analytics
out of the back room and into
the decision-making process
– and to spread it across the
organization to make it more
available to the people making
the decisions.”
Kathy Lange
Senior Director of Consulting, SAS
6. 4
SAS Conclusions Paper
Focus Area 2: Information Environment and Infrastructure
At its core, business analytics is about using data to discover insights that can change
the way an organization operates. Without a strong foundation of accurate and reliable
data, the results of analytics are suspect and likely to be overridden by executives.
According to the SAS and Accenture analytics research, intuition is used over analytics
in an average of 39 percent of business decisions. Survey participants cited two main
reasons for opting for intuition over analytically derived results: lack of access to the
needed data and lack of confidence in the data they do have. Both issues can be
addressed through a sound information management strategy.
Upgrade the Information Architecture
Most organizations have data located across a large number of heterogeneous data
sources. Analysts spend more time finding, gathering and processing data than
analyzing it. “Data quality, access, security… these are very key issues for most
organizations,” said Zeid. “Some of these weaknesses really come to the surface when
you run analytics, especially when working with data from multiple systems, business
units or regions. Inconsistency in coding and product definitions is common.”
The way data is formatted and presented needs to change as well. “IT really needs
to step up and understand the data requirements of analytics,” said Mark Troester,
former Senior Product Marketing Consultant at SAS. “IT is very well-versed in managing
relational databases in the context of operational systems, but the data requirements
for predictive analytics, business intelligence and reporting are very different from that.
So they need to understand how to structure the data to support different kinds of
analytics.”
There will likely be changes in the infrastructure to accommodate analytic processes.
“As analytics becomes more pervasive, organizations are no longer just doing analytics
on a project-by-project basis,” said Troester. “So they need an infrastructure that
supports enterprise-class analytics and multiple projects. IT is uniquely situated to help
with the data aspects of this evolution. At the very least, they own the infrastructure
where the data resides, but they also bring a unique blend of business and technical
knowledge that can be key in preparing data for analytics and offloading that work from
the analysts.
“Finally, IT really needs to understand the analytical life cycle. It’s not the same as
building applications, where you design, test and put it into production. The analytical
process is much more iterative, so IT needs to provide a sandbox environment for the
analysts, and they need to be able to iterate on that, in terms of getting new forms of
information, restructuring the data and trying different modeling techniques. And then
IT needs to step in when the analyst is done and take the results of that analytical effort
and embed it directly into operational systems, with the rigor for which IT is well-known.”
“By making it intuitive – even
wizard-driven – to create
analytical process flows and
explore the data, more users are
empowered to take advantage
of analytics, analysts can focus
on tougher analytical questions,
and we can encourage broader
use of analytics within the
organization.”
Kathy Lange
Senior Director of Consulting, SAS
7. 5
Building an Analytics Culture
Capitalize on Advanced Analytics, Not Just Reporting
“Companies are turning to business analytics to tackle big issues, but even with the high
adoption rate, they are still in the emerging state in their use of advanced analytics,” said
Lange. For example, there’s data mining, which is looking at huge data sets – potentially
millions or billions of rows of data – and uncovering patterns you would not typically be
able to see.
There’s forecasting, which is also using historical data to make better decisions about
the future, but with an added time dimension. For instance, you could use forecasting to
predict sales for the next three, six or 12 months.
Text analytics is an emerging area of importance, Lange noted. “Organizations are
actually collecting more unstructured data (text) than structured data – about three times
as much. We can use linguistics and statistical analysis to incorporate that text into our
modeling and analytics.” For instance, unstructured data gleaned from social media, call
center notes, insurance claims and survey tools could uncover insights about consumer
sentiment, potential fraud, socially connected entities and more.
“Finally, there’s optimization, where we’re trying to find out the best answer – either
minimizing a factor (such as cost or attrition) or maximizing something (such as profit or
revenue) – within constraints such as limited resources, money or time.”
Bridge the Gaps Between IT and the Business
“Data issues are still the biggest inhibitor to the use of analytics within an organization,
but the second biggest constraint is the gap between business and IT,” said Lange.
“There’s a huge communication gap; they speak different languages. IT doesn’t understand
that the way they collect the data is not the way the business is going to use the data.
The business user might say, ‘I want you to give me the 5,000 best customers that
are going to respond to my campaign,’ and the IT person says, ‘Well, the data is there
in the data warehouse’ – but really it isn’t there; there has to be data preparation that
makes it ready for that kind of analysis. This disconnect requires a third-party interpreter
– an analyst or data scientist who can translate between IT and the business.”
Focus Area 3: Internal Processes
“Organizations need a well-defined set of processes to help the business community
tap into and access analytical resources – and to identify, prioritize and address
analytical requirements,” said Zeid. “These processes have to be well-defined, accepted
and promoted within the organization.”
Manage Analytics as an Ongoing Process, Not a One-Off Project
Now that predictive models are high-value organizational assets – essential tools
to manage uncertainty and risk – the models and their underlying data must be
managed for optimal performance throughout the analytical life cycle. It’s not only
about developing the models. It is also about deploying them, embedding them into a
business process and monitoring them over time. With the demand rising for predictive
models, you want to have an enterprise view on managing them.
“The increasing use of
analytics provides an excellent
opportunity for IT to partner
with the business to make
fundamental change to the
business by enabling better
decisions through analytics.
By supporting analytics, IT can
break out of the cost center
mentality and adopt a more
strategic role that helps drive
top-line revenue growth.”
Mark Troester
Former Senior Product Marketing
Consultant, SAS
8. 6
SAS Conclusions Paper
With a formal model management framework – an “ analytics model factory” – it
becomes far easier to document models and collaborate across departments and
internal agencies. An analytics model factory closes the loop in the analytical setup to
get your value.
IDENTIFY /
FORMULATE
PROBLEM
DATA
PREPARATION
DATA
EXPLORATION
TRANSFORM
& SELECT
BUILD
MODEL
VALIDATE
MODEL
DEPLOY
MODEL
EVALUATE /
MONITOR
RESULTS
Domain Expert
Makes Decisions
Evaluates Processes and ROI
BUSINESS
MANAGER
Model Validation
Model Deployment
Model Monitoring
Data Preparation
IT SYSTEMS /
MANAGEMENT
Data Exploration
Data Visualization
Report Creation
BUSINESS
ANALYST
Exploratory Analysis
Descriptive Segmentation
Predictive Modeling
DATA MINER /
STATISTICIAN
THE ANALYTICS LIFE CYCLE
Figure 2. The analytics life cycle should be managed as an iterative, closed-loop process.
Facilitate Collaboration
“When you visualize the analytical life cycle, it looks like a cyclical process, but it’s
actually very iterative around that circle,” said Lange. There are many people involved
in that process at various stages. For instance, a business manager asks the question
that requires an analytics-driven answer, provides domain expertise, makes the
business decisions based on the analytics, and evaluates processes and return on
investment from the decision. A business analyst conducts data exploration, works with
data visualization and creates reports. The IT systems/management team is responsible
for data preparation and model validation, deployment and monitoring. A data miner or
statistician performs more complex exploratory analysis, descriptive segmentation and
predictive modeling.
To get the best analytic results, these players with the right skills need to be in place,
working collaboratively and empowered to perform these roles.
9. 7
Building an Analytics Culture
Focus Area 4: Organizational Culture
“Creating an analytics culture requires that the organization understand, value and
demand fact-based decisions and strategies,” said Zeid. “The organization needs to
communicate the value of analytics, fund the appropriate resources and reward proper
use. Furthermore, we need to set the right expectations about what we’re doing and
what analytics is exactly going to do, how it will contribute to the bottom line – and
make sure the culture is tuned and ready to adopt, absorb and use analytics for
decision making, policy validation and so on. If we don’t pay attention to these cultural
factors, the result is usually suboptimal.”
Grass-roots analytics efforts can be successful, but the path is shorter and smoother
when the culture embraces fact-based decision making. Support from executives from
the outset helps address many of the challenges companies face in trying to move up
the analytics maturity curve. With executive support, talent issues are more likely to be
addressed, collaboration improves, fact-based decision making is more highly valued,
and data issues are more readily resolved.
How to Get Started
Choose a Business Area that Is Ripe for Success
“First, make sure you’re very clear on business priorities and objectives,” said Zeid.
“Use those priorities and objectives to assess the analytics environment on the four key
dimensions: people, technology, process and culture. Once you take a look at those
four dimensions, you will quickly form a picture of current capabilities and can make a
gap analysis and determine what’s missing.”
With those gaps addressed and the dimensions in alignment, where should an
organization start using analytics? Zeid recommends choosing a business area that:
• Represents a high business priority.
• Has integrated and consistent data available.
• Has enough historical data to create meaningful insights.
• Offers the potential to generate tangible business value.
• Has the skills and resources in place to effectively use analytics.
“For instance, if we’re trying to forecast potential sales numbers, we need to make
sure we have the historical data, and it’s integrated and of a good quality,” said Zeid.
The staffing, internal processes, support from stakeholders in the organization – these
elements must be in place. “The first analytic project will be under the microscope, so
you need to get it right.
“Focus the effort on something that’s very critical and strategic. Gain consensus
on how the results of the project will be evaluated, and be clear and honest about
communicating the results. All of that will be critical to get to the second project.”
Integration of analytics across
the organization is a marker
of analytic success, but it’s
not necessarily the cause.
When the organization has
embraced analytics as the
foundation for business decision
making, integration across the
organization will likely follow.
10. 8
SAS Conclusions Paper
Consider Establishing an Analytics Center of Excellence
A center of excellence is a cross-functional team of analytic and domain specialists
who plan and prioritize analytics initiatives, manage and support those initiatives,
and promote broader use of information and analytic best practices throughout the
organization. Although it will not have direct ownership of all aspects of the analytics
framework, an analytics center of excellence will provide oversight, guidance and
coordination for technology, process, data stewardship and the overall analytics
program – both from an infrastructure and support/governance perspective.
“A center of excellence is a very effective way to accelerate an organization’s analytic
maturity,” said Zeid. “It will also produce significant value by finding out where IT, domain
and analytical resources exist and making the best use of them.”
As an analytics center of excellence mobilizes analytic resources for the good of the
organization – not just for specific business units or one-off projects – it ultimately
changes the culture of the organization to appreciate the value of analytics-driven
decisions and continuous learning.
As an analytics center of
excellence mobilizes analytic
resources for the good of
the organization – not just
for specific business units or
one-off projects – it ultimately
changes the culture of the
organization to appreciate
the value of analytics-driven
decisions and continuous
learning.
The most distinctive difference in
analytical performance between
the front-runners and the others
is how they perceive their data
and talent. High-performing
companies see their data as
an asset; they also report that
quality data – and access to the
right data – is available.
11. 9
Building an Analytics Culture
Closing Thoughts
“Analytical leaders have very specific attributes,” said Zeid. “First, they value information
as an asset, and that’s not just saying that information is critical to the organization;
it is supporting that value statement with the prerequisites for success in all four key
dimensions:
• The right blend of analytical talent, domain expertise and IT knowledge, infused
with a collaborative spirit and curiosity.
• An agile information infrastructure that offers trusted, analytics-ready data and
the processing power to deliver timely answers to decision makers.
• Processes that enable business users and analytics specialists to get their
answers quickly, and to treat analytics as an iterative, ongoing process rather than
an ad hoc project.
• An organizational culture that sets the tone for requiring fact-based decisions and
validating assumptions with facts.
“If analytics doesn’t produce the optimal results, the number one reason is a lack of
strategy,” said Zeid. “This underscores the need to take a holistic, enterprise approach
to the analytical environment.
“Allow access to more computing resources. Help business units get the information
they need – in the way they need it – from multiple functions within the organization.
Allow flexibility for analytical experimentation, then apply more rigor to production
deployment. Foster collaboration between IT and the business. Review and adjust IT
governance policies to make sure they facilitate the use of analytics. And finally, look
into establishing an analytics center of excellence to accelerate the organization’s
maturity.”
The Four Dimensions
of Analytic Excellence
Business Analytics Skills and
Resources
• Analytical, technical and
interpersonal skills.
• Training, career advancement to
attract and retain talent.
Information Environment and
Infrastructure
• Relevant, accurate, consistent and
timely enterprise information.
• Mature and capable enterprise
information infrastructure.
Internal Processes
• Well-defined processes to identify,
prioritize and address analytical
requirements.
• Coordinated support from
IT, analytical resources and
computing power.
Organizational Culture
• Understanding the value and
expectations for fact-based
decisions and strategies.
• Communicating the value of
analytics, funding staffing and
rewarding proper use.
