Co
m
pl
im
en
ts
of
Democratizing
Analytics
Compete and Win
by Empowering Your
People with Data
Melissa Burroughs
& David Sweenor
REPORT
Democratizing Analytics
Compete and Win by Empowering
Your People with Data
Melissa Burroughs and David Sweenor
Beijing
Boston Farnham Sebastopol
Tokyo
Democratizing Analytics
by Melissa Burroughs and David Sweenor
Copyright © 2023 O’Reilly Media, Inc. All rights reserved.
Printed in the United States of America.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA
95472.
O’Reilly books may be purchased for educational, business, or sales promotional
use. Online editions are also available for most titles (http://oreilly.com). For more
information, contact our corporate/institutional sales department: 800-998-9938 or
corporate@oreilly.com.
Acquisitions Editor: Michelle Smith
Development Editor: Jill Leonard
Production Editor: Elizabeth Faerm
Copyeditor: Shannon Turlington
February 2023:
Proofreader: Elizabeth Faerm
Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Kate Dullea
First Edition
Revision History for the First Edition
2023-02-15: First Release
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Democratizing
Analytics, the cover image, and related trade dress are trademarks of O’Reilly Media,
Inc.
The views expressed in this work are those of the authors and do not represent the
publisher’s views. While the publisher and the authors have used good faith efforts
to ensure that the information and instructions contained in this work are accurate,
the publisher and the authors disclaim all responsibility for errors or omissions,
including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this
work is at your own risk. If any code samples or other technology this work contains
or describes is subject to open source licenses or the intellectual property rights of
others, it is your responsibility to ensure that your use thereof complies with such
licenses and/or rights.
This work is part of a collaboration between O’Reilly and Alteryx. See our statement
of editorial independence.
978-1-098-14543-9
[LSI]
Table of Contents
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
1. Data and Analytics Challenges in Modern Organizations. . . . . . . . . . . 1
The Issue: Insights Not Available Where Needed
A New Approach: Democratizing Analytics
9
10
2. How Democratizing Analytics Improves Business Outcomes. . . . . . 13
Case Study: Chick-fil-A
Analytics Maturity: The Hidden Key Performance Indicator
Driven by Analytics Democratization
Concrete Benefits of Democratizing Analytics
13
14
15
3. Democratizing Analytics and Data Governance. . . . . . . . . . . . . . . . . . 19
Case Study: UBS Group AG
Learnings: UBS’s Category-Driven Approach
Case Study: BT Group
20
22
23
4. Best Practices for Analytics Democratization. . . . . . . . . . . . . . . . . . . 27
Case Study: Phillips 66
Engage Experts for Guidance
Gain Executive Support
Start Small
Demonstrate Benefit
Train the Trainers
Make Analysts the Foundation
Establish an Analytics Council
Keep the Process Moving
28
30
30
31
31
31
32
32
33
iii
A. Eight Steps to Getting Started with Analytics Democratization. . . . 35
Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
iv
| Table of Contents
Introduction
The world is constantly changing around your organization. The
increasingly rapid pace and massive scale of the online world are
already forcing companies to evolve, but many other external factors
also impact organizations across the globe. Economic uncertainty,
geopolitical instabilities, persistent inflation, and strained supply
chains are now the new normal. How does your organization stay
ahead of all this ever-evolving change? Perhaps a better question is:
what is it that allows successful companies to be flexible, adaptable,
and resilient when the business environment around them shifts
dramatically? How are they able to identify challenges in their busi‐
ness and adapt quickly to minimize impacts? The simple answer is:
information. Successful organizations fuel decisions with informa‐
tion derived from analyzing data.
It is not difficult to understand why. An organization cannot suc‐
ceed without understanding what brings it business or removing
obstacles that hinder progress. Successful organizations do this
based on the data that their operations, customers, prospects, sup‐
pliers, and partners generate each day. But what takes an organiza‐
tion from surviving to thriving? How can it utilize data analytics to
capitalize on success and leapfrog its competitors? What steps must
a company take to transform from merely reacting to anticipating
future demands and opportunities?
v
Industry leaders achieve success by implementing a solid busi‐
ness strategy, advancing their organizations’ analytics maturity,
and leveraging a strategy called analytics democratization. Analyt‐
ics maturity is a measure of how far a company has progressed
toward integrating analytics into its culture, processes, and business
decision making. One step toward analytics maturity is analytics
democratization (also referred to as data democratization), which
makes organizational data and analytics capabilities accessible to
all employees within the organization who can use it to improve
operations, efficiency, and financial performance.
Democratizing analytics doesn’t mean
opening all data to everyone
throughout the organization.
This doesn’t mean opening all data to everyone throughout the
organization. Rather, it’s empowering employees in a moderated and
structured way to better assess and analyze data appropriate to their
role in the business. It’s giving them power to derive insights from
data to optimize and augment business processes with analytics at
all levels within an organization in order to improve efficiency, save
costs, and gain profits. To understand how analytics democratiza‐
tion is beneficial, it’s important to compare the practices of industry
leaders who’ve adopted analytics democratization to those who lag
in competitiveness.
Organizations that democratize analytics have a higher level of ana‐
lytics maturity compared to those that do not. Those businesses
that rise to the challenge of democratizing analytics gain sustainable
competitive advantages across many business and financial metrics.
Democratizing analytics provides
competitive advantages.
For example, analytically mature organizations outperform compet‐
itors in three-year and five-year operating income as well as many
other financial metrics. Even small changes in analytics maturity pay
huge dividends. Businesses that leverage analytics to drive decision
vi
|
Introduction
making pull in nearly six times more revenue than businesses
that use analytics only for reporting!1 Evolving into an analytically
mature organization provides many benefits and opportunities, but
how does a company get there—and what obstacles must it navigate
along the way?
1 International Institute for Analytics, “Analytics Maturity Powers Company
Performance”.
Introduction
|
vii
CHAPTER 1
Data and Analytics Challenges
in Modern Organizations
Accessing and analyzing data presents difficulties for every organi‐
zation. In most organizations, data follows a path from creation
to insight that is both delayed and restrictive. The flow is usually
something similar to Figure 1-1.
Although businesses may follow different processes, common fric‐
tion points exist. For example, any juncture where data is collected
or shared (across applications, business units, repositories, etc.)
introduces pressure to translate that data in some way to ensure
it becomes (or remains) actionable for the recipient (system, team,
etc.). These data transformations can introduce errors and delays,
which add up as the system’s complexity increases. The pain of this
friction is keenly felt in the amount of time it typically takes for
transactional data to become actionable insights. Imagine a retailer
trying to make as many sales as possible during a heavy holiday
season. Things are going well, and the retailer sells most of its local
inventory during the course of the day. The data is typically stored
in transactional systems and is transferred with some frequency to a
centralized warehouse for analysis and reporting.
1
Figure 1-1. Simplified Business Process: This example product sale
flow demonstrates the path data takes across business functions in a
modern organization.
Unfortunately, for this retailer, data extraction and loading into
its warehouse usually occurs nightly, meaning this valuable insight
won’t be available until the next day. Even if the data is available
immediately, however, a process or person is typically involved to
derive and interpret insights from the raw transactional data. All of
these delays mean that the retailer might be unaware until much
later in the day that its local storefront needs additional stock,
potentially missing out on a large volume of sales. For almost all
organizations, the data input at the transactional point of business
is raw and constantly changing. Furthermore, in some cases data
is manually inputted by a person, which can introduce errors and
delays in the process. This means that ingesting, cleaning, analyzing,
and presenting the data for insights creates a significant delay in
business decisions and action.
Time isn’t the only concern, though. The complexity of the data
itself strains organizational expertise. Different areas within the
business have different analytics needs. What answers important
questions for the sales department will likely not suffice for the
2
|
Chapter 1: Data and Analytics Challenges in Modern Organizations
shipping and receiving division. Furthermore, the nature of the data
(as well as the related insights) varies wildly depending on where in
the data path one performs analysis: inventory data, customer data,
and production data have entirely different features, formats, and
relationships between entries.
As a result, data in the organization will be siloed and require
subject matter expertise specific to each area (and the data will often
sit in corresponding organizational silos). However, these professio‐
nals typically lack the analytical skills and tools needed to derive
sophisticated insights from the data. So, they turn to data experts:
data engineers, data analysts, and data scientists who conversely lack
insight into the unique needs of each line of business. This leaves
the organization with a large gap between business expertise and
analytics expertise. This “analytics chasm” delays insight generation
and wastes analytics expertise on business-as-usual questions.
Technology choices also create difficulties unique to each organiza‐
tion. Recent years have seen a dramatic shift in data management,
analytics, and business intelligence technologies and practices with
the advent of remote storage and computing. Organizations are
rapidly transitioning from locally run servers to cloud-based infra‐
structure. This means that the data path shown in Figure 1-1 is
evolving. New technologies, such as machine learning and artificial
intelligence, are being developed to interact with these cloud-based
systems. As technology becomes more advanced, industries struggle
to employ and retain expertise to keep up with these trends. Addi‐
tionally, many of these technologies require significant investments
of time, money, and labor to implement.
With these concerns in mind, examine how the traditional approach
to the data path in Figure 1-1 hinders productivity, delays insights,
and prevents the business from competing effectively.
Figure 1-2 illustrates several places within the flow of a product sale
that are affected by the inability to directly access the information
needed to make intelligent decisions. Each friction point impacts
the costs and revenue generated by the company. Together, they
make the difference between a company being just functional versus
industry leading. How might giving access to analytics help these
areas? Let’s dissect this diagram from the perspective of the individ‐
uals involved to better understand how these friction points impact
Data and Analytics Challenges in Modern Organizations
|
3
the business. At the same time, let’s also look at how access to
analytics improves performance.
Figure 1-2. The Data Flow of a Product Sale: Inefficiencies and
missed opportunities in data flows become pain points. However,
opportunities exist to improve analytics delivery.
4
|
Chapter 1: Data and Analytics Challenges in Modern Organizations
The following list takes a deeper look into how the pain points in
Figure 1-2 interrupt your business flow and examines how access to
data and analytics could lessen that impact:
Suppliers
A great many places in your business rely on data from other
areas. This is especially true for those outside your organization
on whom you depend. Before a product can be made or a
service rendered, your business needs resources. How do you
determine which materials you need and how many? You must
not only communicate your needs to the supplier but also
estimate how other factors could influence those needs. The
ability to analyze stock, supply, and demand data enables those
responsible for ordering your resources and those providing
them to you to monitor and quickly react to changes in the
supply chain. This prevents over- and understock issues and
may reduce the impact that supply chain problems have on your
other business processes.
Production
The people who create or provide your company’s goods and
services also need analytics. They must know not only how
much demand exists for your product but also how much time,
effort, and resources are required to meet that demand. Access
to analytics tools and on-demand data would allow them to
address business needs more proactively, manage employees
more nimbly, and better prevent potential issues from impact‐
ing their work. Analytics tools would also deliver insights into
how well a production line runs and how it might be improved.
Marketing
This is the leading edge of the customer-facing side of your
business. Without data analytics, marketers have few tools at
their disposal to understand customer behaviors, determine
what customers want across various digital channels, assess
which marketing campaigns are effective, and determine how to
adapt your product’s pricing, packaging, positioning, and place‐
ment to remain competitive in the marketplace. They also need
data insights to understand and react to regional preferences
and customer demographics. Providing your marketing team
with analytics tools and access to data helps ensure that your
business appeals to customers, provides the right goods or serv‐
ices, and reaches the necessary markets. The marketing team
Data and Analytics Challenges in Modern Organizations
|
5
should have access to behavioral, regional, and demographic
data to provide insights back to the company as to what is
in demand and how competitive your business is within the
marketspace.
Sales
The people who facilitate your customers’ purchasing of your
product or service are invaluable. In most organizations, these
individuals are given access to transactional systems, allowing
them insights into stock numbers, pricing, and local revenue. Is
this enough? A sales team with access to analytics could look at
buying behaviors, patterns, and shifts in the local store or terri‐
tory. They could adjust strategies around an anticipated product
stock-out or prepare for upcoming demand due to changes in
products, services, pricing, or promotions.
Finance
Your finance team requires data to determine how to distribute
funds throughout your business and monitor revenue sources.
Data insights need to be derived across all areas of the busi‐
ness—from supply chain to employee pay rates and benefits
to revenues from sales. These data points coalesce to tell the
story of the success or shortcomings of your business. Without
analytics tools, financial decisions are typically based on sum‐
mary values. While this approach meets the basic needs of the
business, it does little to anticipate or preempt change. Analytics
tools help a finance team go from looking at the now to looking
at the patterns leading up to now. The team needs the ability
to deeply understand past performance and simulate potential
futures to be ready when a financial challenge presents itself.
Executives and shareholders
The leaders of your business need data insights to make deci‐
sions on how to move the company forward. Insights delivered
to these leaders take time to create, and these delays can have
impacts on the decisions made. Moving decisions closer to
when events happen is part of the democratization process. It’s
a key step to becoming proactive, rather than reactive, with
data. Improved analytics capabilities, combined with faster data
delivery and cleanup, mean that insights get to leaders sooner.
6
|
Chapter 1: Data and Analytics Challenges in Modern Organizations
Customers and prospects
Even your customers and prospects need access to data and
analytics. They want to know what items are in stock, how
much a given item costs, what delivery or pickup options are
available, and alternative options in case their item of interest
is out of stock. They want visibility into order status, when it
will ship, and, ultimately, when it will arrive at their doorstep or
be available for pickup. Today’s customers also expect organiza‐
tions to reach them on their preferred digital channel of choice
with information on upcoming sales and purchasing opportuni‐
ties. In the experience economy, customers demand that you
know what they want, when they want it, and their preferences.
Data also needs to break free from silos within an organization.
Cross-functional data access opens the opportunity to generate
insights that improve business performance. It’s easy to see how
finance units need insights from HR, supply chain, and sales teams,
but other areas benefit as well. Sales teams need to know when mar‐
keting campaigns influence their business. Marketing teams need
to understand the success of these campaigns from the sales teams.
Production teams rely heavily on the demand indicated by the sales
teams as well as the available resources from the supply chains.
Minimizing waste and absorbing demand spikes (as opposed to the
more general bottom-line improvement) require your business to
leverage multiple (often disparate) data sources to develop holistic
insights about critical business processes.
We can dive deeper into how access to data analytics affects your
organization by looking at things from an employee’s role. What
level of data access does each of your employees need? How knowl‐
edgeable are they in the full process of the business, and if they
were offered access to analytics, how much skill would they have
interpreting the results? Table 1-1 breaks this down for us to exam‐
ine. It also helps us understand where gaps exist between analytics
needs, business knowledge, and analytics tool expertise.
Data and Analytics Challenges in Modern Organizations
|
7
Table 1-1. Employee data needs and skill sets across a typical organization
Employee
role
Executive
Data and analytics
needs
Aggregate view of the
organization; relies on
others to generate
reports
Business
knowledge
Top-down view of the
organization with an eye
to the overall success of
the business
Analytics tool
knowledge
Likely is able to view
analytics reports, but is
probably not familiar
with analytics or
reporting tools
Middle
More granular view,
Strong business
May have some analytic
management usually specific to the
knowledge in its
knowledge but still relies
area of the business it
respective areas
on others to generate
manages
needed insights
Business
Need detailed reports to Experts in how the
May have some analytics
subject matter dig into how the
business functions
knowledge but still rely
experts
business is functioning
on others to generate
needed insights
Data subject
Need full access to data; May have some business Strong analytics skill set,
matter experts should have the skill set knowledge but usually
usually with the ability to
to analyze
gather requirements for
drill deeper into the data
organizational data
analytics from those
in order to provide
closer to the business
insights
Information
Needs the ability to
Familiar with the business Strong technical skills
technology (IT) support the gathering,
enough to ensure that
with installing and
storage, and
IT needs are met but
maintaining analytics
maintenance of data and probably not integrated
tools but may or may not
analytics tools
in the actual lines of
be an expert in how to
business
use them
Lines of
Data is needed to
Strong business
Probably little to no
business
understand how the
knowledge, though likely knowledge on analytics
business is functioning
localized to their
tool usage and may or
and make more localized particular role
may not have access to
changes
the data itself
Data scientists Data is everything they Focus is on data
Strong analytics tool
deal with, so they need and digging deep into
expertise, with emphasis
access and multiple
it, but may not be
on artificial intelligence
analytics tools
knowledgeable on the
and machine learning
nuances of the business
As we can see from Table 1-1, each role has differing data needs,
business knowledge, and analytics-tool skill sets. Those in the busi‐
ness who make decisions and need to derive insights from the
organization’s data typically lack the depth of technical skill neces‐
sary to generate results using analytics tools. Similarly, those with
strong analytical and technical skills typically lack the deep knowl‐
edge of how the business runs.
8
|
Chapter 1: Data and Analytics Challenges in Modern Organizations
The Issue: Insights Not Available
Where Needed
At the root of this complex predicament in which modern organ‐
izations find themselves is a simple question: who has access to
data, analytics capabilities, and insights? Business leaders are the
typical recipients of analytics reports in an organization, but they
may not be versed in the skills needed to interpret the results or
question the report’s validity. They rely heavily on the strengths of
their data teams to collect, analyze, and deliver insights for them to
react. From there, the results may trickle down to middle managers
but usually never reach the frontline employees. As Figure 1-3 illus‐
trates, there is an “analytics gap” in modern business between those
who have the questions and those who have the skills and tools to
answer them.
Figure 1-3. The Growing Divide: A chasm exists between those who
have analytics expertise and those who have questions.
Additionally, data for many companies is a commodity that is often
restricted and protected. Does it all need to be? Security typically
assigns permissions based on a mentality of prove that the user needs
access rather than prove there is a reason to restrict access to this data.
As a result, many functional areas within the company that could
benefit from analyzing data are restricted from accessing data in the
first place.
The Issue: Insights Not Available Where Needed
|
9
What does this restriction of data access mean for your company?
According to Dresner Advisory Services, 50% of all businesses have
some sort of lag in their data path, and at the same time, 50% claim
they make data-driven decisions.1 There is a correlation between
these two facts. The faster the data path, the more likely a company
is to make data-driven decisions. Dresner also indicates that 77% of
companies expect their data frequency requirements to increase in
the future. So, if your company wants to be a leader in your market,
you want to get ahead of this need. If you want to make real-time
decisions based on data, then you need the capability to analyze this
data as close to real time as possible.
The typical manual processes used by most organizations make this
scenario appear inevitable. Is there any way to enable all layers
within an organization to utilize data effectively and safely? This is
the vision of analytics democratization.
A New Approach: Democratizing Analytics
Democratizing analytics means making both data and analytics
capabilities available at all levels of the organization. To achieve
this, organizations will need to look for intuitive, easy-to-use analyt‐
ics platforms that are accessible to any skill set—from novices to
experts. In short, it’s data, and the ability to draw insights from
it, for anyone at any level of the organization—from leadership to
marketing to sales. This removal of obstacles preventing the deliv‐
ery of insights from data makes the company more nimble and
faster to react to growing change and increasing competition in the
marketplace.
Analytics democratization is made possible through a combina‐
tion of process and technology innovations. On the process front,
democratizing analytics requires implementing a standardized, gov‐
erned approach to sharing data and analytics capabilities with those
who could benefit from it. This doesn’t mean that data just goes
to everyone haphazardly. But it does mean that we need to shift
our mindset and approach from a data-hoarding to a data-sharing
mentality. Regarding technology, democratizing analytics requires
1 Dresner Advisory Services, “Data Latency Remains a Challenge as Data-Frequency
Requirements Increase”, December 14, 2022.
10
|
Chapter 1: Data and Analytics Challenges in Modern Organizations
user-friendly, flexible, analytics tools with reusable and repeatable
workflows to enable the new data beneficiaries.
The ability to analyze data at any level of the organization means
that employees at the middle management and business operations
levels are able to draw and interpret insights from data. Being closer
to the business operations means that these professionals can com‐
bine data-driven insights with functional knowledge of the business.
Combining these two things generates opportunities for continuous
strategic improvements at small and moderate scale. It also allows
those employees closest to the customer to improve the customer
experience with data-driven insights.
Broader analytics access means faster
insights and more innovation.
When companies provide analytics capabilities and data access at
all layers of business, they open the doors to innovation, faster
insights, and freedom to develop advanced analytics to make more
intelligent business decisions and be better prepared for the future.
These organizations that democratize analytics position themselves
to become leaders within their industry.
A New Approach: Democratizing Analytics
|
11
CHAPTER 2
How Democratizing Analytics
Improves Business Outcomes
We’ve seen that democratizing analytics can improve your business
performance, but what does that look like in a real-life example?
How can enabling employees with access to analytics tools provide a
business with success—and, more important—what types of success
is it able to achieve? Let’s take a look at a company that democratized
analytics to see how enabling employees with analytics allowed them
to overcome challenges and achieve success.
Case Study: Chick-fil-A
Chick-fil-A is an $11.3-billion quick-service restaurant company
that operates close to three thousand stores across the United States.
It has grown significantly over seven-plus decades, and this growth
has affected Chick-fil-A’s relationship to its data. Chick-fil-A wanted
to better understand its customers and personalize offers via a loy‐
alty application, but the company’s expansion generated pockets
of siloed data, and collecting data to report holistically became
challenging. Chick-fil-A’s analysts struggled to get access to data
and relied on IT support. Even when analysts did get the access
they needed, the data was often so immense that it was prohibitive
to query. Additionally, many of the analysts were familiar with
reporting tools but lacked the technical skills to access, retrieve, and
analyze the data effectively.
13
The hardships Chick-fil-A faced were hardly unusual: large amounts
of data were available from which to derive insights, but access was
limited to a select few who lacked the necessary tools and skills. The
company’s supply chain manager sought a solution that would allow
analysts to dive into the data and provide insights to employees
across business units. Chick-fil-A needed analytics democratization.
Chick-fil-A turned to Alteryx, an analytics automation platform,
to help access, prepare, blend, and analyze data across the organiza‐
tion. With Alteryx, the Chick-fil-A team documented and simplified
once-arcane queries and joined disparate data sources to provide
deeper and more informative insights. These new capabilities deep‐
ened the company’s understanding of customer needs and led to the
development of a new loyalty application. With user-friendly work‐
flows and the flexibility to examine its data in new ways, Chick-fil-A
is now able to plan for the future and better understand customer
preferences instead of just reporting on the past.
Analytics Maturity: The Hidden
Key Performance Indicator Driven
by Analytics Democratization
Chick-fil-A’s approach highlights how a company can go from strug‐
gling with analytics to leading or disrupting its market with analyt‐
ics innovations. This is an example of an organization advancing
its analytics maturity. Analytics maturity is an objective measure of
how well an organization employs analytics and data throughout
its operations. Analytics maturity is an important key performance
indicator (KPI), and those businesses that fail to measure and track
it may find themselves trailing their industry competitors.
Multiple independent researchers study and rank industries and
businesses by analytics maturity, and the results typically break
down into five levels of maturity:1
1 Alteryx, “The 5 Stages of Analytics Maturity”, INPUT by Alteryx (blog), July 2022.
14
| Chapter 2: How Democratizing Analytics Improves Business Outcomes
1. Beginner
These organizations are just starting to adopt analytics. They
usually have multiple siloed stores of data and lack execu‐
tive support for implementing additional data and analytics
resources.
2. Localized
Organizations at this level have started using analytics within a
few specific departments. They use analytics insights to make
some decisions, but investment is minimal.
3. Aspiring
These organizations are using analytics more broadly. They are
beginning to experience the benefits of being data driven.
4. Analytical
This group of organizations fully embraces analytics and uses
it extensively in decision making. High analytics competence
drives corresponding corporate performance, as these compa‐
nies set the standard for optimal data usage in their industry.
5. Competitor
This final group not only fully embraces analytics but also has
taken the next steps to leverage analytics for competitive advan‐
tage in their industries. Analytics opens new operating models
and revenue streams for these organizations, enabling them to
leapfrog peers or disrupt the marketplace entirely.
These five levels form a measuring stick for how far your com‐
pany has traveled toward analytics maturity. The levels not only
show you where your company falls on the maturity scale but
also illustrate what needs to be done to reach the next tier. Increas‐
ing your business’s analytics maturity is critical to operating more
efficiently, making better-informed decisions, and competing more
effectively—ultimately setting your business on a path to industry
leadership.
Concrete Benefits of Democratizing Analytics
Sharing data and analytics more broadly benefits your company
as a whole as well as its constituents and stakeholders. Take the
perspective of those directly affected by this improved access and
empowerment, as shown in Table 2-1.
Concrete Benefits of Democratizing Analytics
|
15
Table 2-1. Examples of the beneficial impact of democratizing analytics for
your employees, suppliers, partners, and customers
Individuals
Executives
Shareholders
Middle
management
Analysts
Employees
Customers
Suppliers
Partners
Impacts of democratizing analytics
Less time between asking a question and getting a data-derived insight
Insight into the business without waiting for reports
Insight into area-specific data analysis and the ability to make insight-driven
decisions to improve business success
Less time generating reports and more time focused on value-added diagnostic
and advanced analytics
Granular-level insights inspiring innovation and improvement in business
processes
Faster response times and better customer service
Better understanding and adaptability to meet your company’s demands
Cooperative understanding and competitive edge against competitors in your
market
These compounding benefits affect the performance of your com‐
pany as a whole. As data analytics becomes more deeply integra‐
ted into your organization, its usage provides new insights into
the challenges and opportunities you face. These insights help you
make smarter decisions at an executive level and throughout your
organization. Faster insights mean issues and opportunities can be
identified and addressed more quickly—top to bottom.
As employees adopt easy-to-use analytics tools and resources, they
can ask and answer questions specific to their roles. Achieving
these granular insights improves localized performance and increa‐
ses frontline success.
For example, improved access to analytics improves purchasing
supplies, providing insights into shipping and component costs. It
gives insights into online transactions, improving interaction times,
user experience, and customer satisfaction. Customer data offers
demographic and purchase tendencies and trends, allowing you to
better provide the goods and services that customers desire. Taken
together, these kinds of data-driven improvements drive down
costs, increase revenue, reduce risk, improve customer satisfaction,
and enhance your competitive posture—all of which impacts your
bottom line.
16
|
Chapter 2: How Democratizing Analytics Improves Business Outcomes
At this point, the benefits of democratizing analytics within your
organization are likely evident. However, some functions may make
you feel uncomfortable with opening data access to more users—
often the case with IT. Let’s take a look at how you can address con‐
cerns about data security, privacy, and governance while continuing
to move toward analytics maturity.
Concrete Benefits of Democratizing Analytics
|
17
CHAPTER 3
Democratizing Analytics
and Data Governance
Is it possible to provide greater access to data across your business
while still maintaining high security and privacy standards? After
all, sensitive or personally identifiable data, such as employee data
and customer data, typically has restrictions on who can access it,
for what purpose, and when. Sensitive information faces increased
risk of theft, loss, or exposure when more people have access. Pro‐
viding additional access means you must undertake more frequent
and broader audits to ensure that sensitive data is kept secure.
To understand how to implement analytics democratization appro‐
priately, let’s quickly review the principles of data governance:
Accountability
Security is typically relegated to one department, such as IT;
however, everyone in your organization must understand the
value and risks involved with data access to ensure that the
data is used properly. To do this, accountability has to cross
all areas of your organization and is best represented by a
governing body.
Regulation
Your organization needs well-developed, well-defined rules for
data usage and access. These rules should be flexible enough to
apply to all levels of the organization but restrictive enough to
ensure the security and privacy of the data being accessed.
19
Stewardship
Each area within the organization needs expertise around its
data. These data stewards understand the data, its security con‐
cerns, and its nuances. They provide the basis on which the
regulation of the data can be built.
Quality
Data is useless if it is incomplete or incorrect. Standards related
to accuracy and completeness need to be established. In addi‐
tion, the data areas need a rigorous testing process to ensure
that as new data comes in, it is as complete and error free
as possible.
How do you gain the benefits of democratizing analytics without
increasing the risks to data security and privacy? Let’s look at how
companies can maintain solid data governance and why it works
hand in hand with analytics democratization.
Case Study: UBS Group AG
UBS Group AG (aka Union Bank of Switzerland or UBS), a global
banking leader, needed data insights throughout its organization.
With more than 70,000 employees and offices in more than 50
countries, the size and spread of the company presented a huge
challenge to any solution applied across the entire organization. Like
other companies, it was concerned with making data and analytics
tools available while also maintaining governance over the data. It
needed a repeatable, easy-to-use, and easy-to-maintain system to
provide analytic insights across the organization.
To move forward quickly, UBS broke the solution into three key
parts. First, it utilized a self-service method for data access and
preparation via the Alteryx analytics automation platform. This
applied not only to existing data sets but also across data sets. It
developed self-service templates using Alteryx no-code workflows
to gather, clean, transform, and store data. By using workflows, it
made analytics capabilities not only dynamic and repeatable but also
easily shared and auditable. Once a data-preparation algorithm was
established and validated, others in the organization could reuse it
for analysis of their own.
The second part of the UBS solution integrated machine learn‐
ing. It empowered employees to use existing workflows to gather,
20
|
Chapter 3: Democratizing Analytics and Data Governance
clean, and provide reliable data, and it provided an opportunity
for employees to test and manipulate the data based on theoretical
changes in the business. This meant that users could not only use
analytics automation to present information on what had happened
within the company but also simulate new scenarios and improve
business performance.
The final step in the UBS process addressed data governance. The
workflows generated within the organization were broken down
into types based on what they did and who interacted with them:
End user processes
These workflows are owned and governed by the organization
as a whole and contain automation. They are created by indi‐
viduals with business knowledge throughout the company (not
members of IT). Examples of these workflows include revenue
forecasting, risk ratings, and portfolio management.
IT-managed applications
These are more complex workflows that involve in-house or ITrelated coding languages like SQL, Python, or R. These work‐
flows are managed by IT via a formal software development
lifecycle. Examples of these workflows include pricing and valu‐
ation, transaction monitoring, and fraud detection.
Automated applications
These workflows run on a regular basis or are triggered by other
events. These types of processes include analyzing customer
activity and other outward-facing interactions like chatbots.
Additional workflows
Workflows that do not fit neatly into the previous categories are
classified in a separate group typically containing non-businesscritical or ad hoc analysis. They are usually developed at the
line of business for quick analysis. Examples of these workflows
include equity and economics research as well as discounted
cash flow analysis.
These classification guidelines each come with requirements created
by the company. They are shared with users to help them define
their workflows. These guidelines give the company the ability to
add, remove, or change restrictions based on data security and
privacy, and they leave freedom for users to develop workflows
that meet their analytics needs. This approach empowers users to
Case Study: UBS Group AG
|
21
correctly classify their workflows as well as govern them to meet
the standards required by the organization. Any analytics workflows
developed within the organization are regularly run through the
governance process to determine that they function correctly and
generate reliable results. UBS’s best practices dictated that each
workflow must have a sponsor, an owner, and a validation team
led by a head validator. Together with a well-defined method of
registering workflows, this ensures that workflows are kept up to
date and in compliance with organizational requirements.1
Even though banking is one of the most heavily regulated industries,
UBS was able to democratize data analytics to its employees through
a thoughtfully controlled development process combined with easyto-use, easy-to-share, and easy-to-repeat analytics workflows. UBS
implemented analytics democratization in a way that was both well
governed and impactful to its business.
Learnings: UBS’s Category-Driven Approach
The UBS study highlights key challenges you may face and strategies
you can employ in the process of democratizing data in your own
business.
First, the study makes clear that IT cannot be there to do every‐
thing involving data. IT is generally fully occupied keeping the
transactional systems functioning. Additionally, IT typically lacks
the necessary business knowledge to accurately create and maintain
business workflows for insight generation. So, UBS empowered its
business users to develop, maintain, and help govern their work‐
flows, freeing IT to focus on technology-specific aspects of the
business.
Second, UBS highlights the benefits of empowering business users.
Governance was decentralized, engaging the owners of the work‐
flows. Rather than requiring a single group to figure out what was
happening within tens of thousands of workflows across the com‐
pany, each workflow was managed and maintained by the user who
created it and governed by a localized validation process supported
by well-documented company-wide classifications. This approach
1 Alteryx, “UBS AG: On Governance of Models and Workflows in a Regulated
Environment”, video.
22
|
Chapter 3: Democratizing Analytics and Data Governance
ensures that even the smallest localized workflows are governed
and maintained. Additionally, since the data is analyzed within the
business units, far more granular insights become possible, as the
analysis is infused with users’ intimate knowledge of their products,
customers, and business functions.
Finally, UBS illustrates the power of analytics workflows. By com‐
bining data collection and cleanup processes into a workflow, the
company overcomes three significant issues. First, classifying the
workflows means that they can easily be shared and reused by others
within the organization based on the type of flow rather than the
data it accesses. Second, sharing analytics workflows means that data
once siloed to a particular area can now be accessed and utilized
by others who may benefit from similar analytics insights. Last,
using workflows provides process transparency on how areas of the
organization utilize and manage process-specific data. So, over the
course of five years, UBS went from having one analytics user to
having more than three thousand analytics users around the world
on every continent. The company runs more than six thousand
workflows each day and uses analytics for fraud detection, capital
planning, and forecasting.2
Case Study: BT Group
Let’s consider another example of how governance and democratiza‐
tion work together. BT Group (aka BT and British Telecom) is a
telecommunications and network provider in the United Kingdom,
serving more than 180 countries. As part of providing a wide range
of services related to voice, mobile, and broadband, the company
is required to meet strict standards in reporting while also being
competitive in its market. This leads to serious obstacles not only
with collecting and combining all data but also with doing so in the
correct order.
Before BT embarked on analytics democratization and automation,
it ran a process it called the “Cascade.” In simple terms, it was a
huge process linking data from more than 140 spreadsheet models
together sequentially to generate necessary reports. It was cumber‐
some, error prone, and time consuming to run. It often took up
2 “Alteryx (AYX) Q2 2021 Earnings Call Transcript”, The Motley Fool Transcripts,
August 4, 2021.
Case Study: BT Group
|
23
to four weeks to execute and required multiple runs before the
necessary reports were successfully completed.
Obviously, the Cascade was not sustainable. Not only did it illustrate
the difficulties of working with multiple restricted data sources,
but it also highlighted the obstacles of 1,500 unique data sources
and methodologies. No one was able to comprehend and maintain
the overall workflow, as it was simply too complex, with each area
requiring its own unique set of rules and processes. Knowledge
transfer was difficult, if not impossible, and annotation was chal‐
lenging within the format of the spreadsheet data.
BT worked with PwC (PricewaterhouseCoopers) to find a solution
that would render the Cascade obsolete. The reporting requirements
still needed to be met, and the individual areas within the organiza‐
tion still needed to function to meet their own regulatory require‐
ments. How could the annual reporting combine all these disparate
data sources to produce the necessary reports without the need for
months of data preparation and development?
PWC helped BT move its process into analytics workflows. Rather
than each portion of the company generating data that required
manual manipulation and weeks of transformations and joins, the
workflows introduced automation to simplify the process. The
workflows allowed the analytics process to be accessible at all levels
of the organization. This analytics democratization gave each team
the access and ability to manipulate area-specific data to meet the
requirements of the annual reporting process.
BT’s intelligence analysts worked with PWC to establish guidelines
for tool usage, data paths, and conventions across the company.
The governance guidelines they provided meant that each of the
distinct business units had tools and strategies to work cooperatively
with one another without having to worry about developing a gov‐
ernance model itself. Additionally, these guidelines meant that once
the individual analytics workflows were complete, they were trans‐
parent and easily reviewable through an audit process to ensure that
the calculations were complete and accurate.
The final aspect of introducing analytics workflows was the doc‐
umentation embedded directly within each workflow. Each busi‐
ness area was provided with the necessary tools to document its
workflows, ensuring a smooth knowledge-transfer process among
company personnel. The teams also leveraged simple innovations
24
|
Chapter 3: Democratizing Analytics and Data Governance
like color coding and links to further organize and chronicle their
processes. This documentation made audits, reviews, and transfers
of the workflows essentially seamless.
All of these strategies employed by BT and PWC replaced the Cas‐
cade. Now, BT utilizes organizational strategy and analytics auto‐
mation to accelerate the report-generation process. The efficiency
of the annual report process has improved by more than 75%.
Additionally, the added transparency into the process benefits gov‐
ernance by providing new insight into the processes and calcula‐
tions. Lastly, automation and reduced redundancy save BT money
in several ways. Instead of spending hours running the Cascade,
employees now have time to focus on removing unnecessary pro‐
cesses and improving data workflows.3
These examples illustrate how analytics democratization harmonizes
with analytics governance. Sharing data and analytics empowers
your whole organization. Democratized analytics provides insight
from the executive level through the customer, leading to improve‐
ments in business process efficiencies, decreasing obstacles and
issues, and resulting in higher quality customer service.
Democratized analytics drives efficiencies,
decreases obstacles, and raises quality.
Data governance and security is a way of life in modern business,
and by using self-documenting analytics workflows, training, and
decentralized governance models, you allow your company to take
full advantage of analytics without compromising data security and
privacy.
These guiding principles provide the basis for successful analytics
democratization. Now let’s explore how your organization can get
started on the path to democratized analytics.
3 Alteryx, “How BT Is Automating Regulatory Compliance with Alteryx and PwC”,
Alteryx Customer Story.
Case Study: BT Group
|
25
CHAPTER 4
Best Practices for
Analytics Democratization
The journey to democratizing analytics and advancing your organi‐
zation’s analytics maturity is both challenging and rewarding. You
may encounter one or more of the following trials along the way:1
Data quality
Many organizations have an overwhelming amount of data.
That data is typically decentralized, low in quality, and prohibi‐
tive to connect to. As such, when blending in additional data
sets or producing insights, some doubt may be cast on the
accuracy of the results.
Enterprise adoption
Analytics democratization aims to drive adoption throughout
the organization. This means that buy-in is needed from ana‐
lysts, managers, leaders, employees, customers, shareholders,
and more. It’s challenging to create a shared desire across
so many constituencies to understand and utilize data and
analytics to make analytics-informed decisions in the business.
1 David Alles, Research Brief: “Competing on Analytics by Industry”, International Insti‐
tute for Analytics, 2020.
27
Leadership support
For a democratization effort to gain traction, the leadership
of the company must understand the benefits and be suppor‐
tive of the initiative. It needs to understand that analytics
democratization is not opening the organization to vulnerabil‐
ity but rather providing valuable insights into—and improv‐
ing—organizational processes. The involvement of leaders
ensures that the move toward democratization is strategic and,
importantly, funded.
Technology
Shifting to an analytically-driven organization requires the right
technology. When democratizing analytics, it’s not complex
analytical software that you need, but rather an easy-to-use,
easy-to-repeat, and easy-to-share method for providing analyt‐
ics capabilities and data access to those with fewer technical
skills. This approach also requires that data be stored in a
responsive, easy-to-access location and made accessible to those
who need insights from it.
Analysts and trainers
Disseminating new skills and knowledge to the workforce
requires support from technical experts and trainers. Their roles
will be not only to understand the analytics solutions provided
but also support the rest of the company as employees learn to
use new analytics capabilities to generate insights.
Let’s look at how a leading global organization navigated these
bumps in the road.
Case Study: Phillips 66
Phillips 66, an energy company, faced all of these challenges as
it worked to build a data-driven culture. Phillips had a dedicated
analytics team that reached out to employees across the company to
understand their analytics use cases and skill levels. What the team
discovered was that many users were looking for a way to automate
analytics processes managed in spreadsheets. The analytics team
determined that a new approach would be beneficial.
As Phillips 66 already leveraged Alteryx within the analytics team, it
decided to expand usage of the platform beyond the analytics team
environment. It hoped that by providing analytics tools directly to
28
|
Chapter 4: Best Practices for Analytics Democratization
frontline workers (rather than making them rely on the analytics
team and wait to receive information), the organization would real‐
ize the benefits of analytics democratization.
The analytics team started small, with a group that was already
somewhat fluent in analytics. The strategy was to leverage word of
mouth to encourage other employees to get involved in the analytics
process. The training was successful, and soon many employees out‐
side the initial group started asking to be involved in analytics. This
led to a new complication for the analytics team: personally training
everyone was no longer a viable option. Instead, the team would
need to develop a self-sustaining culture to nourish this newfound
appetite for data analytics.
The analytics team at Phillips 66 approached these training and data
challenges by cleverly solving two problems at once. To encourage
frontline workers to adopt analytics processes and improve under‐
standing of how the organization functioned, the analytics team
began requiring prospective trainees to do two things to gain admit‐
tance to training courses: 1) share their own data obstacles; and
2) get an Alteryx license. This strategy ensured that the training
would solve real data problems that trainees faced while providing
hands-on experience with the new analytics software.
The analytics team continued to provide training for new analytics
users, building relationships with those interested in learning more
about their data. This ongoing practice not only introduced the new
analytics users to analytics experts but also provided the analytics
team with insights into who, how, and where analytics was being
applied at Phillips 66. At the same time, the analytics team fostered a
broader analytics community across the organization. This commu‐
nity supported itself through knowledge sharing, innovation, and
documented, repeatable analytical techniques. As such, the analytics
support model organically evolved from relying exclusively on tech‐
nical experts to being community driven.
These organizational shifts created another opportunity for the
analytics team. By illustrating the benefit of providing analytics
capabilities to employees throughout the organization, the analytics
team demonstrated the value of a data-driven approach to Phillips
66’s senior leadership. The grassroots model quickly and effectively
convinced executive leadership that a data-driven culture was not
only beneficial but also required to move forward in the industry.
Case Study: Phillips 66
|
29
Phillips 66 has evolved into a data- and analytics-driven organi‐
zation. It now utilizes analytics at all levels of the business and
relies on analytics insights to drive the company. The analytics team
within Phillips 66 still holds regular training sessions on how to use
data and analytics as well as the software; however, the communitybased support structure that the analytics team established allows it
to spend more of its time focusing on Phillips 66’s most challenging
data questions.
How does Phillips 66’s experience in data democratization help you
in your own company’s transformation? Let’s look at some of the
steps that Phillips 66 took.
Engage Experts for Guidance
The democratization of analytics often starts with those most famil‐
iar with data analysis: the analytics team. The individuals in your
organization who deal with data and analytics questions understand
the benefits of their skills and the insights they provide. The chal‐
lenge for those experts lies either in finding a method to quickly
distribute actionable insights across the business or in developing
a solution to help more employees generate their own insights.
Engage with your analytics team, and they may illuminate a path
forward for your business.
Gain Executive Support
A data-driven culture encourages growth, efficiency, high perfor‐
mance, and innovation-driving product and service improvements
as well as cost savings for the company. Identify and measure those
relevant KPIs in your organization, and estimate both where quick
improvements can be made and where the most ROI is expected.
Analytics democratization increases analytics maturity, and thirdparty research shows that analytics maturity impacts an organiza‐
tion’s bottom line and competitive position in its industry.
The broader organization will need to understand the benefit of
investing time and resources in analytics initiatives. One way to do
this is to encourage executives to model the new analytics-driven
behaviors desired by leveraging data and analytics as part of their
leadership process. Encouraging leadership to adopt analytics into
its workflow not only provides it with valuable decision-making
30
|
Chapter 4: Best Practices for Analytics Democratization
insights but also demonstrates for the rest of the organization the
benefits and outcomes desired from the analytics democratization
process.
Start Small
Analytics democratization doesn’t happen overnight. You must first
demonstrate value and build from there. This can be done with
a proof-of-concept trial within the organization. Engage candidate
“power users” or a small, motivated group within the company and
provide them with new analytics software capabilities and training.
Aim to help them solve a well-known or long-standing problem
with analytics. As more and more members of the organization see
their workloads simplified thanks to analytics, they become advo‐
cates for the transformation.
Demonstrate Benefit
It’s common for individual contributors, mid-level managers, and
senior leaders to resist change. So, you must make clear the benefits
of analytics democratization to each constituent—from the frontline
worker who will need to learn new tools and techniques to the
busy executive who will need to consume insights in a new way.
You may want to consider constructing incentives to encourage
behavior change. Phillips 66 did this by requiring employees to
bring problems to solve in order to get an analytics automation
software license and training. By solving a localized problem for an
individual or a team using automated analytics, trainers provide not
only a solution to the problem but also an incentive to utilize the
new analytics capabilities further.
Train the Trainers
The goal of analytics democratization is to deliver analytics capabil‐
ities to anyone and everyone who would benefit from them. For
larger organizations, this can be a challenge. The team responsible
for training in the use of analytics software may quickly become
inundated with requests for support. In this case, find in-house
power users to enable and empower to train others in analytics
software as well as best practices.
Train the Trainers
|
31
Create a center of excellence by pulling together the analytics exper‐
tise within your organization and providing them with the tools to
distribute support and training of analytics across the organization.
Enable them to explore technologies that manage, store, and deliver
clean, consistent data that supports multiple departments, end-user
questions, and diverse use cases. Encourage them to build a com‐
munity around data analytics by leveraging tools such as blogs,
hackathons, and social media within the organization. Teaching
employees to support one another eases the burden on dedicated
analytics and technical teams, so they can focus on delivering not
just training but also other value-add work.
Make Analysts the Foundation
Your analysts can catalyze increased analytics maturity for your
business as they provide the hands-on training, demonstrations,
social content, and reference materials needed to support your ana‐
lytics democratization initiative. Ensure that they want to stay and
keep the company moving in the right direction. As your company
develops an analytics culture, these dedicated analytics experts act
as internal thought leaders. They form the foundation from which
a self-supporting community can grow, fostering the exchange of
ideas and practices. Confirm that these analysts are empowered to
leverage and experiment with new data.
Establish an Analytics Council
Create a cross-functional group of leaders that maintains oversight
of analytics resources and activities across the business to ensure
engagement in analytics, enable data governance, and identify and
address issues. Establish agreement among teams regarding data
security, quality, and auditing as well as analytics provisioning and
user management. The analytics council should also establish mech‐
anisms for cross-team collaboration and the sharing of best practi‐
ces across the organization.
32
|
Chapter 4: Best Practices for Analytics Democratization
Keep the Process Moving
Democratizing analytics generates returns quickly. Be sure to
highlight the benefits to leadership, shareholders, customers, and
employees. Use your initial analytics democratization victories to
generate the momentum needed to drive the program forward and
advance the organization’s overall analytics maturity.
When obstacles appear along the way, break them into their con‐
stituent parts and prioritize those issues that appear to be quickly
addressable. Use tangible examples of your success in one busi‐
ness area to promote analytics democratization to those who are
more reluctant to change. When you create tangible localized
improvements, you spark analytics democratization to spread fur‐
ther through your company. We have provided a checklist in Appen‐
dix A to help you get started on your journey.
Empowering your whole organization with analytics creates mul‐
tiplying positive impacts. The ability to make ground-level datadriven decisions without delay helps ensure your business will
withstand global, economic, and industry change.
Withstand global, economic, and industry
change with data-driven decisions.
Analytics democratization provides you and your team with insights
into how the company is performing and where gaps in your pro‐
cesses can be filled. Although there can be challenges in implement‐
ing a new approach to analytics in the business, you can surmount
them by strategically building and embracing a supportive, adaptive,
and data-minded community within your organization. With these
strategies, you can lead your organization to greater analytics matur‐
ity and a position of industry leadership.
Keep the Process Moving
|
33
APPENDIX A
Eight Steps to Getting Started
with Analytics Democratization
Use the guidelines provided in this article to start your analytics
democratization journey:
☐ Determine where your organization currently falls on the ana‐
lytics maturity scale.1 Alteryx provides a free analytics matur‐
ity calculator developed in partnership with the International
Institute of Analytics.
☐ Identify a set of candidate projects that are aligned to your
strategic goals. Prioritize these projects with a cross-functional
team. Select one to get started.
☐ Identify the project team who will work with you on democra‐
tizing analytics. Create a charter and define roles and responsi‐
bilities for the team.
☐ Obtain allies within your organization who will adopt and
champion the analytics democratization process with you.
☐ Converse with leadership and your support teams to start the
process and secure funding and resources for the project.
1 Preethika Sainam et al., “How Well Does Your Company Use Analytics?”, Harvard
Business Review, July 27, 2022.
35
☐ Identify the obstacles that may stand in the way of analytics
democratization, and address them with your analytics commu‐
nity and leadership.
☐ Share this article with others to help them understand the ben‐
efits that analytics democratization and analytics maturity will
generate for your organization.
☐ Celebrate successes early and often at all levels across the
organization.
36
|
Appendix A: Eight Steps to Getting Started with Analytics Democratization
Acknowledgments
We want to express our immense gratitude to the magnificent peo‐
ple who helped bring this report to life.
A big thank you goes to Kalyan Ramanathan and Keith Pearce for
their support on this project. Thank you to Steve Brodrick and
Alan Jacobson for the enlightening conversations. Also, thanks to
Christopher Gardner for his fantastic contributions and maintaining
good humor in the face of unremitting feedback rounds and a
continual flow of new ideas and information.
Much appreciation to our technical reviewers Mattia Ferrini, George
Mount, and Tobias Zwingmann.
Finally, we want to thank the members of the Alteryx community
who generously shared their experiences, best practices, and success
stories. You inspire us with your passion, your ingenuity, and your
skill in using automated analytics to spin raw data into meaningful
business outcomes for your organizations.
37
About the Authors
Melissa Burroughs is the Director of Technology Solutions Market‐
ing at Alteryx, where she crafts messaging and positioning strategy
for the Alteryx portfolio in the context of multivendor solutions.
She is an accomplished technologist, writer, and public speaker,
bringing a passion for communication together with diverse experi‐
ence in science and analytics.
With over 15 years in the software industry, Melissa has originated
RAD development, professional services, and product marketing
for a number of early-stage technology firms. Her prior work also
includes accelerator physics research, medical data analytics, and
other academic positions.
Follow Melissa on Twitter @MelBuzz and connect with her on
LinkedIn.
David Sweenor is the Senior Director of Product Marketing at
Alteryx, a top 25 analytics thought leader, an influencer, interna‐
tional speaker, and acclaimed author with several patents. He is a
specialist in the business application of AI, ML, data science, IoT,
and business intelligence and is responsible for the go-to-market
strategy for Alteryx’s analytics portfolio.
With over 20 years of hands-on business analytics experience,
Sweenor has supported organizations including SAS, IBM, TIBCO,
Dell, and Quest in advanced analytic roles.
Follow David on Twitter @DavidSweenor and connect with him on
LinkedIn.