How to create a data-driven dynasty

How to create a data-driven dynasty Best Practices Guide
How to create a data-driven dynasty
Identifying key focus areas to foster a more data-centric environment
and generate increased ROI from digital analytics investments
Today, most companies rely on digital analytics tools to measure the performance of their online
marketing initiatives, such as websites, campaigns, mobile applications, and so on. Although most
marketers understand the value of data and have a strong desire to become more data-driven, many
organizations still struggle to tap into the full potential of their digital analytics investments. In many
ways, executives at these companies may feel as though they’re still waiting for the promised returns
from these powerful, but often underutilized tools. In their data-driven journey, some organizations have
realized that simply having a digital analytics tool in place is not enough. Indeed, technology is only one
of several key factors needed to be successful in digital analytics.
This white paper is designed to help executives identify key focus areas that can foster a more datacentric environment and generate a greater return from their digital analytics investment. And it provides
digital analytics practitioners with strategies for overcoming common challenges that may limit the
adoption of digital data and the success of a digital analytics program.
1: Playing or playing to win
2: Parallels between sports and
digital analytics
3: Building a data-driven dynasty
3: A balanced approach
4: Digital governance factors
11:The path to a data-driven dynasty
12:Common pitfalls along the datadriven journey
13: Laying a solid foundation
In this paper, you will be introduced to a new digital governance framework and maturity model that will
help you in establishing a best-in-class digital analytics practice and data-driven culture at your
organization. We explore several key factors—leadership, strategy, people, process, technology, and
organizational dynamics—that contribute to the long-term success of any digital analytics program.
These concepts were developed and refined by working with hundreds of companies at different stages
in their digital analytics maturity. Although each organization faces unique challenges in its data-driven
journey, these concepts and best practices will help you identify critical gaps and deficiencies in your
current approach, and provide a guide for establishing a data-driven dynasty at your company. If you
want to use digital data to inform decision-making and optimization efforts, this paper lays a crucial
foundation for accomplishing these goals.
14:Case study: Skandinaviska
Enskilda Bank
Playing or playing to win
15:Building a roadmap for your datadriven dynasty
When it comes to digital analytics, is your company playing or playing to win? There is a difference. As
any avid sports fan can attest, there are many professional sports teams that field a team of players year
after year but don’t achieve much success—especially not postseason success. One wonders if the team
has a long-term game plan. Some team owners appear to be content with mediocrity — and even when
losing money in the process.
15:About the Author
On the other hand, there are some sports teams that strive for something higher—to become champions
and in some unique cases—dynasties. The National Hockey League’s (NHL’s) Edmonton Oilers won five
Stanley Cups (1984–90). The Chicago Bulls won six National Basketball Association (NBA) championships
in eight years (1991-98). Michael Schumacher and his Ferrari team won five straight Formula One World
Championships (2000-04).
Why were these teams so successful? You might say having superstars like Michael Jordan and Wayne
Gretzky probably didn’t hurt. However, before you drop everything to recruit an analytics all-star,
consider how the Bulls and the Oilers went seven and five years, respectively, with their superstar players
before winning their first championship. In actuality, it was a combination of different factors that made
all these teams truly successful.
Parallels between sports and digital analytics
There are interesting parallels between the world of sports and the world of data analytics. Let’s take
a closer look at several components that were integral to the success of these championship teams.
Leadership—When you evaluate successful sports teams, you’ll typically find they are owned and
managed by people who are prepared to make the necessary investments in talent, equipment, and
facilities. Their coaches are adept at extracting optimal performance from players and developing
strategies that take full advantage of their unique talents. In many cases, these championship teams
also have strong team captains who both inspire greatness and lead by example.
Guidance and sponsorship from company leaders will help digital analytics to prosper within your company.
Creating a sustainable data-driven
dynasty takes:
•Guidance and sponsorship from
company leaders
•A clear strategy to unify priorities
•The right mix of people and
•Well-defined processes to obtain
efficiency and effectiveness
•The right technology
•A company culture that fosters
Strategy—These high-performing teams have a clear vision of what it takes to win championships.
They share a common strategy for how to achieve their goals—the entire team, from players to
coaches, is determined to do whatever it takes to fulfill their objectives. Frequently, less successful
teams are not unified or aligned in their approach, which leads to suboptimal results.
A clear digital strategy enables your digital analytics group to align its measurement activities to the
key priorities of your business and thrive as an integral part of your organization.
People—Most fans acknowledge that championship teams are made up of more than just one or
two all-star players. Even superstars, such as Michael Jordan and Wayne Gretzky, needed a solid
supporting cast of teammates before they could compete for a championship. The legendary racing
driver Michael Schumacher needed equally talented engineers, mechanics, and pit crew members
on his Ferrari team in order to dominate Formula One racing.
Having the right talent and sufficient resources on your digital analytics team is crucial to your
long-term, data-driven success.
Process—The managers and coaches of championship teams are constantly developing and refining
their tactics to help their teams execute more efficiently and effectively. These teams develop
specific plays and in-game strategies that help them to beat their opponents. Remember that a
superior team can be defeated by an inferior competitor when it fails to execute its game plan.
To have an effective digital analytics program, it is important to develop internal best practices and
well-defined processes.
Technology—Many professional athletes depend on high-quality equipment to successfully
perform their roles. In some professional sports, such as auto racing or cycling, technology is a
major component of the sport. Frequently, not having the right equipment can actually put athletes
at a major disadvantage compared to other competitors.
In digital analytics, technology plays a key role in fostering a data-driven organization.
Organizational dynamics—Championship sports teams forge a winning culture that permeates
their entire organization. Everyone puts the greater goals of the team before individual agendas or
internal politics. None of these championship teams immediately started with a winning culture—
over time it was developed, nurtured, and enshrined.
Plugging in a digital analytics solution will not automatically transform your company into a datadriven organization—a data-driven culture will need to be purposefully cultivated over time.
Championship teams bring together all of the required pieces on a repeated basis to form legendary
sports dynasties. They clearly play to win, not just play. When it comes to digital analytics, we’re
striving for sustainable excellence—not just a few consecutive winning seasons. However, companies
that want to create a data-driven dynasty can follow the example of championship sports franchises
by focusing on these six key areas: leadership, strategy, people, process, technology, and
organizational dynamics. These areas form the foundation of the Digital Governance Framework.
How to create a data-driven dynasty Best Practices Guide 2
Digital Governance Framework
•Leadership—Executive buy-in and
support for the digital analytics
•Strategy—Clarity and alignment
around key business goals for
evaluating online performance
•People—Resources, expertise, and
the appropriate team structure to
run an effective analytics practice
•Process—Procedures, policies,
standards, and workflow for
deploying and using digital data
•Technology—Right analytics,
optimization, and digital marketing
tools to meet key business needs
•Organizational dynamics—Culture,
internal politics, and history that
influence analytics adoption
Building a data-driven dynasty
Through the sports analogy, you have learned about a new framework for analytics governance—
the Digital Governance Framework. Just like championship teams, your company must address
several different areas in order to sustain its data-driven success, which requires more than just an
emphasis on data management or governance.
Digital governance creates an environment where digital analytics can succeed. It encompasses not
only data measurement aspects, but also taking action on the data and deriving recurring value
from it. Being an “action-agile” organization goes hand-in-hand with being data-driven because
most companies will want to quickly translate business insights into tangible optimizations. Rather
than focusing on just data collection when considering the key areas of this framework, think more
holistically about how the data within your organization can be turned into action on a regular and
timely basis. Your organization needs to be both data driven and action agile. (Note: For this white
paper, data driven will refer to being both data driven and action agile.)
The Digital Governance Framework includes another somewhat sticky factor that influences your
company’s evolution to becoming more data driven—organizational dynamics. It is often the
elephant in the room that people rarely talk about, but face every day in the workplace. This factor is
comprised of your company’s culture, its internal politics, its history with analytics in general, and the
specific reputation of your web analytics program. These different aspects of your organization create
an environment that makes your company supportive, neutral, or resistant to being data driven.
A balanced approach
Ultimately, your organizational
dynamics determine whether
your company is going to face an
uphill battle or gain some downhill
momentum in its data-driven quest.
Your corporate environment affects
your initial approach. Over time
the organization dynamics can be
changed and then the new dynamics
can influence future efforts.
At a high level, this framework may look similar to the familiar IT governance framework of
“people, process, and technology” that various technology vendors have espoused for enterprise IT
systems, for example, customer relationship management (CRM), enterprise resource planning
(ERP), business intelligence (BI), and so on. However, upon closer evaluation the Digital
Governance Framework differs from the traditional model in some subtle but important ways.
Figure 1. Digital governance requires a balanced approach
As shown in the diagram above, the framework emphasizes the interdependent relationships
between the different factors in the model. Some people might argue that you could simplify the
framework by including leadership in the people category and strategy in the process category.
However, these two areas play critical roles in creating a data-driven organization and need to be
kept separate to emphasize their importance. It really begins and ends with leadership. Although
senior management initially approved the budget for your current analytics solution, that can’t be
the extent of their involvement. Without executive sponsorship and buy-in, your program will go
nowhere. It happens frequently—talented, intelligent analytics professionals become trapped in
providing little more than technical support and basic reporting. They are unable to take their
programs to the next level without senior management involvement.
How to create a data-driven dynasty Best Practices Guide 3
Next, you need to have a clear digital strategy with well-defined, agreed-upon business goals and
key performance indicators. It’s hard to provide relevant reports, meaningful analyses, and impactful
optimizations if your digital strategy is nonexistent or ambiguous. Because digital analytics is
ultimately about optimizing online performance, it is critical that your analytics team understands
what needs to be measured. Your team might be able to guess what’s important to the business, but
it’s much more effective if key stakeholders can clarify and agree on what needs to be measured and
optimized. Although gaining organizational alignment may be easier said than done, it’s a crucial
step because your digital strategy will provide a foundation for all of the other factors.
Organizational dynamics are like a
crosswind that influences how you
balance the people, process, and
technology factors.
Strategy is dependent on leadership because senior management’s input is essential to clarifying
the digital strategy. The rest of the traditional factors—people, process, and technology—balance
upon strategy because it influences all of these factors in different ways (for example, where to
allocate analytics staff, what analyses to perform, what analytics tools to purchase, and so on). The
balancing analogy highlights the importance of taking a balanced approach with people, process,
and technology. If your company loads up on technology without having sufficient resources or
processes in place, your approach will tip over and you won’t be able to achieve your goal.
Organizational dynamics are like a crosswind that influences how you balance the people, process,
and technology factors. Initially, you may need to counterbalance a particular strong crosswind in a
certain way (for example, using more processes to overcome cultural challenges) and then adjust
or rebalance your approach as these winds can change direction over time.
Digital governance factors
We’ve covered the six top-level categories, and now we’ll examine the subcomponents that make
up each category. As you review the different subcomponents within each category, you may
notice that a particular element could have been included in a different category. Although some
subcomponents spanned more than one category, they were positioned where they made the
most strategic sense. In addition, the categories and subcategories within the Digital Governance
Framework are also relevant and applicable to other related areas, such as testing and content
management. However, for the purposes of this white paper, we will only examine each category
from the perspective of digital analytics.
Figure 2. Digital governance category breakdown
How to create a data-driven dynasty Best Practices Guide 4
Leadership is critical—it provides the foundation for a successful digital analytics program. Leadership
consists of four subcomponents: sponsorship, buy-in, communication, and accountability.
First, you need to have effective executive sponsorship—someone who has sufficient influence
and authority within the organization to make things happen. The program will achieve limited
success without an executive sponsor who is both committed and involved. To be truly effective,
this internal champion should possess some level of self-interest in the program’s success or a
passion for creating a data-driven environment.
The four P’s of executive sponsorship
Prioritization—To be successful, digital analytics needs to be
aligned with key business goals. The
executive sponsor provides crucial
direction to the team, ensuring the
digital analytics program is always in
line with the corporate strategy and
top priorities.
Protection—The executive sponsor
plays an important role in protecting
the digital analytics team from other
conflicting initiatives or corporate
Problem solving—Using their clout
or influence within the organization, the executive sponsor steps in
to remove any problems that could
impede the success of the program,
such as resource or budget constraints.
Promotion—The executive sponsor
plays a key role in championing the
benefits of digital analytics, holding
people accountable, and promoting data-driven wins within the
organization, especially among other
Source: Dykes, Brent. 2011. Web Analytics
Action Hero. Adobe Press.
Executives who are involved but not committed might attend meetings—but because they do not
believe in the importance of analytics, no real progress will be made. Executives who are
committed but not involved might believe in the value of digital analytics, but they won’t be
effective because they don’t dedicate enough time to support it properly. An effective sponsor will
ensure the digital analytics program stays in line with the corporate strategy and top priorities,
protecting it from conflicting initiatives or internal politics and helping address any limiting factors,
such as resource or budget constraints.
Management buy-in
Second, you need to have management buy-in across your leadership team to create a data-driven
organization. It can’t just be left up to the executive sponsor—you need multiple change agents to
drive adoption. One senior executive in a large corporation—even the CEO—cannot drive
adoption alone. The responsibility for creating a data-driven dynasty needs to be shared by the
entire leadership team. With different departments and teams owning various parts of digital
marketing initiatives, it is critical that all groups share a common strategy to foster a data-driven
environment. In some cases, lack of support from one team can undermine the efforts of other
groups who are dependent upon their collaboration. The executive sponsor can play a key role in
winning over the executive team by sharing examples that demonstrate the value of digital
analytics. In addition, many analytics teams have launched internal roadshows to raise awareness
and win support from executives and their direct reports.
Leadership needs to play a key communication role. Effective communication from management
can accelerate user adoption of data-driven practices. If your management team wants the
organization to become more data driven, it’s important to let the employees know it’s a priority.
Typically, what’s important to your boss is important to you. By sharing a data-driven vision and
repeatedly reinforcing this message (for example, sharing examples of data-driven successes),
management can help everyone throughout the organization get on board.
Finally, management plays a key role in holding people accountable—employees, teams, partners,
and most importantly themselves. Without accountability within an organization, the data
becomes “nice to know” instead of “need to know.” When no one is held accountable for online
performance, nothing will change because there is no urgency to operate differently. Organizations
that invest in analytics and optimization only to maintain the status quo are funding an expensive
business proposition. Managers need to change the perception that accountability is about
discipline and punishment, and instead associate it with learning and improvement. Leading by
example is essential—leaving little room for anything but data-driven decision making. Although
senior executives have the discretion to rely on their intuition when making key business decisions,
purposefully using and trusting the data can have a positive effect that spreads throughout the
entire company.
How to create a data-driven dynasty Best Practices Guide 5
Management guru Peter Drucker once said, “What gets measured, gets managed.” Understanding
the business strategy is critical to effective digital measurement and ensuring the right things will
be optimized. Strategy is divided into three main areas: focus, alignment, and innovation.
Focus emphasizes the organization’s understanding of key business goals and strategic initiatives
to achieve those objectives. It’s critical to understand how these goals are prioritized as well as
their scope (for example, only this country, only these brands, or only these websites) and timing
for completion. In addition, focus also includes defining the key performance indicators (KPIs), such
as online revenue or order conversion rate, as well as the associated targets for those metrics (for
example, increase online revenue by 20%). When referring to strategy as a key factor, it’s not about
analyzing the actual business or digital strategy—but how clearly that strategy is communicated,
understood, and agreed upon throughout the organization. If an organization’s strategy is
understood and can be measured, the effectiveness of the digital strategy will be clear.
One of the biggest challenges is ensuring alignment between your company’s current strategy and
the deployment of your analytics solutions. Organizations are rarely static—leadership changes,
business strategy evolves, websites are redesigned, new products or services are introduced, new
marketing campaigns are launched, new marketing channels appear, new partnerships are formed,
competitive landscapes shift, and so on. Without proper alignment between your implementation
and the current digital strategy, the reporting and analysis may be irrelevant or less useful to the
business. Your measurement strategy needs to be dynamic and adjust as changes occur within your
business. Having a representative from the digital analytics team on a digital marketing steering
committee can ensure that the team stays on top of what’s happening within the business and any
potential shifts in priorities.
If your company has covered the first two foundational areas, it can then focus on innovation. From a
strategic perspective, if your organization is successfully collecting the right data on a consistent basis,
you are in a position to innovate, turn your data into a competitive advantage, and take your business
to the next level. Your organization will be able to explore new applications and new ways to extract
even greater value from your digital data. Your company may even be able to transform your data
into unanticipated revenue streams via new products or value-added services for your customers or
partner networks. The opportunities are limitless once the foundational pieces are in place.
Despite ongoing advancements in analytics technology, human beings will continue to play a
central role in the success of digital analytics. The people category consists of four subcomponents:
resources, expertise, structure, and community.
One of the main aspects of any analytics or optimization program is resources. You’ll need to
consider many factors to decide how to staff your analytics team. Based on the number of internal
customers across your business teams and the overall complexity of your business, you’ll need to
determine the right amount of analytics professionals to meet the data-driven needs of your
organization. The different roles and responsibilities need to be defined in terms of business
reporting, deep-dive analysis, technical deployment, and project management.
You’ll also need to decide the appropriate mix and allocation of internal staff and external
consultants, which may depend on your organization’s maturity level with digital analytics (less
mature organizations may require more consultants), as well as staffing constraints (it may be
easier to hire a contractor than get more headcount). Finally, your organization will need to
determine how to best hire and retain this unique breed of talent. Although you can always recruit
seasoned analytics talent from other companies, more and more organizations are finding success
in grooming college graduates into future analytics experts.
How to create a data-driven dynasty Best Practices Guide 6
Expertise relates to the types of analytics skills and knowledge that are required by your
organization’s analytics staff, business users, and senior executives. Each group will have related but
vastly different needs. For example, your analytics power users will need extensive training on your
analytics solution to take full advantage of its capabilities. Business users need role-specific training
on how to quickly access the key day-to-day information and reports they need for their position.
Executives won’t necessarily need or want extensive product training, but instead need training on
how to interpret metrics and reports so they can make informed business decisions. Your
organization will need different training approaches for onboarding new staff as well as helping your
current employees to develop their expertise over time. Other considerations include how much
emphasis will be placed on cross-training, whether your firm will leverage internal and external
training programs, and how those courses will be administered—web based or instructor led.
Structure is a major people-related consideration in the Digital Governance Framework. Digital
analytics teams are typically organized in one of three ways: decentralized, centralized, or hybrid
(hub-and-spoke), as shown in the following figure. Today, most organizations rely on a centralized
model where all of the analytics resources report into a single corporate team (colocated or
distributed), or a hybrid approach where a core analytics team manages the overall analytics
program and collaborates with distributed analysts within each business unit. The hybrid model
combines the advantages of the centralized approach (standardization, shared training and best
practices) with those of the decentralized approach (provides more flexibility within business units
and allows analysts to be closer to the business).
Figure 3. Analytics team structures
The ownership of the analytics team also continues to be a topic of debate (sometimes heated)
among organizations. When web analytics first emerged as a technology, the group was part of the
IT function; however, over the past five years more analytics teams have shifted away from IT to
the business side, such as marketing or e-commerce. The optimal structure for your analytics team
will depend on your company’s digital strategy, its unique organizational structure, and the
maturity level of your company.
Although overlooked, community is another key consideration. The well-known saying “it takes a
village to raise a child” is appropriate for digital analytics programs as well. You want to encourage
the creation of an analytics community within your company where members can learn from each
other and share experiences, ideas, and best practices. When you have distributed analysts and
business users across different business units and countries, the digital analytics community
provides valuable support to new users as well as opportunities for more advanced users to share
their collective wisdom. Community can be fostered in a number of different ways, such as a simple
email distribution list, internal wiki, corporate chat groups, scheduled monthly calls, and workshops.
How to create a data-driven dynasty Best Practices Guide 7
If your company wants to be successful with its analytics and optimization efforts, it will need to
establish and streamline its analytics-related processes and workflows. There are four main types
of processes: deployment, usage, sustainability, and change management.
•Change Management
Deployment covers the various processes related to implementing tags and configuring your
analytics solution in an efficient and effective manner. At a basic level, analytics tagging should be
built into the current web development process—and not be a recurring afterthought. Organizations
should have a formal process for gathering business requirements for new projects, and a robust
quality assurance process for tagging before it goes live. Without well-defined processes in the
deployment phase, key data needed by the business can be left out due to incomplete requirement
gathering, introducing unnecessary risks and delays due to last-minute code fixes. It can also erode
confidence in the data if it’s frequently implemented incorrectly. Larger corporations with several
analytics deployments occurring concurrently need to involve project managers.
Figure 4. Sample deployment process for analytics projects
Once an analytics solution is in place, how your company uses the tool becomes important because
you’ll want to maximize your investment. Usage means you are establishing and leveraging best
practices for reporting and deep-dive analysis. For example, is your team monitoring how much time
and effort is being spent on reporting instead of deep-dive analysis? If your analysts are being
bombarded with reporting and analysis requests each week, do you have a system in place to help
prioritize those requests? If you’re familiar with digital analytics, you also know that there’s more
than one way to answer a particular business question or report on performance results. For routine
reports and business questions, it may be helpful to agree on the best approach to ensure numbers
match up properly regardless of who is building the report or performing the analysis.
Sustainability focuses on having the right infrastructure and procedures in place to support or
sustain your analytics efforts postdeployment. These processes ensure that your analytics and
optimization initiatives do not go off course in six to 12 months. Some examples of sustainability
practices include documenting each deployment for future reference, establishing and enforcing
corporate standards, creating a centralized knowledge base of metrics and reports, providing
ongoing “open office” hours for analysis and reporting questions, clearly defining escalation paths
for analytics issues, and scheduling periodic implementation reviews. By focusing on sustainable
analytics, you’re managing digital analytics like a program instead just a project or set of projects.
How to create a data-driven dynasty Best Practices Guide 8
Change management
Many organizations are looking to change their current culture to one that is more data driven. In order
for this to happen, leaders, employees, and partners may need to adjust existing attitudes and
behaviors. Change management is about managing the people side of change. A wide variety of change
management tactics can be employed in order to achieve this type of organizational transformation.
These tactics can include focusing on short-term wins to build internal momentum, evangelizing the
successes of the program throughout the company, and creating optimization checklists to turn
behaviors into habits. Like it or not, if you work in digital analytics you need to be a change agent.
Along with the previously mentioned factors within the Digital Governance Framework, technology
also plays an important role in creating a data-driven organization. The technology should act as an
enabler—empowering your organization to obtain data and act on it. Technology does not refer to
just your analytics tools, but to all of the technologies that form your digital marketing infrastructure
(content management systems, testing, targeting, internal search engines, social media, and so on).
When evaluating the different aspects of technology, there are five key considerations for digital
analytics: solution fit, enterprise class, integration, empowerment, and automation.
•Solution Fit
•Enterprise Class
Solution fit
First, you need to ensure there’s a good solution fit between your current business needs and digital
marketing technologies. Are you able to perform the necessary analysis and optimization based on
your current toolset? For example, if you need to analyze marketing campaigns across online and
offline channels, only certain analytics tools are capable of handling this kind of request. In another
example, if your company would like to optimize its online user experience but its legacy content
management templates are inflexible, the business won’t be able to act on any of the analysis findings.
Whenever there’s a poor solution fit between your business needs and the available technologies,
you’re essentially using the wrong tools for the job. When an athlete doesn’t have the right equipment
for a particular sport, the activity becomes more difficult and time-consuming. The same problem can
occur with digital analytics—technology can get in the way or it can enable success.
Enterprise class
If you work for a medium-to-large-size business, you’ll probably need enterprise-class analytics
and optimization tools. In other words, are your analytics tools scalable and reliable? Do you have
sufficient levels of support and professional services? Do you have adequate application program
interfaces (APIs) and user administration tools? Do you own your data? Although a homegrown
solution might have been sufficient when your company was smaller, a rapidly growing and
expanding company needs enterprise-class analytics and optimization tools.
Companies are frequently using various tools to measure and optimize different areas of their
digital marketing (for example, social media, email, targeting, and so on). Integration between
technologies is critical because companies can benefit from integrated data and workflows that
streamline processes, provide better insights, and enable greater agility to seize.
Achieving adequate staffing levels can be challenging—making empowerment even more crucial.
In order to have your analytics resources focused on the most strategic projects, you’ll need to
democratize the data and empower disparate business users to answer routine business questions
themselves. Analytics tools can enable business users to access data through online dashboards,
Excel-based scorecards, scheduled custom reports, and other ways.
Automation can offset resource bandwidth issues. Whenever a company can substitute technology
for people through automation, it means they can either reduce costs or reallocate resources to
more strategic areas. Digital analytics can provide automated alerts to notify analysts of key
problems requiring investigation, and refreshable Excel-based dashboards to simplify reporting
and free up analysts’ time to focus on more strategic analyses.
How to create a data-driven dynasty Best Practices Guide 9
Organizational dynamics
Although the rest of the factors focus on different levers that you can employ in your data-driven
journey, you can’t ignore the unique institutional factors that influence your company’s success with
digital analytics. Rather than overlooking these organizational dynamics, it’s better to acknowledge
them and determine how they might shape your data-driven plans. Externally, companies of a similar
size, corporate heritage, and industry may look similar on the surface. However, once you examine
their inner workings—their values, culture, and ways of getting things done—they can be vastly
different. As a result, a successful practice at one company may not be as effective at another. It’s not
about finding excuses for why something won’t work at your organization—it’s about tailoring your
data-driven approach to its unique environment so it will succeed.
Organization Dynamics
As previously mentioned, organizational dynamics aren’t necessarily negatives and can actually be
strengths that give your company an advantage in creating a data-driven organization. These
institutional factors can also shift and change over time—but more in a time frame of months and
years rather than days or weeks. There are three organizational dynamics factors: corporate
culture, corporate politics, and history/reputation.
Corporate culture
Each company has its own unique corporate culture that influences how the organization perceives
and uses data. Some organizational cultures may be resistant to adopting a more data-driven
approach because they are intuition driven or bureaucratic. Despite the fact that these companies
have invested in digital analytics, leaders and employees may be skeptical or fearful of the data.
Meanwhile, other firms that are more results driven or sales oriented may embrace data-driven
practices, welcoming additional business insights that can improve their performance and pinpoint
new opportunities. Culture can also be an issue when it comes to how digital or Internet savvy your
organization is. Although some companies have dived headfirst into the digital world—others have
barely dipped in their toes. Any resistance to digital analytics may actually be directed towards the
digital space in general.
Corporate politics
Another key organizational dynamic is the level of corporate politics at your company.
Organizational politics are a reality at most companies, and they can interfere with efforts to build
and sustain a data-driven organization. Politics can play havoc when departmental turf wars and
different personal agendas—control of resources, promotion of individual goals, or pursuit of
power and position—push digital analytics initiatives off course. Navigating politically-charged
organizations requires extra attention and patience.
Some companies have been successful with analytics in unrelated parts of their business (for
example, point of sale, supply chain management, and so on), and as a result, these firms are more
receptive to analytics in general. In addition, past successes or failures in web analytics and the
overall reputation of the program can have a lasting effect on building internal momentum. With
the right plan and some determination, all of these dynamics be turned to your favor—cultures can
be molded, politics can be mitigated, and history can be rewritten.
The path to a data-driven dynasty
On the journey towards becoming more data driven, companies move through different stages. If
we look at a graph that compares the level of investment (loosely defined as money, time, staff,
and management focus) and the amount of data-driven success (ROI) they achieve, most
organizations would anticipate that there is a perfect linear relationship between these two
variables—the more that is invested, the greater the return.
How to create a data-driven dynasty Best Practices Guide 10
Figure 5. Expected relationship between analytics investment and return
In the real world, most companies don’t follow the straight line up and to the right. Instead they run
into the “optimization speed bump.” The actual return from their investment in digital analytics
doesn’t result in the return they expected.
Figure 6. Optimization speed bump
The reason for this arc is that most companies do not take a structured and balanced approach
with their analytics and optimization efforts, and as a result these organizations find themselves in
an impaired state. As shown in the graph above, there’s a gap between the value that could have
been generated and what actually is created. These organizations have made a significant
investment in technology and may have assigned someone to manage the tool—but they haven’t
worried about establishing any processes, securing the necessary leadership support, or clarifying
the digital strategy.
Figure 7. Data-driven maturity requires balanced investments
How to create a data-driven dynasty Best Practices Guide 11
However, over time if they make strategic, balanced investments in their data-driven capabilities,
they will gradually move through the initiated and focused stages until they reach the highperforming optimized stage. As your organization matures in terms of its data-driven capabilities,
you’ll become more efficient at maximizing the value from your analytics and optimization
investments. From the following matrix, you can get a sense of what is occurring at each maturity
level based on the five principal factors within the Digital Governance Framework.
Figure 8. Data-driven maturity matrix
Frequently, problems are
­interconnected, and some ­challenges
may even be symptoms of more
deeply rooted issues.
Common pitfalls along the data-driven journey
Each company faces unique challenges along its data-driven journey. However, there are some
common pitfalls that prevent organizations from reaching their full data-driven potential. As you
evaluate your current maturity level with digital analytics and chart your course for success,
consider which of the following issues might be challenges at your company:
• No executive sponsor—The digital analytics program doesn’t have a champion, and without
sponsorship it is unable to build momentum internally.
• Unclear strategy—Without a clear, agreed upon digital strategy, the analytics team is unable to
align the data measurement efforts to the real needs of the business.
• No accountability for digital metrics—Individuals, teams, and partners are not held accountable
for the performance of digital marketing efforts. As a result, there’s no incentive to change or
improve current behaviors or approaches.
• Disconnected, outdated implementation—Data collection hasn’t kept pace with the changes
occurring within the business, and the analytics reports are no longer as relevant or meaningful.
• Poor deployment process—Too often analytics ends up being an afterthought each time the
company launches a new digital marketing initiative. As a result, the reports end up being
incorrect, incomplete, or less useful than they could have been.
• More emphasis on reporting than deep-dive analysis—The analytics team’s time is primarily spent
on maintaining the existing reports and responding to ad-hoc reporting requests. Almost no
emphasis is placed on advanced analysis, which can provide significantly more value to the business.
• Lack of analytics resources—Digital analytics teams are insufficiently staffed and are unable to
address more than just the basic responsibilities of implementation and reporting.
• Dysfunctional team structure—Analytics resources are located on the wrong team, siloed within
the organization, or misaligned with the needs and structure of the enterprise.
• Insufficient tool training—Employees receive little to no training on the analytics tools, and as a
result they are overly dependent on the analytics team to answer routine business questions.
• Siloed technologies—Different digital marketing point solutions aren’t integrated and don’t play
nicely together. Rather than focusing on improving the digital business, the digital analytics team
is constantly sidetracked by extraneous implementation roadblocks and data validation issues.
How to create a data-driven dynasty Best Practices Guide 12
Laying a solid foundation
Frequently, problems are i­nterconnected, and some ­challenges may even be symptoms of more
deeply rooted issues. Trying to solve symptoms instead of more serious underlying problems can
be a frustrating, wasteful exercise. By strategically addressing one key issue, an organization may
be able to ­alleviate challenges in other areas. Two common pitfalls are critical starting points:
securing an executive sponsor and clarifying your digital strategy.
In the Digital Governance Framework, all of the areas are dependent on leadership’s involvement
in nurturing a data-driven organization. Leadership is even the most effective tool in tackling
various organizational dynamics, such as a change-resistant culture or corrosive internal politics. If
your company can find a strong executive sponsor at the right level within the organization, that
individual can rectify other issues by spearheading efforts to clarify the corporate strategy,
introducing more transparency and accountability, securing more analytics staff, identifying other
managers who can act as change agents, and so on.
Clarifying the digital strategy is another critical step in your company’s data-driven journey, and
many other considerations hinge on having clear objectives and KPIs. Once you have proper
direction based on a clearly defined and agreed-upon strategy, it becomes easier to create a sense
of urgency, discern priorities, and focus your efforts. When your strategy lens is blurry, the quality
and relevance of your data may not come into question. Alternatively, your organization may
question why certain metrics matter or even how to properly interpret them. Only when your
strategy lens is sharpened and put into focus, do other challenges become higher priorities to fix. In
some cases, you might not be able to address either of these areas initially—however, they can’t be
ignored because before too long they will end up inhibiting your overall progression.
Finding an executive sponsor
If you don’t have a champion or executive sponsor for your digital analytics program, follow these steps.
1. Identify which departments or groups will benefit the most from digital analytics—typically
business units that have high investments in digital marketing initiatives.
2. Pick a team that is excited to work with you and learn more about their part of the business and
their unique challenges.
3. Target potential quick wins for this team from the digital data. A series of small successes begins
to demonstrate the value of the digital analytics data. You’ll attract attention from managers
who appreciate how the data can help them achieve their objectives and targets.
4. As you share your findings and recommendations, identify an executive within this group who
would be a good champion. Initially, you might not be able secure a senior executive. Start with a
mid-level manager who is passionate about the data. As you continue to deliver value by
generating wins for the organization, you’ll eventually get the attention of higher level executives.
Developing a digital strategy
You may think every company but yours has a digital strategy, but sadly many do not. Often
corporate websites aren’t owned by a single owner and represent a mixture of disparate, or worse,
competing interests and purposes. So much effort goes into maintaining and updating them that
nobody has taken the time to define what they’re trying to achieve online, frequently leading to
counterproductive results. Follow these steps to define your digital strategy.
1. Identify all of the key stakeholder groups for your company’s digital properties.
2. Gather key business objectives from each group separately.
3. Merge the goals into a set of four to five key objectives.
4. Based on your understanding of the corporate strategy, prioritize and rank the list of goals.
5. In a group meeting, review and refine the goals with key stakeholders. If needed, involve a
neutral third party to mediate potential disagreements.
6. Based on stakeholder feedback, finalize the business objectives and KPIs.
7. Share an overview of the agreed-upon digital strategy with key stakeholders.
How to create a data-driven dynasty Best Practices Guide 13
Case study: Skandinaviska Enskilda Bank
Åsa Iggström joined the Swedish bank, Skandinaviska Enskilda Bank (SEB), in 2008 after working
several years in different web management roles in the wine and spirits industry. Two years after
joining the financial institution, she was given the opportunity to manage its digital analytics
program. Reflecting on her organization’s journey, Åsa identified three key phases during her
company’s evolution in digital analytics, as shown below.
Figure 9. SEB’s data-driven journey (2008-2012)
Impaired to initiated (2008-2009)
The beginning of the journey was a little rough. Overall, the business had a low appreciation of the
business value that web analytics could provide, which was further complicated by the fact that
there was no clear ownership of the company’s web initiatives. Web analytics wasn’t viewed as a
business driver or opportunity, but mainly as a way to measure and maintain its online properties.
Although SEB had an advanced analytics platform (Adobe® SiteCatalyst® software), it only had one
person working 50% on web analytics and an antiquated content management system (CMS) that
created several implementation headaches. At that time, Åsa was working as an online business
developer, but she sought out training to strengthen her skills and sharpen her focus on web
analytics. She hired experienced consultants to help move her own digital analytics initiatives in
the right direction.
Initiated to focused (2010-2011)
SEB’s management team came to realize that web analytics played a strategic role within its group
marketing department. Åsa was asked to own the global web analytics function, and she secured
budget for technical and strategic support from an optimization agency. The part-time resource
within the business unit was replaced by a full-time dedicated analyst, who collaborated with Åsa
in expanding the digital analytics program.
With the support of her manager, Åsa trained two power users so that reporting responsibilities
could be delegated from her role. She worked with different internal stakeholders to establish the
overall goals and purpose of the site, which helped to ensure SEB’s implementation matched its
digital strategy. Despite ongoing issues with the company’s CMS, she was able to introduce some
workarounds and establish more consistent processes for campaign and paid search tracking.
However, Åsa ran into a setback when her role and function was shifted to a nonstrategic
department during a corporate reorganization. The change temporarily limited her ability to
influence the bank’s digital initiatives from an analytics perspective.
How to create a data-driven dynasty Best Practices Guide 14
Focused to optimized (2012 and beyond)
Åsa noticed a real shift in momentum at SEB when its leadership team spearheaded various
top-down change management efforts. Although another reorganization occurred at SEB, this time
the digital analytics role landed in a more strategic department responsible for digital governance.
The correct placement, along with strong executive sponsorship, was critical to steer different
groups and establish guidelines and best practices.
Åsa introduced a more robust data management process, where business owners are required to
complete a form that documents each project’s purpose and scope. Through this new process,
Åsa’s team now holds each business group more accountable to monitor and optimize its own
digital initiatives. Åsa has added five additional power users who are able to generate reports and
analysis for their respective groups. SEB is also in the process of replacing its problematic, outdated
CMS with a new, more flexible platform.
Certainly, there are no silver bullets for creating a data-driven organization. Åsa established a plan
early and methodically executed it. Through determination and hard work, the SEB team has been
able to transform and elevate its digital analytics program. If you have patience and focus on your
plan, you can follow a similar path as SEB.
“Some people want it to
happen. Some wish it
would happen. Others
make it happen.”
Michael Jordan, five-time NBA MVP
Building a roadmap for your data-driven dynasty
The first step to create your digital governance roadmap is to evaluate and understand your current
maturity level. Using the framework, you’ll want to determine the gaps that are impeding your
organization’s progress with digital analytics. Adobe has created a self-assessment survey, the
Adobe Digital Measurement Maturity Assessment, based on the Digital Governance Framework, that
asks a series of questions to help you determine your company’s current maturity level.
You may consider gathering other stakeholders’ opinions on what the key gaps are after sharing
the Digital Governance Framework with them. Don’t be surprised if they identify unexpected issues
that you might have overlooked. It’s better to get a realistic and comprehensive assessment before
building your roadmap. Remember to take a balanced approach and concentrate on the weak
areas across the different factors. If you would like help with building your roadmap as well as
executing on your plan, you may want to consider leveraging Adobe Professional Services.
When it comes to creating a data-driven organization, many companies want it to happen or wish it
would happen. In many cases, they invest in the right tools but fail to invest in other key areas. Just
as Michael Jordan highlighted, organizations need to make it happen by focusing on all the areas in
the Digital Governance Framework—leadership, strategy, people, process, technology, and
organizational dynamics. It’s the only way to create a data-driven dynasty that will turn your
customers into lifelong fans and raise your company’s play above that of its competitors in the
digital arena.
About the Author
Brent Dykes is the evangelist for customer analytics at Adobe and is responsible for guiding and
evangelizing the vision of Adobe’s analytics solutions. He has been focused on enterprise-level web
analytics consulting for eight years, working with industry leaders such as Microsoft, Sony, Dell,
Comcast, and Nike. Brent recently published his first book, Web Analytics Action Hero, which outlines
how to be a successful analyst and help drive action from digital data. Brent has been involved in
digital marketing for more than 10 years, including positions at Blast Radius (WPP), Lands’ End, and
Microsoft. Brent has a Bachelor of Business Administration (BBA) in marketing from Simon Fraser
University, and graduated from Brigham Young University’s Master of Business Administration
(MBA) program, where he was a Hawes Scholar. Follow Brent on Twitter @analyticshero.
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