Launching an Insights-Driven Transformation

Launching an Insights Driven Transformation January 2016

POINT OF VIEW

Launching an Insights Driven Transformation Overview

Copyright © 2016 Accenture All rights reserved. 2

POINT OF VIEWLaunching an Insights Driven TransformationLeaders across industries recognize that, in today’s competitive landscape, data-driven decisions are the key to developing a competitive advantage. We believe that the use of analytics to create a competitive advantage is about to become even more widespread. To help gain a greater understanding of these transformations, we studied what the most analytically mature companies are doing differently to understand the tangible value from analytics, and how their practices might be adopted by companies less far along the maturity curve.

RESEARCH STUDYAnalytics Operating Model Benchmarking Study As a basis for this point of view, Accenture conducted a benchmarking study based on detailed online interviews with more than 100 analytically mature companies around the world. Respondents held titles of director and higher and 75 percent were from C-suite roles. Companies included a range of industries and geographies and were selected on the basis of their analytical sophistication.

Key Themes and FindingsWe developed five primary themes that more mature companies are doing differently

They establish a Center of

Gravity for analytics that is often led by a Chief Data and Analytics

Officer and sets enterprise analytics

strategies and standards.

They work to raise the company’s analytics IQby implementing

training programs for analytics, functional and IT talent and

they work to embed desired behaviors

into business processes.

They deploy new capabilities

faster by implementing a “test and learn”

approach and then scaling capabilities

where the value has been proven.

They employ “agile

governance structures”

which are focused on driving speed to

value, measuring and optimizing value as well as driving innovation across

the enterprise.

They create inter-disciplinary, high performing

teams with talent effectively

organized into “pod” structures.

They employ creative ways to source,

develop, and retain talent.


4Copyright © 2016 Accenture All rights reserved.

Establishing a Center of Gravity for AnalyticsKEY POINTS

Chief Data & Analytics OfficersNearly 40 percent of the companies we surveyed said they are elevating

analytics by creating a Chief Data and Analytics officer

(CDAO) responsible for both the vision and the

implementation of the enterprise analytics

strategies.

Market Archetypes as an Organization

ConstructWe found that companies are investing considerable

time and resources in building operating models,

and many are thinking along non-traditional lines.

Cyclical Nature of Operating ModelsOne of the more interesting

findings in our study was that some companies plan

to distribute certain activities back out to the business

functions in the future once the standardized processes

are in place.

Establishing the Center

Companies with high maturity levels (Stage 5

companies) were more likely to have established a center

of gravity for analytics activities, often in the form of a Center of Excellence

(CoE) or similar concentration of talent

and resources.


Employing “agile governance” KEY POINTS

StructuredInnovation

Another element of agile governance is a structured

innovation process. Leaders may set up innovation or

“SWAT teams” with a mandate to focus on key business

questions for a concentrated period of time, often as

short as 30 days.

Build Horizontal Governance Structures

High performing analytics leaders are building thin, horizontal governance structures, focused on

outcomes and speed to value, rather than creating hierarchies that are slow

to adapt.

Value Creation Offices

Leaders are also instituting value creation “offices” to

spearhead outcome tracking against key metrics and to ensure that the value from

analytics is realized. ExecutiveScorecards

Perhaps the most critical part of establishing an agile governancestructure is ensuring that it usesthe key metrics that are most

important to the business. To this end, CDAOs are designing

executive scorecards to trackperformance against these

key metrics.


Creating inter-disciplinary, high performing teams KEY POINTS

Analytics Pod Structures

It remains difficult, if not impossible, to find all of the skills required

for analytics success within any one individual. Some companies, therefore, are establishing “pod” teams which have a mix of roles, including data scientist, analytics

modeler, visualization expert, data engineer, business

analyst and business domain expert.

Non-Traditional Career Paths

Analytics talent tends to follow non-traditional career

paths in response to the value that this talent tends

to place on becoming subject matter experts in

finding insights in large data sets, often instead of following traditional

managerial career paths.

Talent Sourcing

Leading analytics companies create mechanisms to source the best talent for their organization.

They may, for example, build their brands internally and

externally, marketing themselves as the destination for

top talent.

Talent Retention

Analytics talent is seen as being in short supply. Given that there does not appear to be a solution to the talent shortage

in the near future, it is important for analytics organizations to focus on talent retention by

developing reward and incentive programs that keep these individuals

engaged.


Deploying new capabilities faster

Hybrid Architectures & Agile Toolsets

Leading analytics organizations deploy new, agile technologies, as well as hybrid architectures and

specifically designed toolsets, to help achieve speed to capability and

desired outcomes.

Piloting with the Intent to Scale

One distinguishing characteristic of fast-moving companies is that they pilot with the intent to scale; that is, they establish the right mindset, processes and

accountability in advance, then move quickly to test,

learn, refine and implement.

New MindsetWe believe that scaling

priority capabilities requires new approaches and

mindsets for many organizations. These

organizations may need to “unlearn” what has already been learned; for example, they may need to bring the

data to the analytics, rather than the other

way around.

Prioritizing and Scaling Capabilities

By prioritizing the scaling of capabilities, companies

stated that they were able to optimize analytics

investments, rationalize vendors and suppliers,

improve talent acquisition, and rationalize data and

tools.

KEY POINTS


Raising the company’s analytics IQ

Analytics Academies and

Talent DevelopmentHigh performers implement an

Analytics Academy that provides analytics training for functional business resources in addition

to the core management training programs.

IntelligentEnterprise

High performers are training resources to use new tools and techniques to improve decision-

making throughout the enterprise and they are also implementing

innovative technologies such as advanced data visualization

to communicate the value of analytics.

Immersive Environments

High performers are moving away from traditional presentation tools

and are leveraging more interactive tools to improve

collaboration.

Holistic Interactive Learning Approach

This holistic, interactive learning approach where business, analytics,

and IT resources are equipped with new immersive tools, techniques,

and formal training opportunities allows companies to activate

leaders by focusing time, funding and attention on desired

behaviors and skills.

KEY POINTS

Copyright © 2016 Accenture All rights reserved.

Recommendations

9

Be Thoughtful on ApproachStart by aligning on the North Star for analytics by determining where your company is in its journey and what it needs to do to develop sustainable capabilities to reach its goals. This is often driven by an understanding of the organization’s existing talent, tools, and investments so that gaps, redundancies and opportunities can be identified. Knowing the current landscape will also help to prioritize investments in capabilities and will serve as an input to the analytics operating model, governance structure and ways of working between the functions. It is also important to identify a Chief Data and Analytics Officer or equivalent individual with the authority and accountability to initiate and manage the journey.

1Be Agile and NimbleExperiment with team structures and other approaches to ensure that talent from all required disciplines is identified, organized effectively, and retained. Companies should explore non-traditional ways of identifying “diamonds in the rough” or untapped analytical talent such as innovative marketing or partnerships with academia. Once the talent has been hired, analytics pods often serve as a means to effectively deploy analytics resources. Further, look at governance as a way to quickly empower and drive programs, rather than as a control function that only slows things down. Lastly, identify the top priority capabilities to invest in, relentlessly concentrate on proving the value in days and weeks (not months), and identify a path to scale once the value has been proven.

2Change the MindsetMake a candid assessment of the company’s analytics IQ and undertake programs, such as implementing an Analytics Academy, to raise the business acumen for analytics resources and the analytics acumen of business resources. This also includes implementing interactive learning plans and innovative technologies such as advanced visualization, immersive environments and collaboration tools.

3


Contact Information

Bob BerkeyManaging Director, Accenture AnalyticsAnalytics Strategy & Transformation Global Offering [email protected]

Chad VaskeManager, Accenture AnalyticsAnalytics Advisory [email protected]

Brian McCarthyManaging Director, Accenture AnalyticsAnalytics Advisory Global [email protected]

10

FOR MORE INFORMATION

mailto:[email protected]




Launching an Insights Driven TransformationBuilding and Sustaining Analytics Capabilities Across the Enterprise

(For more information contact [email protected]

POINT OF VIEWLaunching an Insights Driven TransformationLeaders across industries recognize that, in today’s competitive landscape, data-driven decisions are the key to developing a competitive advantage. We believe that the use of analytics to create a competitive advantage is about to become even more widespread. To help gain a greater understanding of these transformations, we studied what the most analytically mature companies are doing differently to understand the tangible value from analytics, and how their practices might be adopted by companies less far along the maturity curve.

We developed the following five primary themes that more mature companies are doing differently:

(1) They establish a Center of Gravity for analytics that is often led by a Chief Data and Analytics Officer and sets enterprise analytics strategies and standards.

(2) They employ “agile governance structures” which are focused on driving speed to value, measuring and optimizing value as well as driving innovation across the enterprise.

(3) They create inter-disciplinary, high performing analytics teams with talent effectively organized into “pod” structures. They employ creative ways to source, develop, and retain analytics talent.

(4) They deploy new capabilities faster by implementing a “test and learn” approach and then scaling capabilities where the value has been proven

(5) They work to raise the company’s analytics IQ by implementing training programs for analytics, functional and IT talent and they work to embed desired behaviors into business processes

RESEARCH STUDYAnalytics Operating Model Benchmarking Study As a basis for this point of view, Accenture conducted a benchmarking study based on detailed online interviews with more than 100 analytically mature companies around the world. Respondents held titles of director and higher and 75 percent were from C-suite roles. Companies included a range of industries and geographies and were selected on the basis of their analytical sophistication.

11



Research Study Overview: Analytics Operating ModelAccenture conducted a study with executives at 103 companies around the world to better understand how organizations are structuring analytics-driven organizations around these key operating themes

Research ObjectivesUnderstanding the operating model of analytically mature companies, in terms of:• Organization Strategy • Organization Design & Talent • Governance • Change Journey & Adoption

Research Overview• Fielded in March 2015• Global, cross-industry• 103 respondents• Greater than $5B in annual revenue• Respondent profile: most senior analytics person in the

company (75% from C-suite)• Company profile: analytically mature companies with a

cross-functional analytics organization. Maturity levels:– Stage 3: Coordinated Analytics Integration (22%)– Stage 4: Enterprise-wide analytics (44%)– Stage 5: Analytics as a Competitive Advantage

(34%)

12

Launching an Insights-Driven Transformation

Technology

Transcript of Launching an Insights-Driven Transformation