Data and analytics has the potential to be a disruptor and impact shareholder value. The data and analytics operating model drives the difference between leaders and laggards.

1. Data and Analytics:
Creating or destroying
shareholder value?
The data and analytics operating model drives
the difference between leaders and laggards.
Unlock data
possibilities

2. Data and Analytics | 2
A Silicon Valley company that uses
advanced modeling techniques to
predict product yields on a real-time
basis gets acquired by a well-established
company for hundreds of millions
of dollars. An established mutual
insurance company acquires an online
financial planning company to realize
the value of “robo-advice”. While the
acquisition cost has not been disclosed,
the valuation of the company was
hundreds of millions of dollars. Each of
these examples illustrates the value
companies are placing on data and
analytics capabilities with the ability
to radically alter or disrupt their
existing business model or create a
new one. Yet it also begs the question,
what prevented them from building
the data and analytics capabilities
themselves—capabilities that ostensibly
every organization possesses in its data
technologies and business analysts?
With the right mission, focus, (data
and analytics) operating model, and
investment in building capabilities,
companies can develop similar or
better capabilities than start-ups.
While the established companies are
making selective acquisitions, venture
capital firms are investing heavily in data
and analytics companies. CB Insights1
reported that VC firms invested over
$309 million in Artificial Intelligence
(AI) startups in 2014 alone—a 304%
jump from the previous year. While
AI is a narrow and specialized area of
investment, the amount being invested
in data and analytics companies in
general is a multiple of this number
as any quick review of the ‘big data’ or
‘analytics’ vendor landscape reveals.
What is the expected return based
on? In many cases, the companies are
extremely adroit at using data and
analytics techniques in new business
models that are disrupting well-
established industries and service
domains. Research firms are altering
the traditional survey approach by
crowd-sourcing data collection to create
the biggest, most diverse “data lakes” in
the world. Financial advisors are using
machine learning techniques in the
hopes of providing better investment
advice. Internet of Things companies
are providing a panoply of sensors and
monitoring systems to collect new data
with applications including predicting
maintenance, improving safety, and
reducing traffic congestion. In all cases,
these companies are creatively using
‘large volumes of new and existing data
and advanced analytic techniques to
increase the speed and sophistication
at which their industry operates to
“The reason why it is so difficult for existing firms
to capitalize on disruptive innovations is that their
processes and their business model that make them
good at the existing business actually make them
bad at competing for the disruption.”
—Clayton Christensen , Author of The Innovator’s Solution and The Innovator’s Dilemma
$
$$
Buy for
$1 billion versus
invest $100 million
for the same outcome:
Which would you choose?
1 Artificial Intelligence Startups see 302% funding jump in 2014, CB Insights, https://www.cbinsights.com/blog/artificial-
intelligence-venture-capital-2014/

3. Data and Analytics | 3
redistribute and expand profit pools
to their advantage—a concept we call
increasing the speed to value.
Given the potential for data and analytics
to be a disruptor and impact shareholder
value positively or negatively, Boards
of Fortune 500 companies are asking
the C-Suite how they are developing
data and analytics capabilities to grow
revenue, increase profits or reduce risk by
improving decision making or monetizing
data assets in new products and services.
Some companies are transforming
their organizations with new data and
analytics operating models that help
them compete with emerging players;
others are cautious, believing they need
to take a crawl, walk, run approach.
In this paper, we explore how the
operating model choices leaders make
will determine whether shareholder value
is created or destroyed as a result of being
a leader, fast follower or laggard in the
data and analytics game. Case studies are
used to illustrate ways to approach it and
why companies contemplating a crawl,
walk, run approach may be creating a
false choice that doesn’t exist—one
they may not be able to afford.
More data, better analysis, more intuitive
visuals and transparency of data alone
don’t create shareholder value. But
increasing the speed and sophistication
of the resulting decisions and actions
that can be taken to improve outcomes
does. Across industries, there are a few
major themes companies are exploiting
with data and analytics to drive
shareholder value.
• Aggregating and analyzing massive
amounts of customer data to enable
real-time hyper-targeted offers (e.g.,
Amazon, Best Buy)
• Creating massive unstructured and
structured data sets to deliver new,
disruptive products and services in
value chains or ecosystems (e.g.,
Netflix, Premise Data, Google)
• Targeting the ultimate “decision
maker” in the value chain with
more timely and better advice
(e.g., Monsanto/Climate Corp,
Betterment, MD Anderson)
• Creating markets that didn’t exist
using real-time data exchange to
better match buyers and sellers (e.g.,
Uber, AirBnB, Facebook, Kaggle)
• Shifting from reacting to risks to
predicting and preventing risks
(e.g., MetLife, Apache, Delta)
• Monitoring and simulating operations
to improve performance (e.g., VF Corp,
Citi, Sydney Trains, Coca-Cola)
These companies have data and analytics
organizations that are linked to creating
new value. Yet, many companies are stuck
with divisions that provide outdated,
low value-added business intelligence
services that suffer from a lack of clarity
on objectives, reporting relationships
and incentives. In addition, while there
is the ability to collect and analyze more
data, there is also more regulation, and
privacy concerns that control how it’s used.
Digital Rights Management is emerging
as a rigorous discipline to architect the
technical and legal framework for personal
data access, ownership, and usage.
These companies are in need of a clear
strategy, adequate and skilled talent,
agile data and technology architecture
and methodology to capture the benefits.
Based on the type and amount of value
they can drive, their reporting relationship
in the organization, the services they
provide, and their performance metrics we
have defined four, not necessarily mutually
exclusive, archetypes— The Information
Enabler Model, The Functional Model,
The Performance Optimizer Model,
The New Value Creator Model—that
companies should consider.
The data and analytics operating model you choose determines your ability to capture
new shareholder value.

4. Data and Analytics | 4
01The Information Enabler Model
Information Services case study
A large financial services organization
with four business units in life
insurance, annuities, retirement
services, and investments runs a
centralized IT organization that
handles the data and technology
needs of all the business units. The
traditional responsibility of this group
has been to gather, cleanse, store,
and provide data to the different
groups by building data warehouses
and data marts. The business units
were responsible for generating the
insights from the data and using
them in their business decisions. As
a result the advantages of sharing
common analytic techniques,
leveraging a standard set of analytic
tools, generating insights and acting
on them across the different business
units were not available to them.
In this model, the team typically reports
to the CIO and provides data delivery,
reporting and business intelligence
services to different functions and levels
of the organization—ranging from
executives to those leading day-to-day
operations. The services help provide
a basic understanding of the business
performance by providing operational
and/or financial metrics. The primary
way these groups are measured is
based on service delivery efficiency
and effectiveness and reducing the cost
of running data environments for an
equivalent unit of output. Investment
is focused on infrastructure and tools.
There is limited focus and investment
dollars on innovation, exploration
and experimentation with third party
data sources or applying new analytic
modeling techniques. Skills are
generally focused on data technologies
including those used to source, store,
manage and report the information,
as opposed to developing deep analytic
modeling skills.
Group
Business
unit
Business
unit
CIO
Functional
unit
Functional
unit
Functional
unit
Functional
unit
Analytics
group
Description
• Analytics group reports to the CIO at the Group Level or to the CIO at the
Business Unit level in a large global organization
• Focused on data delivery, data management, reporting and business
intelligence functions
• Business analytics needs to take place at the business unit level or
functional level within a business unit
• Success based on service delivery efficiencies and cost reduction
• Skills focused on data technologies—data storage, data management,
data security, etc.

5. Data and Analytics | 5
02Functional Model
In this model, there may be several
groups reporting to functional
leaders (e.g., Marketing, Sales, etc.)
that build targeted data marts and
analytic models to improve functional
performance. The services help provide
in-depth understanding of functional
performance drivers and associated
decisions, ranging from using the
right marketing mix to improving
sales territory coverage to automating
customer service. These groups may
rely on the services provided in the
Information Enabler Model, although
they are just as likely to use specialists
to access their own data and build
function specific data marts.
Investments are focused on procuring
third party data and exploring new
analytic techniques and tools to improve
functional decision making. Skills of the
group may include deep quantitative
expertise, as well as business analysts.
The primary way these groups are
measured is based on function
objectives.
Claims Data Science
Office case study
The claims department within a
personal lines insurance company
had recently completed a large,
multi-year claims transformation
effort. As they started collecting and
organizing new data, they wanted
to ensure that the different groups
within Claims were able to access
and exploit the data. They created a
Claims Data Scientist role to exploit
data from their new systems as well as
external information, to offer better
insights to their business groups.
The insights generated improved the
efficiency of the claims process and
claims satisfaction among customers.
Although the insights could have been
more broadly applied to marketing
and product design, the mandate
of the group and the integration
of the data were restricted to the
claims function.
Group
Business
unit
Business
unit
Cross
business
unit
Functional
unit
Functional
unit
Functional
unit
Functional
unit
Description
• Multiple analytics group at the functional unit level within some or all business
units reporting to the functional head
• Focused on functional data delivery, management, reporting and business
intelligence functions
• Business analytics focused at improving and enhancing functional decision making
• Success based on improving functional metrics (depends on functional
unit—e.g., marketing—conversion ratio)
• Skills focused on combination of functional and analytics expertise
Analytics
Group
Analytics
Group
Analytics
Group
Analytics
Group
- IT
- Finance
- Operations
- Strategy

6. Data and Analytics | 6
03The Performance Optimizer Model
In this model, the group reports to a
cross-functional business role (e.g.,
CFO, COO) to deliver cross-functional
analysis to support strategic, financial
and operational decisions that span
multiple functions. Analysis focuses on
broad root cause analysis to determine
drivers of business performance as
determined by overall revenue and cost
metrics, as well as scenario analysis
and forecasting to evaluate options and
predict performance. Investments are
made in innovation, third party data sets
and tools, as well as proprietary analytic
models. Skills include data scientists and
deep quantitative expertise across many
disciplines including econometrics,
operations research, and simulation
modeling. The groups are evaluated
based on their ability to identify root
cause issues, to support executive
decision making, and insights offered to
target specific improvements in financial
and operating metrics.
Health Services Business
case study
A health services company facing
uncertainty from the Affordable
Care Act and needing to expand into
new markets with new products
and services was rethinking how
to manage the performance of the
business. They created a role for a
Chief Analytics Officer reporting
to the COO. The CAO worked
with the executive team to rebuild
its management reporting by
incorporating performance targets
and variance thresholds and, using
small analytics SWAT teams to
perform root cause analysis, to
identify options of actions to take,
and to quantify the associated impact
for the leadership team to inform
their decision making.
Group
Business
unit
Business
unit
Cross
business
unit
CSO or COO
or CFO
CSO or COO
or CFO
Functional
unit
Functional
unit
Description
• Cross-functional analytics group reporting to a cross-functional executive—
CSO, COO, CFO in one or more business units
• Business analytics focused at improving and enhancing business unit decision
making across all functions
• Success based on improving business unit metrics; e.g., sales, profitability of
business unit
• Skills focused on combination of business knowledge and advanced analytics—
including forecasting and scenario analysis
Analytics
group
Analytics
group
Functional
unit
Functional
unit
- IT
- Finance
- Operations
- Strategy
Analytics
group

7. Data and Analytics | 7
04The New Value Creator Model
In this model, the group reports to
business unit or PL owners and creates
value by embedding data and analytics-
driven offerings into new or existing
products and services, explores new and
third party data sources and models, and
nimbly packages concepts into products
and services through prototypes
and pilots. Investments are made in
innovation, third party data sets and
tools, as well as proprietary analytics
models. Skills include data scientists
and deep quantitative expertise
across many disciplines including
econometrics, operations research,
and simulation modeling. The group
is evaluated based on their contribution
to revenue, profit and shareholder
value growth.
New Agricultural Services
Business case study
A data and analytics company in
the agribusiness sector completely
transformed the sector by taking an
outcome based approach to precision
agriculture. While many companies
focused on different elements of
improving efficiency of agriculture
(e.g., making better seeds, improving
irrigation, etc.) this company focused
on becoming the decision support
engine for the farmer. They focused
on predicting the range of crop yield
for a specific field and specific crop
in real-time. By focusing on the
eventual outcome of crop yield they
built a very sophisticated real-time
yield management prediction model
to monetize all of the data, insights,
decisions, and actions.
Group
Business
unit
Business
unit
Analytics
office
Analytics
group
Analytics
group
Functional
unit
Functional
unit
Description
• Analytics group reports directly to the Business Unit head and optionally to
a Chief Analytics or Data Science officer
• Business analytics focused at improving and enhancing business unit decision
making as well as innovative applications
• Success based on improving business unit metrics and causing/responding
to disruptions
• Skills focused on combination of business knowledge, advanced analytics
and innovation
Functional
unit
Functional
unit

8. Data and Analytics | 8
01
Discovery
“Observations to information”
Find value in internal and external,
structured and unstructured data.
Automate data discovery, data cleansing,
and analysis as much as possible
04
Outcomes
“Decisions actions to outcomes”
Unlock value by transforming business
function, business unit or industry
Recruit and train talent to deliver improved
financial, market and risk metrics
02
Insights
“Information to insights”
Apply new techniques on existing and new
data to generate insights
Create a test-and-learn environment for
continuously harnessing the insights
03
Actions
“Insights to decisions actions”
Link insights with decisions and actions to
deliver quick wins
Compete with faster and more sophisticated
decisions and actions
Keys to Success
Time to value from
data and analytics
As companies contemplate the right operating model for their organization, they
need to consider a new operating style that helps them compete with companies
that are disrupting their industries by discovering and capturing value in small
and big data sets quickly. Companies are building better predictive or prescriptive
models that are the foundation of providing better insights to leaders, day-to-day
operations, and customers. In many cases they are creating new business models,
and changing the economics of their business and shifting profit pools. For example,
Netflix employed new data discovery and analytics techniques to offer better
personalized recommendations to its customers, driving its less sophisticated
competitors out of the market. They have used their understanding of customer
preferences to move up the value chain into creating entertainment content—
a feat that would not have been possible without the business strategy and model,
and analytics strategy and operating model. To help companies adopt this operating
style, we have defined a framework we call Discovery, Insights, Actions, Outcomes
(DIAO). For each step, there are several new capabilities required and design
considerations to help companies become leaders in data and analytics.
To help companies
generate greater value
with their chosen
Analytics Operating
Model we have
defined a framework
we call Discovery,
Insights, Actions,
Outcomes (DIAO).
Keys to success: Time to value from data and analytics

9. Data and Analytics | 9
Discovery
Data and analytics
trends require a
‘DIAO’ approach
to deliver greater
shareholder value
Discovery converts observations into useable information. PwC’s Global Data Analytics
Survey: Big Decisions™ illustrates how executives are conflicted in this area. On the one
hand, the sentiment expressed by a large global real estate developer is common: “We
need to increase the amount of data that we collect, we need to discover the potential
value of that data, and we need to use it as a reference for big decisions.” On the other
hand, the sentiment expressed by a global consumer electronics company is just as
common: “If big data is consumed inappropriately, generated randomly, or kept for no
reason, it will create a lot of virtual garbage”. Having a structured approach to Discovery
is becoming an increasingly important step to unlock the possibilities of data as the
variety, volume, veracity and velocity increases—without creating virtual garbage.
First, it requires establishing the scope of value that the company wants to
inform by exploring new types of data to gain new business insights. The scope
of value dictates the type and amount of data that should be sourced, procured
and maintained—it is critical to avoid collecting a huge amount of potentially ‘useless
data’. The scope of value also has to take into account country-specific regulations
on data usage, privacy and security. These often differ by region and country and
are also cultural and generational.
Effective discovery requires creating hybrid data models and data management
capabilities that provide guidance on how to fit together hundreds of data sets, with
consideration for types of business uses supported, and data quality, compliance/privacy,
ownership and distribution guidelines. Traditional data models and processes that rely
on manually codifying structured data relationships and meta-data need to be expanded
to incorporate automated classification of unstructured data and its meta-data.

10. Data and Analytics | 10
When data was scarce, manual look-ups to extract observations from data sufficed.
Cost effectively discovering value in terabytes of internal and external data from
numerous sources that include social media, photos, videos, transaction data and
sensor data requires building data lakes and automating data environment scans
to find interesting observations.
In many organizations, the ‘discovery’ step is not explicitly managed. In the ‘information
enabler model’ the discovery aspect is implicit and is based on the data that is captured
by the transaction systems and is resident in databases and datamarts. As one moves into
the functional and performance optimizer models there is a more explicit intent towards
data discovery. The data discovery is restricted to a functional area or spans multiple
business units based on the respective models. In the case of ‘New Value Creator Model’
the ‘discovery’ step becomes a critical and differentiated step of the operating model.
Overview Use cases Solutions
Document
store
Stores data in a “Document” format that relies
on XML, YAML, JSON, and BSON, as well as
binary formats (e.g., PDF, MS Office) as an
encoding mechanism and does not provide
the burden of a predefined schema
- Store complex structures without the
need to pre-define a data model
- Data sources and structure are diverse
or they evolve their information and
data structures
- MongoDB
- CouchDB
- TerraStore
- ElasticSearch
Key—value
store
Allow an application to store its data in
a schema-less way. The data could be stored
as a string or a programming language or an
object, eliminating the need for a fixed data
mode and allowing for easy distribution
- Applications where objects must be
persisted
- Schema-less data storage mechanism
is needed
- Scalable, key based search for large
data sets
- Berkley DB
- Redis
- Amazon Simple—
DB
- Dynomite
- HamsterDB
Graph
store
Designed for data with relationships
that are better represented as a graph
or network of interconnected entities
and measured by the number of relationships
- Data sets can be represented and explored
as a network or graph
- Analysis is conducted of paths and
connections among data set entities
- AllegroGraph
- Bigdata
- Neo4J
- OpenLink Virtuoso
- InfiniteGraph
Columnar
store
Takes advantage of a column based physical
storage representation instead of the row
based representation that is common in
traditional databases. This difference can
improve the efficiency of storage and analysis
that involve aggregation
- Large data sets where aggregate metrics
are generated for large data sets
- HBase
- Big Table
- Cassandra
- Hypertable
A structured approach
to Discovery unlocks the
possibilities of data as the
variety, volume, veracity and
velocity increases—without
creating virtual garbage.
Techniques to create value in discovery

11. Data and Analytics | 11
The increase in available data has expanded the number of analytic techniques and
tools that can be applied to improve insights. In a recent survey conducted by MIT
Sloan Management Review, it was reported that ‘turning analytic insights into business
actions’ featured as one of the top three challenges. A first step is to categorize
the types of analytic techniques and tools that can be applied based on their
application (e.g., forecasting, customer life time value analysis, etc.), purpose (i.e.,
descriptive, diagnostic, predictive, and prescriptive), disciplines including statistical
and econometric techniques (e.g., regression analysis, clustering, time-series analysis,
etc.) and computational and complexity science techniques (e.g., deep learning or
neural network techniques, topological and graph analysis, genetic algorithms,
natural language processing etc.). This approach helps apply the right techniques
for the situation based on value, effort, accuracy and quality trade-offs. Using
multiple techniques frequently leads to novel insights and better accuracy.
Based on the multitude of analytic techniques and tools, an analytic model
architecture is needed to manage the complexity. Traditionally, analytics has
been viewed as a ‘batch processing’ activity with different data-marts or data
warehouses feeding an analytics layer that generates business intelligence. This
architectural view is more suited for ‘backward looking’ descriptive and diagnostic
analytics. With the continuous availability of huge volumes of real-time data,
the analytic model architecture should account for rapid testing and updates to
predictive and prescriptive models based on new insights.
Finally, a sandbox environment to experiment with insight generation fosters a
test-and-learn culture within the organization. The environment should facilitate
Insights
For better insights, analytic
techniques and tools go beyond
backward looking descriptive
and diagnostic analytics to
forward-looking predictive
and prescriptive models.

12. Data and Analytics | 12
both explorations of multiple types of data in data lakes as well as implementing
multiple analytic techniques and tools to determine their efficacy. From a more
practical perspective, such environments are being provided in public or private
clouds on an ‘on-demand’ basis, allowing firms to cost-effectively experiment with
and deploy analytic solutions.
As one moves from the ‘information enabler’ model towards the ‘new value creator’
model the ‘insights’ step gets progressively more sophisticated and faster. The
information enabler model is primarily focused on reporting and ‘backward-looking’
descriptive analytics. The functional and performance optimizer models start
exploiting more sophisticated forms of diagnostic and predictive techniques. The
‘new value creator’ typically uses more predictive and prescriptive techniques. The
speed of insight generation also increases as we move through the models from the
ad-hoc insight generation of ‘information enabler’ models to real-time continuous
insight generation embedded in the ‘new value creator’ models.
Foundational
Classify
Simulate
Predict
Trend
Optimize
Illustrative insight generation techniques
Rear-view
supported
decisions
Foundational: Discover relationships patterns
- Root causes
- Dependencies
- Patterns—behavior, video, audio
- Analysis of cross-tabs
- Inferential statistics—ANOVA, Chi-square, binomial
- Bayesian inference
- Decision trees
- Influence diagrams
- Regression—linear, non-linear, logistic
Classify the data
- Eliminate variables
- Group cases
- Classify instances
- Tradeoffs
- Discriminant analysis
- Principal component analysis
- Factor analysis
- Cluster analysis
- Neural network analysis/Deep learning
- Conjoint analysis
- Supervised unsupervised learning
Trends over time
- Historical future
- Projection
- Quality control/control charts
- Linear regression
- Logistic regression
- Time-series analysis
- Discrete-event simulation
Forward-looking
decisions
Optimize what you are doing
- Objective function
- Constraints
- Genetic algorithms
- Linear programming
- Mixed-integer programming
- Elasticity modeling
Predict what is going to happen
- Static real-time
- Data-rich data-sparse
- Multimedia
- Linear logistic regression
- Time series analysis
- Survival analysis
- Neural networks
- Decision trees
- Collaborative filtering
- Content-based filtering
Simulate alternative future scenarios
- Qualitative quantitative
- Delays feedback loops
- System dynamic modeling
- Agent-based modeling
- Discrete-event simulation
- Complex systems—chaotic, non-linear, complex
adaptive

13. Data and Analytics | 13
Insights from data are not useful unless they can help us make better decisions or
take meaningful actions. Leading companies are defining decision models that
categorize and specify the decisions that get made, insights, options, subsequent
actions and potential for automation. The increase in predictive and prescriptive
models makes it critical to specify the decision rights and override rules in the case
of automated decisions. As the Internet of Things gains adoption, our trust in how
well sensors are measuring observable parameters, the appropriate margins for
error, and decision rights will become a necessity for efficient workflow. Creating
a decision-making culture or mindset requires executive commitment to foster
change throughout the organization by making data-driven decisions versus over-
reliance on gut instinct.
To add maximum value, decisions and actions have to be integrated with the
organization’s existing operations, workflows and technology. In many cases it
changes the paradigm from being reactive to more proactive and significantly
changes the workflow of the employee or operator. In service industries, predictive
models that calculate the propensity for cross-sell or the ‘next best product’ changes
sales and customer service processes. As data and analytics operating models
mature, more complex, interrelated decision nodes in business processes will be
automated. Technologies related to the Internet of Things, smartphones, tablets,
wearables, and ingestibles all will be connected with prescriptive systems to
enable actions with little or no human intervention.
Taking full advantage of faster, more sophisticated ways to model and use data
requires thinking about all of the ways decisions can be embedded in and create
new products and services. The entertainment content we consume is invariably
accompanied by “if you like this, you will like these” advice. Cars assist us in making
parking decisions and give warnings when it is dangerous to change lanes. Physical
assets deploy more advanced sensors that tell maintenance operators when they
should be serviced. In labs such as A-Star in Singapore, techniques for self-correcting
machines are being explored. In many cases, a company’s data and analytics
operating models need to be expanded to help design, develop and deliver
product and services.
The breadth and depth of decisions and actions change when moving from the
‘information enabler’ to the ‘new value creator’ model. In the ‘new value creator’
and ‘performance optimizer’ models, decisions and actions are more fluid, impacting
multiple functional and business units. Moving to a ‘functional’ or ‘information
enabler’ model, decisions and actions are more localized. The progressive speed
and sophistication of insights generated by the different operating models are
reflected in the decisions and actions as well.
Actions
Creating a data-driven
decision making culture
requires executive commitment
to foster change throughout
the organization.

14. Data and Analytics | 14
Developing a tighter link between insights from more sophisticated models, actions
taken and expected impact on financial and operating outcomes is key to success
in capturing shareholder value—and thereby further improve decision making.
As more decisions become automated, companies that fail at this approach will be
beaten by those that succeed. There are three main categories of outcomes, each
requiring different approaches. The first relates to determining how key decisions
and actions enabled by data and analytics are expected to impact operational
metrics (e.g., conversion ratios, efficiency, and productivity metrics) and financial
metrics (e.g., revenues, costs, profitability). With predictive and prescriptive
analytics, companies now have the capability to predict what is likely to happen
and, with better monitoring, observe what really happened. They can then use
advanced analytics to attribute the reasons for any deviation. This attribution
can be with respect to factors that are beyond management control (e.g., economic
outlook, inflation) as well as factors that are within management control. This
feedback between analytic models and real-world results can lead to progressively
better analytic models and, ultimately, better decisions.
Generating outcomes with analytics also necessitates a ‘test-and-learn’ culture
where firms can implement changes in decisions and actions in a limited form,
observe the results, and change the model to reflect reality. In the future, as the
Internet of Things evolves, data continues to proliferate and more decisions and
actions can be taken and automated, companies will need to go beyond considering
how big decisions they make are impacted by data, to having an extremely efficient
approach to making thousands of “small decisions”.
The final factor related to outcomes is monetization. While the above factors
help to improve the efficiency and effectiveness of existing decision making to
increase revenues and profits, data monetization looks at how data and analytics
could potentially result in additional revenue sources. The simplest form of data
monetization is aggregating existing data. Synthesizing new data elements from
existing data that either summarizes data (e.g., principal component analysis)
or draws new insights is the next stage of data monetization. Drawing insights
from data and then selling the insights is the next stage. Further stages of data
monetization involve either making the decisions or taking actions and charging
for it and finally getting paid on outcomes.
The ultimate power of data and insight-driven decision making is reflected in the
outcomes. The ‘new value creator’ model is engineered to improve the value creation
for an organization. The ‘new value creator’ model and ‘performance optimizer’
model are both targeted at the outcome of increasing shareholder value. The
‘performance optimizer’ model is engineered to improve the value from the existing
business model. Unlike the ‘new value creator’ model it could miss out on disruptive
value creation. The ‘functional model’ optimizes the outcomes and value created
within the functional unit. The ‘information enabler’ model is more passive and
leaves the value creation to the human decision-maker.
Outcomes
A tighter link between insights
from sophisticated analytic
models, actions taken, and
expected financial and
operating outcomes improves
an organization’s decision
making capability.

15. Data and Analytics | 15
Conclusion
Many large organizations have a mix
of several data and analytics operating
models without clarity on their purpose,
how they work together and go-forward
plans to build new capabilities to support
a DIAO approach that will continue to
progress in speed and sophistication.
Given the potential shareholder value
impact on the business, the amount
of investment needed and amount of
change moving forward, it requires
no less than C-Suite alignment on the
data and analytics operating model.
Answering several questions can help
companies down the path.
• Mission, Vision, Purpose—
What type of data and analytics
organization do we need? What is
its purpose and the value we expect
it to deliver? Clarity on the amount
and type of benefits expected is
critical to justify the investment.
• Executive Sponsorship
Governance—Who should be the
primary sponsor for this group? How
will it be funded and governed? A
key consideration is making sure the
level and area of sponsorship is able
to provide the group the necessary
support and governance to realize
the expected benefits.
• Organization Structure—Where
will the group fit in the current
organization structure and who will
it report into? What are its different
roles and responsibilities? A key
consideration is determining the
appropriate degree of centralization
and decentralization across
geographies, business units and
functions, and specifying solid vs.
dotted-line reporting relationships
across the business and IT.
• Talent Culture—What are the
skills required within the data
and analytics organization? How
do we acquire, nurture, grow and
retain talent? How do we change
the culture of the organization
into an insights-driven culture?
Transforming to a data-driven
culture needs to accommodate
learning from “gut” decision making.
• Innovation Alliances Model—How
can we continuously stay ahead
and innovate with respect to our
competitors? How much of this should
we do ourselves versus acquiring or
partnering with others? Consider
if and how your organization can
provide analytics subject matter
experts with a constant stream of
interesting problems to solve—
otherwise you will have trouble
recruiting and retaining them.
• DIAO Approach—What are the new
capabilities required to effectively
and efficiently follow the Discovery-
Insight-Action-Outcome (DIAO)
process? How do we embed these
capabilities within our existing
business and technology processes?
Typically, it’s not a bolt-on to how
a company operates.
Disruptors or start-ups that use data
and analytics as a game changer
frequently have built their business
around the discovery, insights, actions,
and outcomes approach. Their data
and analytics operating model is in
many cases indistinguishable from
their broader operating model. All
companies possess the ability to succeed
in this space—it may require a radical
rethink of the business model and how
it operates, but as GE’s former CEO, Jack
Welch said, “change before you have to”.

16. © 2015 PwC. All rights reserved. PwC refers to the US member firm or one of its subsidiaries or affiliates, and may sometimes refer to the PwC network. Each member firm
is a separate legal entity. Please see www.pwc.com/structure for further details. 31947-2015
Authors
Paul Blase
US and Global Data and
Analytics Consulting Leader
312 282 1015
paul.blase@us.pwc.com
LinkedIn
Dr. Anand Rao
Principal, Innovation,
Data and Analytics, PwC
617 633 8354
anand.s.rao@us.pwc.com
LinkedIn
For more information, contact
Paul Blase
US and Global Data and
Analytics Consulting Leader
paul.blase@us.pwc.com
LinkedIn
Yann Bonduelle
EMEA/UK Data and Analytics
Consulting Leader
yann.bonduelle@uk.pwc.com
LinkedIn
Barbara Lix
EMEA/Germany Data and Analytics
Consulting Leader
barbara.lix@de.pwc.com
LinkedIn
Scott Likens
China/Hong Kong Data and
Analytics Consulting Leader
scott.sl.likens@hk.pwc.com
LinkedIn
John Studley
Australia South East Asia Data
and Analytics Consulting Leader
john.w.studley@au.pwc.com
LinkedIn
Carlos Lopez Cervantes
Mexico Data and Analytics
Consulting Leader
carlos.lopez.cervantes@mx.pwc.com
LinkedIn
Jorge Mario Añez
Latin America Data and
Analytics Consulting Leader
jorge.mario.anez@co.pwc.com
LinkedIn
For referral to Data and Analytics
leaders in countries not listed,
please contact Paul Blase.
Learn more
Visit our website: www.pwc.com/us/analytics
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The authors would like to acknowledge significant contributions from Bill Abbott,
Punita Gandhi, and Mudit Mathur.