Business Process Analytics Unlocking the Power of Data and Analytics: Transforming Insight into Income

Business Analytics the way we do it

Business Process Analytics
Unlocking the Power of Data and Analytics: Transforming Insight into Income

Contents

Foreword 3

The power of analytics 4

Is your organization structured to exploit data and analytics? 5

How to manage data as an asset for the entire business 6

The case for a centralized analytics function 7

Sourcing the centralized analytics function 8

How the analytics function can maintain control over decision-making 10

Conclusion: three steps you should take now 11


Capgemini research shows that Foreword
for processes where analytics has Staggering volumes of data from an ever-increasing range of sources are
been applied, companies have available to businesses today. That data (and the insight that comes from it)
seen a 26% average performance is coming to be recognized as a critical organizational asset. High-quality,
improvement over the past real-time or near real-time, business analytics, making appropriate use of
three years “big data”, can deliver significant value by enabling better decision-making
and hence better business outcomes.

In practice, however, the value of business analytics is not always easy to
realize. Obstacles include the technical challenge of collating, managing,
and analyzing vast quantities of internal and external data – both
structured and unstructured – to provide forward-looking insight, cost-
effectively and in the required timescale.

However, even when companies have well-advanced data strategies and
substantial investment in technology platforms and analytical tools, they
have struggled to optimize ROI and achieve analytics maturity.

Customer Analytics Maturity

How can I optimize
Business Value – customer interactions?
Competitive Advantage What is the likelihood of
my customers behaving
in a particular way?

Optimization
The Economist Intelligence Predictive
Customer Analytics

How can I group Modeling
Unit Survey: The Deciding my customers?

Factor: Big Data and How is my business Forecasting
Decision Making performing (in relation
to customer metrics)? Segmentation

The Results:
Descriptive
75% believe their organizations to Modeling
Business Intelligence

Can I predict
future customer
be data-driven but “Drill-Down”
behavior?
“Slice and Dice”
Traditional

9 out of 10 say the decisions Standard and
Alerts What are my
customers like?
they’ve made in the past 3 years Ad-Hoc Reports Why do they behave in
Data particular ways?
would have been better if they’d
had all the relevant information
Capability

1
Capgemini and Economist Intelligence Unit, The Deciding Factor: Big data and decision-making

3

Organizational structure is often the problem. It is no surprise that 56%
The Economist Intelligence of companies say organizational silos are their biggest problem in making
Unit Survey: better use of big data1. Analytics tends to be carried out within, and applied
42% survey respondents say that to, individual functions, whereas to reap the full benefits it is necessary to
unstructured content is too difficult examine end-to-end processes and impacts across functional boundaries.
to interpret
To make this possible, companies need a single, operational view of
their data, available right across the enterprise. As we argue in this white
paper, the best way to achieve this consistent view of data is through the
creation of a centralized, specialist function that provides core analytics
as a managed service to the rest of the business. This function could be
an internal shared service center, or could be created through Business
Process Outsourcing (BPO).

Either way, it is time to start thinking differently about how analytics is
applied, in order to be able to deliver optimal, insight-based decisions
across every aspect of the business. Best-in-class organizations are already
seeing results from doing so.

The power of analytics
In every large business, thousands of decisions are made each day:
strategic, tactical, and operational decisions. It is increasingly recognized
that information, intelligence, and insight are the key to making the right
decisions at the right level and at the right time, yet in practice, many
decision makers report they do not have enough data2. Even routine
decisions such as whether to pay a given invoice are often made without
reference to the relevant scores and risk indicators.

Reports of inadequate data seem paradoxical given the rapid growth of
data volumes – in the medical sector, for example, it has been estimated
that knowledge doubles every five years. The explanation is that decision
makers are “rich” in data that they cannot interpret, but poor in terms of
insights they can use to drive decisions and actions.

Business analytics exists to bridge this gap between the glut of unusable
data and the unsatisfied appetite for insights that can inform decisions.
But clients tell us that analytics, as currently carried out, does not always
achieve its potential. To do so, analytics must generate the type of insights
that can truly inform decisions, so that the business can make the right
interventions at the right time.

This need points to a different way of delivering analytics from the current
norm. Instead of being backward-looking, and focusing on a single point
in time, analytics needs to be forward-looking and continuous. It needs to
embody the right set of business objectives and KPIs to drive the actions
that will meet the organization’s goals (while avoiding “drowning in data”).

2
Capgemini/EIU

4 Business Analytics as a Service


It is clear that, approached in the right way, analytics can help a company
to tackle its strategic challenges, to respond rapidly to change, and to
improve the speed and accuracy of its decisions at every level.

Is your organization structured to exploit data and analytics?
Why a functional approach
Few organizations have so far seen the return that they expected from their
to analytics is sub-optimal:
investments in business analytics. To a large extent, the problem arises
three examples
from the way they are approaching it, and from the type of questions they
Food manufacturers typically are asking.
waste stock worth hundreds of
millions of Euros through shelf-life Specifically, in applying analytics they investigate questions that reflect the
expiry. Identifying the reason for needs of a particular department or subsidiary instead of the needs of the
slow-moving stocks can eliminate
business as a whole. No wonder that in our recent “big data” survey, well
probably 60% of the waste. But
you need to analyze not just the over half of respondents cited organizational silos as their biggest obstacle3.
immediate supply chain but the
end-to-end process, including Companies can gain immensely by focusing on what counts for the
manufacturing, differences between organization as a whole, not just one silo. That means taking an end-to-end
geographies, and changes to the view of each process: order-to-cash, source-to-contract, procure-to-pay,
business environment. quote-to-deliver, marketing campaign effectiveness, and so on. Analytics
must therefore cross functional boundaries: its true power emerges when
Retailers often lose out through information from different functions is connected.
slow price-setting. Analytics
techniques allow pricing scenarios This approach allows analytics to answer more ambitious questions, like:
to be rapidly compared so the time
taken can be reduced from days ƒƒ
What factors determine whether and how much our customers buy?
to hours. But again, modeling has
ƒƒ
How effective are advertising campaigns and other promotions?
to take a comprehensive view of
the business and its environment,
ƒƒ
Where and how should we invest to achieve a particular level
rather than looking at one function. of demand?
ƒƒ
How important is speed and efficiency of fulfillment to getting
repeat sales?
Organizations in all industries
ƒƒ
How are customers responding to our overall offer, and can we change
suffer from revenue leakage, for
example as a result of incorrect
that using social media management?
pricing or customer deductions. It ƒƒ
What should our strategy for collections, disputes, and deductions be?
is only by applying analytics to the ƒƒ
How much does it cost us to process an inbound order or invoice?
end-to-end order-to-cash process, ƒƒ
How can we harness the power of digital marketing and channel mix?
including pricing, discounts, and
terms of trade, that leakages can A client found that it was spending an average of €200 to process each order,
be stopped and recovered. when industry best practice cost was just €8. Applying business analytics to
the end-to-end process allowed it to pinpoint the process steps that were
causing inefficiency.

The data needed to answer these and other complex queries is often already
available within, or to, the organization. The challenge comes in being able
to access and interpret it quickly.

3
Capgemini/EIU

5

How to manage data as an asset for the entire business
The Economist Intelligence
Managing data is becoming an increasingly complex task, involving
Unit Survey:
external and unstructured data along with internal and structured data.
85% say the issue is not about Social media data, credit ratings, marketing data from third parties – all
volume but the ability to analyze these potentially need to be rapidly integrated with internal records, and
and act on the data in real time refined into a useable form, to allow decisions to be fully informed.

Even within internal systems, data is not always harmonized across
various ERP installations and subsystems such as procurement, CRM, and
accounting. Equally problematic, data tends to be static whereas decisions
often need to be based on up-to-date and dynamic information. In addition,
people in different parts of the business often conduct analytics working
from different data sets that do not match one another closely.

To get the best out of data and enable the end-to-end approach discussed
above, data needs to be managed as an asset for the organization as a
whole, not just its individual functions.

A central data repository or insight center can ensure that everyone is
working from the same data. Analytics and access tools can then be applied
to create a variety of windows on this one view. A window (typically in the
form of a context-sensitive dashboard) might relate to customer acquisition,
business strategy, resource allocation, governance, or procurement, for
example. What is essential is that the different windows all look onto the
same data – that is, the same absolute truth.

An enterprise-wide approach to data management is a significant
undertaking, and needs board sponsorship. It also needs a model for
converting data into insights that can inform decisions.

Data Value Pyramid

Reports
Dashboards
Decisions Transactional delivery

Application of business analytics
Interpretation
Propensity models
Knowledge Scoring

Integration
Aggregation
Third party data
Information Social media data

Integrity
Validation
Completeness
Data Management



Getting data right is a prerequisite for creating the insight to drive decision-
making. Another equally vital requirement is to have the right expertise
available to interpret the data – and we shall argue that the solution lies
once again in centralization.

The case for a centralized analytics function
The permanent analytics function The essential skills needed to apply best-in-class analytics are in scarce
needs the following characteristics:
supply. By establishing a permanent, centralized analytics function, an
organization can bring together the best analytical skills available to it
� available to the whole business,
Is and harness them for the benefit of the business as a whole. This is the
not just to specific functions same thinking that has resulted in successful transitions of other support
� responsible for maintaining
Is functions to a shared service basis.
data quality and integrity on
behalf of the entire business The analytics function should be a center of excellence, delivering
� suitable software tools and
Has analytics and insight as a managed service across the enterprise. As well
partnerships (big data capabilities as enhancing speed of development, it should also reduce costs, enhance
are imperative) control, and enable faster and more consistent benefits realization.
Treats analytics tasks as a
�
long-term process, not a The key to deriving value from the centralized function is the application
one-off problem of business process tools that allow transformation of data into insights,
� a global perspective on
Has and efficient delivery of these insights to the points in the operational
the organization environment where key decisions and customer interactions are made.
� a culture of recognizing
Has
data as an asset and valuing The function should be equipped with a model like Capgemini’s Global
the insights it can generate Process Model. This can organize KPIs, benchmarks, and controls for
� business-oriented, and
Is the entire business, helping to achieve harmonization and consistency of
knows that the purpose of approach, and to ensure that analytics outcomes can be used effectively
analytics is to drive decisions across the enterprise.
� strong management, clear
Has
priorities, and accountability for People working in the analytics function need advanced skills and
providing value for money talents, including:

ƒƒ
Data science and architecture
ƒƒ
Statistical modeling, scoring, and propensity profiling
ƒƒ
Consulting skills and business knowledge (for discussing requirements
with users)
ƒƒ
Data management skills
ƒƒ
Training skills (to teach end users to apply analytics outputs)

Unit Survey:
26% is the level of performance
improvement already seen from the
application of big data analytics

7

The cost and scarcity of these skills are not just a strong argument for
centralizing analytics: as we shall discuss next, they also strengthen the
Unit Survey:
case for implementing it as a bought-in function or managed service.
41% is the level of performance
improvement expected in the next
Analytics as a Service Delivery Value Chain
3 years

Consultancy, Data Management, Analytics Processing Automated Reporting,
Scoping and Classiﬁcation and and Dashboards and
Data Strategy Standardization Service Delivery Decisions as a Service

Sourcing the centralized analytics function
The discussion above points to a new way of organizing business analytics.
Instead of tackling analytics tasks on an ad hoc basis, and within particular
functions, getting the best out of analytics requires a permanent, centralized
organization: one that provides analytics as a managed service to
business users.

How exactly you decide to source business analytics is a separate decision
from the decision to centralize. You might decide to set up a shared service
center internally. Or you might opt for an analytics hub provided via
Business Process Outsourcing (BPO) – an attractive option if you cannot
afford, or cannot easily recruit, the specialized skills needed.

In deciding how and where to set up a business analytics capability, it is
important to make sure that the following are available:

ƒƒ
Ability to understand business processes
ƒƒ
Familiarity with, and expertise in, advanced analytics
ƒƒ
Ability to ensure that analytics drives business decisions
ƒƒ
Appropriate technology solutions and knowledge

Cost implications should also be carefully evaluated. “Pay as you go”
options may be more attractive than those requiring a large up-front
investment and inflexible ongoing expenditure.



BPO appeals to many organizations, not only because of the skills and
The Economist Intelligence funding aspects but also because it can provide virtually instant solutions.
Unit Survey: Many of the disciplines of business analytics are already available in a form
56% cited “organization silos” that can be immediately applied to specific challenges within your business.
in the top three impediment to
effective decision making - 50% Capgemini, for example, provides a comprehensive set of business analytics
cited “shortage of data analyst” solutions, the most important of which are listed in the table below.

Analytics solutions available from Capgemini
Capgemini’s business analytics portfolio is a suite of solutions supported
by our global business analytics practice network and insight center. The
solutions help clients tackle key challenges in a range of contexts.
CFO Analytics: Exploit the data that you already have to improve financial
and operational performance. CFOs get real-time or near real-time insights
that allow them to take swift action.
Working Capital Analytics: Reduce capital tied up in inventory and
semi-finished goods while maximising availability and service levels.
Customer Analytics: Make your customer interactions more effective and
mutually beneficial by combining unstructured social data, transactional web
data, and structured organizational data to gain a deep understanding of
what customers want.
Marketing Analytics: Our combination of advanced analytics and continuous
feedback mechanisms ensures your customers receive the most relevant and
timely offers, and that your campaigns become more intelligent over time.
Predictive Asset Maintenance: Information governance and leading
predictive analytical modeling can systematically identify the right
maintenance and inspection regime. Achieve compliance and minimize
unplanned downtime while avoiding unnecessary work.
Enterprise Performance Analytics: Use enterprise-wide insight and an
understanding of the impact of key decisions to achieve performance
improvements, even when readily-available efficiencies have already been
realized, and when competitors can quickly replicate innovation.
Social Media Analytics: Despite the vast volumes of unstructured data
involved, advanced text (and other) analytic techniques mean you can hear
and understand what your customers are saying and then respond in a fast,
efficient, and scalable manner.
Advanced Planning & Scheduling: Improve service levels and reduce
operational costs through better use of people, process, technology, and
information. Planners can base decisions on the right information via powerful
visualization and optimization techniques.
Fraud Management: Gain competitive advantage by using analytics to
prevent and detect fraud in a timely manner. Reduce costs and offer a better
service to customers and partners, while managing financial, reputational,
and punitive risks.
Risk Analysis: Apply advanced analytics to manage risk holistically at
enterprise level. Link different types of risk, such as credit risk and market
risk, together to get a complete picture. Possible areas of focus include
revenue, margin, controls, and cashflow.
Spend Analytics: Develop a better understanding of spend across the
organization and identify opportunities for efficiencies and cost reduction.

9

How the analytics function can maintain control over
The Economist Intelligence decision making
Unit Survey: Companies need to make decisions fast, but they cannot afford to lose
62% Dispute the proposition control. Approached correctly, automation of decision-making processes is
that most operational / tactical an opportunity to strike the right balance between speed and control, as
decisions that can be automated well as to save considerable sums.
have been automated
The need to maintain control is an additional argument for creating a
centralized and permanent analytics function, since this is the easiest way
to achieve continuity and universality of control.

The process of feeding insights into decisions can be fully automated in the
case of operational decisions, and partially so in the case of tactical ones.
Strategic decisions may still need to be based on “gut feel”, but analytics
can ensure that the right “nutrients” in terms of insights are available to the
decision-maker.

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Control over decision making can be maintained by building analytics-
based checks for each quadrant into the automated decision-making
process. The checks will ask questions such as:

ƒƒ
What have we achieved strategically?
ƒƒ
What resources have we deployed operationally?
ƒƒ
How effective have we been at innovation and learning?
ƒƒ
What is the financial impact and are we getting the returns we require?

The questions in each quadrant need to be looked at together rather than in
isolation. By using an automation tool such as Capgemini’s Global Process
Model to bring the elements together, the centralized analytics function can
help enforce consistent controls, enterprise wide.

Conclusion: three steps you should take now
A fuel-saving analytics application,
“Trip Optimizer”, looks set to There is little doubt that business analytics is best provided as a service by
reduce General Electric’s fuel use a central function, but organizations need to weigh up the best options for
by up to 14%.5 them in terms of sourcing. In the meantime, here are three steps you can
take immediately to start organizing your analytics better:

�
Identify the important key decisions that your organization needs to
monitor continuously this year. Which could be improved if you had
better insights into the business and its environment? Arrange these in
order of value.

�
Review the analytics initiatives already under way in your company.
Which ones address the areas identified in step 1? Are there important
gaps that are not being addressed?

�
Consider what analytics resources exist in your organization already.
Which do you regard as most effective? Could they be redeployed to
address the gaps identified in step 1? Could a centralized function
providing analytics as a service help?

Recent Capgemini research4 into “big data” and analytics found that for
processes where analytics has been applied, companies have seen a 26%
average performance improvement over the past three years; they expect it
will improve by 41% over the next three.

For best-in-class companies, analytics already informs every aspect of the
business. Can you afford not to follow their example?

4, 5
Capgemini/EIU

11

About Capgemini
With around 120,000 people in 40 countries, Capgemini is one of the world’s foremost providers
of consulting, technology and outsourcing services. The Group reported 2011 global revenues of
EUR 9.7 billion.
Together with its clients, Capgemini creates and delivers business and technology solutions that
fit their needs and drive the results they want. A deeply multicultural organization, Capgemini
has developed its own way of working, the Collaborative Business Experience™, and draws on
Rightshore®, its worldwide delivery model.
Rightshore® is a trademark belonging to Capgemini

More information about our services, offices and research is available at

www.capgemini.com
For further information contact terence.sandiford@capgemini.com
or visit http://www.capgemini.com/business-process-analytics

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