White paper on how to map Analytics to Strategy to establish the value Analytics delivers and to prioritize investments and efforts.

1. Zeno Analytics
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Analytics Strategy Value
Map: mapping analytics to
strategic value
To derive value from analytics you
need to create a synergy between four
dimensions: Business Process, Data,
IT Architecture, and Analytics as the
glue to bring them together. At the
intersection of these four dimensions
they come together to optimize your
decision making. Realizing this
convergence is how you derive the
value.
To achieve that, you will need to think
about a decisioning framework to help
you define what guides your decision
making:
• What are your business
objectives?
• What are your business goals
linked to those objectives?
• What limitations do your ethics
and values put on your decision
making?
• What limitations or
requirements are put on your
decision making by society
(legal, social norms,…)?
• How do you want to make
decisions and monitor and
control decision making in your
processes?
• What decisions you make in
which processes?
Based on that framework you can
define a business architecture that can
support decision making in your
organization: a Decisioning
Architecture.
Analytics Strategy
Value Map
This framework is based ‘Value
Modeling: A Systematic Method to
Unveil the Business Value of Industry
Solutions’ (Proceeding of 2014 Annual
SRII Global Conference) and the
concept of strategy mapping from the
balanced scorecard approach (See for
example ‘Having Trouble with our
Strategy? Then Map It’ by Robert
Figure 1 Convergence

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Kaplan and David Norton (HBR
2002)). The basis is to map your KPI’s
on all levels from business objectives,
down to your operational processes,
and make sure that you understand
how they influence each other to the
top.
Components of the Decisioning
Architecture
The Decisioning Architecture contains
4 levels of components as displayed in
Figure 2.
• Business (in green)
• Decision Management (in red)
and Analytics Infrastructure (in
blue)
• Data Infrastructure (in orange)
• IT Infrastructure (in purple)
On the right-hand side (in yellow) the
management roles and their
responsibilities are displayed. With the
increased regulations and the
importance of an organization’s ethics
& values a n overarching
(independent) governance process is
crucial.
Business management is responsible
for setting strategy and translating
that into business objectives and how
they are measured. These objectives
can then be targeted using different
tactics and the related business
processes where business
management sets priorities and
determines decision options. All of this
is embedded in the ethics & values
that define the organization and it is
linked to the
aggressiveness/defensiveness: the
risk/opportunity appetite.
Analytics Process Management is
responsible for defining how analytics
will be performed and linking the
output of the analytics to the decision
made in the business process. They
are also the guardians of analytics
quality. They need to implement a
constant critical review process that
monitors and challenges the analytics.
They need to manage the life cycle of
analytics. To do this they need to
define what is needed in the analytics
infrastructure and how the developed
analytics capabilities can be best
managed to support the decisions in
the decision management approach.
Data Management is responsible for
the availability, quality, collection and
storage of the relevant internal &
external data. The data infrastructure
is the foundation of analytics & data
driven decision making: bad data →
bad analytics → bad decisions.
Finally, systems management needs
to make sure the technical IT
infrastructure is available, secure and
allows for integration of data,
analytics and business processes.

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.
Mapping the Strategy
The goal of the Analytics Strategy
Value Map is to link the Decisioning
Architecture and the activities in that
architecture to the KPI’s at different
levels. This allows you to relate it back
to your overall strategic objectives. By
using this map, you can look at the
immediate impact of IT, the
immediate impact of data, the
immediate impact of analytics, and
link that back to the business
outcomes as displayed in Figure 3.
Figure 2 Decisioning Architecture

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A simplified example
A very simplified example: you could
have as a business objective to
increase revenue. One of the business
goals attached to this objective would
be to acquire new customers. Of
course, there could be many others,
like: sell more to current customers,
retain more customers etc. A decision
that you may want to think about is
‘who do you want to target?’. To do
that, you will need the analytic
capabilities on prospecting and credit
limit modeling. Prospecting to find the
targets, and credit limit modeling to
determine how much you are willing
to sell to them.
To achieve that, you may use
Predictive Modeling/Machine Learning
and Campaign Optimization to find the
right targets. As input you would
need, from the data infrastructure,
Customer & Prospect data, which
could be from your internal data:
Customer Product information and
revenue information, and previous
campaign data. From an external data
provider for example the marketable
universe for the potential customers in
your market. All of this is supported
by the infrastructure.
Mapping this, you could say at the top
end, for example, that your KPI is
your total revenue, and that you drive
that on your business goals with your
number of new customers and
revenue per new customer. So, using
prospecting and credit limit, you could
influence the conversion rate, order
value, and the payment received. That
Figure 3 Mapping Analytics Value to Strategy

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is driven also by, for example, by data
quality indicators, like the bounce
rate, or the number of duplicates in
your customers in your customer file;
they have an influence on the
conversion rate, and on the order
value, because if you have the wrong
information on your customers, you
might be missing out on valuable
prospective customers. The same goes
for the number of customers with
missing data. And then of course your
external credit risk accuracy model
because that influences your payment
received, but also your order value,
because it influences your credit limit
model.
Creating a prioritization matrix
Once you’ve looked at ‘where are my
potential value drivers from
analytics?’, you can look at the
expected value and the expected
effort. These you can use to build a
prioritization matrix.
The example here, in Figure 5, is
simple: a high/low division where if
the expected value is high, and the
expected effort is low, that is your low
hanging fruit. If the expected value is
high and the expected effort is high,
you may want to start planning those
initiatives. If the expected value is
low, and the expected effort is low,
you may want to do that when
resources are available. The ones
where the expected effort is high, but
the expected value is low, you do not
want to touch it yet. That may change
in the future, and you might identify
Figure 4 Simplified mapping example

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that there is an influence of some of
those on other high value ones. They
may move across this matrix.
It’s not fixed in time; you need to
reevaluate this prioritization matrix on
a regular basis.
How to determine this value and effort
basically is by doing a “proof of value”
project: a limited time project with
clear goals linked to the business
objectives. You need to make sure
that it is not just an academic exercise
but that it leads to real results. Then
you can evaluate the actual expected
value of analytics for your own
organization.
You may also want to look at stories
from other organizations and industry
analysts. You don’t want to only look
in your own industry, because you
may learn from other industries. If
you’re in TELCO, you may learn from
financial services; if you’re in financial
services you may learn from retail; if
you’re in manufacturing you may
learn from professional services; if
you’re in professional services you
may learn from the hospitality
industry etc.
You do need to be critical about the
content, of course, because most of
the published stories have been edited
and polished by the marketing
communication departments.
You want to, and this is an old one
and still valid, “think big, and start
small”. Build incrementally to allow
your organization to adapt to making
decisions based on analytics, to adjust
your analytics to changing
circumstances. Your environment and
organization are constantly changing.
You need to continuously learn and
improve. You don’t want to boil the
ocean.
Figure 5 Prioritization Matrix

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About Zeno Analytics
Zeno Analytics is an “Analytics Translator” and “Data Translator” that will help
you close the gap between reality and expectations of your advanced analytics
(Predictive & Prescriptive Analytics, Machine Learning, AI) investments &
projects.
By applying an incremental approach, connecting business, analytics and IT,
Zeno Analytics takes you closer to you objective step-by-step. This approach
allows for an agile approach to gaining business value and adapting the
organization and processes to create maximum leverage from your data &
analytics initiatives.
You can contact us at:
info@zenoanalytics.com
www.zenoanalytics.com