The Data-To-Business Bridge Model Mathieu Rioult
1
The Data-To-Business Bridge Model
Data structure & Actionable Insights exploitation from multi-
stream sources dedicated to New Business Development
optimization. Application to the Chemical Industry
RÉSUMÉ
In the tremendous context of the digital
transformation, I wanted to bring under
the spotlights a hidden topic – i.e. How
to concretely extract a business value
from both digital & human streams and
how to apply it to get its wide range of
benefits for your new business
development organization.
Mathieu Rioult

The Data-To-Business Bridge Model Mathieu Rioult
1
Digital Transformation & Business
Decisions: A proven gap coming
from unstructured data & unaligned
New Business Development
organizations
Few months ago, SAP stated that Digital
Transformation highlights and focus on
numerous topics & challenges for companies:
Gathering all company’s internal information &
incorporate data form external sources that,
once processed & analyzed, can bring changes
in existing workflows. A consequence of an
aggressive use of data is the transformation of
business models & decisions.
The digitalization is effecting these changes in
every industry and function – The traditional
sales channels are now integrated into a multi
channels approach and the data generation
coming from Prospects & Customers, ie.
Users, highlights new services more attractive
and compatible to the users’ journey
That’s for the theory and the expected Big
picture. Reality is unfortunately much more
complex. I had the chance to work for a
company that supported more than 100 New
Business Development organizations in the
chemical industry over the years in the
acquisition and implementation of multi
channels and - for 90% of them – the starting
point was the same: None of these companies
where structured to handle & exploit its data
– whether these where coming from traditional
channels or from new ones – including digital
streams.
A key to understand such statement is the
irregular & unstructured use of CRM tools.
Most of the time, such systems are used to
store raw data - but not so often used for
being exploited. Sales people usually consider
CRM tools as a storage facility to save their call
reports or raw information captured during
the sales process – when they do it. A good
practice but not efficient enough. Moreover,
they add their own wording or may forget
pieces of information. As a result, an
unstructured data with lot of difficulties to
analyze and extract the relevant insights.
But the Sales Force is not the only one to
blame. This data structure must come from
specific requirements of the different
departments involved in Product & Business
Development. Most of the time, departments
blame the Sales force to retain or even not
capture the right pieces of information. In fact,
the Sales force should know from the others
what type of data they need to collect in first
place. And these departments should
understand how to extract the value of such
information. A value, if well identified and
designed, quickly turned into insights helping
to take or support strategic decisions. The lack
of cross-coordination & education to each
other needs is clearly demonstrated here,
acting as roadblock to take these decisions.
Considering these facts and beyond the data
structure need, it is a human coordination
process between departments to implement
and a mindset to adopt by each internal actor
involved into that process. Such coordination
will lead to a strong potential opportunity to
build collaboration workplaces for them in
order to talk and extract the – now so called –
actionable insights – the grail leading to
strategic decisions.
A legitimate question would be “why should I
do that? What is the concrete business value
behind it?” – Well, these 5 years of experience
have shown me that companies that built this
bridge to fill the gap (and implemented the
right mindset, methodologies, processes &
tools) beneficiated from the following results:

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 Higher Chance to convert prospects
into sales & Better Focus of the Sales
Force
 Faster Speed to convert prospects into
sales
 Faster Settlement of Marketing
Strategy
 Deeper Market Understanding
 Faster response & higher efficiency of
the Technical Support
 Higher chance of Technical success for
the prospect’s project
 Stronger Technical Response to
market challenges
 Accelerated & Relevant Applied
Research
For instance, across the 30 clients I worked for
– coming from the polymer, coating and
cosmetic sectors, I monitored or participated
to concrete and strong achievements such as:
 2 years of R&D spending saved by
having validated applications with
partners & structured data pool that
confirmed market presence &
potential.
 The development of a data pool in new
applications 5 times bigger and 6 times
faster than the original one (aged of 2
years) & 30 partners ready to validate
such leads
 The creation of a market presence in a
geographical Region with 80% of
prospects coming from our New
Business Development Initiative – A
program that in total for this mission
generated more than 100 laboratory
trials at different companies in 2 years.
 The discovery in average of 3 to 5 new
successful applications per year & per
program – depending on the strategy
followed.
 In general, 80% of these New Business
& Market Development programs that
reached the agreed KPIs of success.
These results among others were mainly the
consequence – beyond the digital strategy and
the human resource - of the capability to
structure the collected data and turn it into
strong insights that contributed in taking the
rights decisions for the existing or future
business of my Clients. It positively impacted
their departments that received consolidated
data – and even direct insights – and that had
the opportunity to run multidisciplinary
actions – such as repositioning with the
corresponding set of comparative data vs the
new benchmark.
Plus, such asset is now more than ever critical
if you do consider the top 6 major business
decisions for 2020 that +2000 C-level &
business leaders rated in the latest PWC survey
– such as: develop a new product or service
(31%); enter new markets with existing
products or services (15%) or change business
operations (10%). Moreover – These key
decision makers acknowledge at (61%) their
companies could rely on data analysis more
and intuition less and they don’t consider
their own organizations to be highly data-
driven. In other words, it may be time to say
goodbye to the Sales force “God Feeling” and
start focusing on tangible evidences.
Such move and all the related benefits listed
above are possible to obtain by implementing
data extraction and exploitation within your
organization.
However, it has to be settle in the right way.
Data need to be structured. Key departments
have to be identified, kept in the loop and need
to work with each other on specific topics with
the right mindset and the corresponding tools
on a regular frequency to make it work. And
they need to track down the progress of such
initiative to show such benefits.
To get there you need to build what I call the
“Data-To-Business Bridge”. A simple
“coordination & action” concept based on four
pillars / key actions:

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1. Data consolidation & structure
2. Departments coordination for data
extraction & transformation
3. Insights transfer & implementation
into the relevant departments
4. Key Decisions & Results
communication
With the digital transformation and the “data
use” awakening that happen all around us, you
may have been already exposed to such
statement. Hundreds of articles claim the need
to adopt a digital structure and automated
data-extraction models but in the end, let’s
face it. Adopting a “ready-to-use” solution is
the must but it requires huge investments and
lot of time to awake and educate the
department consciousness, set up the solution
infrastructure into the organization and make
it work on daily basis. Plus, a risk appears to cut
off most of the cross-departments
interactions that make your insights valuable
and your organization a continuous learning
environment. In the other hand, relying only
on your “God’s feeling” or the one of your
Business Developers or Departments becomes
less and less aligned with a world that require
structured insights from the market serving
the entire organization as fuel for the learning
machine (not just one individual) and the
required business decisions such as argued
smart moves related to weak signals
identifications or proof of blue oceans
existence.
Today, what I do propose here is a
compromise, a methodology to extract,
justify, apply and track actionable insights
from a structured dataset and a cross-
departments exchange, without investing in a
full-range solution out of reach for numerous
organizations. It is based on what I did observe
during those past five years, i.e. an abstract of
solutions applied over 30+ NBD organizations
in the Chemistry industry with concrete
example when needed.
I will describe in details each pillar of the “Data-
to-Business Bridge” and in the end, I would
propose you a short exercise to evaluate your
ability to extract & exploit data for serving your
NBD strategy. So, ready to jump in a brave new
world?

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1. Data Consolidation &
Structure
Established Fact: Nowadays, data made for
Business Development come from many
sources: The traditional ones, i.e. From CRM &
prospects development, customer’s feedback,
etc. And from the digital ones, such as Online
marketing surveys, opinion leaders, online
leads, open data etc.
So many streams but not so many places to fill
in the right information. And when tools are in
use, such as CRM tools, it is a non-sense to
believe that everybody use it at it should. As
consequence, because nobody showed the
value and how to do it, you end up with a tool
like Salesforce used as call reports storage
facility with unstructured raw data. We all
have experienced it into our companies over
the years.
Yet, the solution is simpler than you can think.
Just talk altogether in a smart way by
exchanging on accurate topics. Explain and
educate your stakeholders on the value that a
dataset can bring. And make them thinking
about what they need or why they should
collect it. By having everybody on board,
things naturally accelerate.
How? Well, you need to run three specific
actions when it comes to data structure: Data
Origin identification; Data needs & structure
requested by departments; Data storage.
Why? The output will be a set of
unidimensional data (one information at a
time) coming from streams ranked by
relevancy and stored in an accessible and
identified location. So a structured data set
ready to be exploited, aligned with
departments expectations and useful to
extract complex trends and weak signals.
Let’s now review these steps in details:
a. Step 1: Identify the Data Origin
In my sense, first thing would be to assess the
number of streams you beneficiate from or use
for the business development. Is it only data
from customers & traditional prospects? online
leads from digital initiatives? Marketing
qualified leads? Other?
Knowing where your data come from will help
you to explain the context to the
departments. Below is a table that attempts to
summarize the sources:
b. Step 2: Involve the departments that
can / could use these data
This is the next part of your action: meet each
department and understand what they would
need in term of data. Moreover, you need to
understand what they do require in term of
data: their origin of course but also the type: is
it for a tactic use (short term; immediate use)
or strategic use (long-term; continuous use).
For instance, expose them the following
situation: when a discussion starts with a
prospect to engage a potential project
development – what kind of information would
your R&D or Marketing department like to
collect? Which dataset would be immediately
used? Which one could feed a deeper thought.

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And this at any stage of the project
development process that your Sales force is
running. You can turn it as well as a meeting
involving Marketing, Technical Service and
Sales departments where all of them have to
agree on the right target of prospect they
should generate and develop. And of course
what the right set of information to collect is
and which of the data would be critical to select
the “best-in-class” prospects? Structuring data
would then become easier and a concrete ID
profile could be generated such as: the
“qualification reports” which regroup
marketing, sales & technical data at the same
place and that would be accessible by anyone
any time.
To provide some guidance here, the best thing
is to give an orientation to your exchanges.
Lead the discussion to ensure they provide you
the data that must serve your “new business
development purpose”. So ensure in first place
that you are aligned with yourself on the value
you wish to extract.
Another example to illustrate this:
You need to structure your data to monitor
your sales process or investigate on a potential
hurdle. The structured data could be:
 A tracked & updated pipeline status & sub-
status
 Age of the prospect’s project
 Timing to sign Non-Disclosure Agreement
 Frequency of interactions & last touchpoint
 Sampling date
 Lab trials date
 Technical match (or score)
 Potential volume & revenue
 Etc.
Combining them will clearly highlight the
prospect status – in particular if you make the
study on 30 ongoing projects. Plus, you
obtained at least 8 unidimensional data. 8
categories describing your prospect. By doing
some math, 30 information about one category
– enough to evaluate your ability to sample in
time or to show evidences that Non-Disclosure
Agreements kill opportunities … Just be careful
to not overload your space with a massive
model. Everybody would get lost – including
yourself.
To go further on this topic, let me share with
you the following table – It is gathering 10
categories of value you can extract from
structured data. These ones have been
assessed across the years while I was applying
this model - they provided great insights for
New Product Development & New Business
Development topics. I have also commented
on their use – strategic vs tactic. You can use
them in first place to initiate the discussion:
*NBD: New Business Development; NPD: New
Product Development
Step 3: Create a space to store a ranked,
unidimensional and organized data.
Finally, “Structuring” means also accessing the
data in an exploitable way at a known place.

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First, Create or use a tool that you can access
and have the organization being able to access
it as well. Ensure it is something that you can
modify and model as you wish. I refer here to
an Excel spreadsheet, Salesforce mods or any
other solution that comes to your mind. There
is a huge variety of tools available on the web
to digitally act as data warehouse.
Then, having a place to store a qualification
report is good. But having dedicated spaces as
well to split it into different sets of information
for further extraction is better. Nothing
complex here but which can generate great
insights and accelerate the process. For
instance:
The important fact to keep in mind here is to
absolutely set up one category per piece of
information. A list of bullet points into one cell
would not allow anything and would be as
efficient as a text in a Word file.
One dimension / category per information is a
must to have and a strong need to educate the
“data suppliers” (i.e. The ones that collect the
data and store it) appear but must be
overcome.
Step 4: Educate on regular basis your “data
suppliers” with simple messages & tools
Triggers must be found and I admit that today
it remains a challenge. Companies were and
are continuously testing solutions, all with pros
and cons: creation of Inside Sales position;
Intense formations; micro-management;
Rewards … Here I would propose three
suggestions based on what I experienced:
a. Smart Education: communicate about it
as a win-win situation: Getting a
structured data from them was possible to
their work. And they did and will receive
structured information in return. I am
thinking about one example from my past
experience: I did provide to Sales
Managers full qualification reports with all
possible details about the prospect’s
projects and this, every time I qualified a
lead. Plus, I showed them the insights
coming from a structured pipe – helping
them to improve their own sales
development process. Thus, they all came
back with positive feedback, embraced
the overall new business development
program and started to deliver all possible
data they could collect – and the most
important thing – structured data.
b. Ease to supply the data: select or design
a nice-to-use tool; avoid complex entries;
do not overload the tool with numerous
categories by creating shortcuts & simple
algorithms that will calculate by their own
some entries.
c. Regular sessions during meetings to
refresh the need: Some people may need
clarifications. Other just need to be
reminded they have “unfinished
business”. They tried and will try again to
demonstrate their good will so no need to
start an internal war. Another good point
of this type of meeting is to quickly
identify the reluctant ones. Resistance
usually appears but then, it is a question
of personality handling (check MBTI
assessment & tools) and change
management.
Of course, other suggestions could be made.
We could imagine monitoring per “data
supplier” the number, relevancy and quality of
data that we collected. A new KPI that could be
considered in management or even in incentive
models or rewards.
And finally, sometimes, the outcome is just
enough to justify doing it in the right way.
Having unidimensional data is the entry door
to quickly identify complex trends, identify
weak signals and of course, extract actionable
insights. But before that, a coordination work

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would be needed. I strongly believe that the
learning loop starts with consolidated data.
And the next step of the loop is to discuss about
them.
2. Departments coordination
for data extraction &
transformation
Now that your data is structured it is time
to coordinate your departments. Unless your
company has invested in a massive cloud
structure with Intelligence system that uses
complex algorithms to analyze the data and
dispatch it as actionable insights to the relevant
departments, you are going to handle this part
by your own.
Of course, as the data are accessible by
everyone you could ask them to capture this
information on regular basis to extract by their
own valuable insights. In my sense, several
risks could happen such as departments
stopping to do it on regular basis. Furthermore,
you miss the chance to get all your
departments aligned on co-thoughts actions at
the same time.
Four conditions should be met to ensure a
proper value extraction from a data set by the
departments:
1. The Data-To-Business process owner. No
surprise. The champion that rose beyond
the awakens. The key position to ensure
the integrity of your data. The project
leader that will bring everyone around the
table and make them exchanging on the
collected data. The keeper of the whole
process. The cement of the bridge.
Without this position, it would be almost
impossible to build the model as
everybody could apply its own vision of a
structured data or draw its own
conclusions on the cross-departments
exchanges.
Below are the key functions of the D2B
process owner. This person:
 Manages the entire Data-To-Business
process
 Owns the structured data and is
responsible of its integrity
 Owns the D2B processes & tools and
can modify them
 Can select resources and dispatch
them with parsimony into the
process.
 Should be strongly empowered by the
Management to ensure His / Her voice
is the final one.
The profile of such champion is yet to be
determined and lot of organizations have
their own vision but raw suggestions can
be made based on what I have already
described and seen:
o Core competencies in 1. Marketing
(strategic & operational) & 2. Business
Development (processes & analysis)
o Cross-department management & project
management skills
o Communication skills & networking ability
o Change management skills
o Technical Knowledge of the company’s
products / services
o Knowledge around the data (origin; type;
value extraction of their analysis)
o Business & Digital acumen
o Creative & Analytical
In term of organization, delegating this position
to somebody already in place could be
considered but the consequent quantity of
work it represents may overload your
champion that will naturally protect his core
mission. Creating a specific position is a smart
move – just ensure legitimate it with the teams
/ departments – otherwise their resistance
move will be infamously known (“no

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experience within the company”, “no
knowledge about market, technology, clients”,
god-feeling reactions will prime, ...). And here,
another smart move would be to rely on your
department representatives – stakeholders
that I am going to introduce now.
2. The mindset of the department
representatives. The involved
stakeholders should acknowledge and
even being convinced in the value they
could extract from these meetings. They
should adopt a collaborative and “thinking
out-of-the-box” mindset. Involving
reluctant stakeholders will end with no
proper results – whatever the frequency or
the relevancy of such meetings. 2 keys in
my sense are:
o Their working profile – are they
creative, collaborative, sensible to
“data” meanings & impact,
awaken and “really” aware about
digital streams and business
needs?
o The educational actions you will
put in place to ensure they
understand the objectives & the
potential benefits.
3. The frequency of meetings. This point
could depend on the regularity of the data
generation and NBD progress. Massive
streams would need frequent meetings to
avoid any unwilling data overloading that
could break down the process. In the
opposite way, maintaining frequent
meetings to handle poor data pools could
negatively impact the mindset and the
motivation of your team. Finding the right
balance is nothing complex but should be
closely monitored. My experience in that
field allows me to provide you a benchmark
to start with:
o Regular meetings on a monthly basis. No
surprise. But considering what I notified
with my 30+ Clients, the range [15 days – 60
Days] is the best in class to use to assure a
regular frequency to handle your data.
4. The type & relevancy of these meetings.
“Too many meetings while we could get
all of this dealt into one”. Wrong. 100%
wrong. Please don’t do it. I have been in
such meeting. The first word I think when
somebody talked to me about it is “a
slaughter”. Noise & Interferences are
legion. Reluctance and even Resistance can
and will quickly appear. In conclusion, the
best way to sabotage all of your work. So
think twice about this condition. The best
way is to state which department is
involved into the New Business
Development & New Product
Development processes and impacted by
the data categories and then to seek for
synergies. Let’s take the 10 categories of
value you can extract from structured data
I provided before. I have added in that
table the departments that not only
beneficiated of the final insights but
worked in that sense as well:

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A quick look on that table allow us to see how
strong can be (and should be) the connections
between the departments when it only
involves structured data. In this case, the
assessment of 30+ organizations in chemistry
industry that extracted such value showed
numerous connections and opportunities for
the departments to meet each other on several
topics:

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From such assessment, three types of meetings
I conducted with my Clients were selected as
critical to extract the right value for and with
the corresponding departments:
 The Pipeline Process review meeting –
that can be part of your pipeline review
meeting – where you meet the
Technical Support, the Sales force and
if needed, the logistics department (if
sampling issues occurred).
 The Lab Trials Review meeting – A
privileged moment where the
marketing, R&D and Technical support
departments can exchange on
technical failures, successes and
improvements – as well as on
“technology attributes” to be
redefined / improved in order to
accurate the positioning and/or value
proposition.
 The “Target & Project” Identification
workshop – on a quarterly basis – that
brings around the table the marketing,
sales & technical support departments
to ensure that all of them are aligned
on the “high chance of success to
convert” prospects.
From these meetings, you & your departments
should be able to extract actionable insights,
the Holy Grail & purpose of all this to optimize
your NBD / NPD process and support your next
Business decisions & strategy. As example, let
me share a direct benefit for the Marketing
department and the actionable insights that
allowed it:
In order to leverage such value, transferring &
implementing insights into the day-to-day
actions of your departments is of course the
next step. In other words, it’s time to build the
third pillar of your data to business bridge.
3. Insights transfer &
implementation into the
relevant departments
Let’s assess what you have now. First of all,
a structured data stored in an accessible
location. Secondly, cross-departments
meetings with key stakeholders of each field
that extract actionable insights from this data
set on regular basis. It is time for them to
receive and use such insights.
a. Step 1: Two keys to transfer your
insights
Logically, once you have collected your
actionable insights it should be easy to transfer
it to the relevant departments thanks to your
meeting results – such as the previous table –
and the key stakeholders that attended it.
Acting as ambassadors and project leaders,
they will be strategic assets to convey the
valuable information into the departments and
to follow up on the day-to-day actions such
insights require. To summarize, two successful
keys to transfer your insights:

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With such leverages you should implement
easily the day to day actions that are needed.
These actions are the operational translation of
your insights – ie. The required steps to apply
your discoveries. To describe this part, I will
continue on the previous example and its
output – The actionable insights & its benefit
for the marketing department.
b. Step 2: Extract actions to take to
implement your insights.
The first step is of course to understand how
actionable are these insights. So identify and
list all the actions that are attached to the
insights by answering simple questions: how do
I do that? With whom? What material /
product or process is impacted? Who can help
me? Etc. You should get then the following
table:

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The actions should be translated by specific
skills & tools attached to various domains of
the department. In this case:
 Marketing:
o Strategic Marketing
o Digital & Content Marketing
o Operational & Product Marketing
o Market Insights Technics
Another example - Typical Sales Skills & Tools
to ensure a good completion of the actionable
insights.
 Sales:
o Qualification & Interview Skills
o Pipeline Management Knowledge
& Tools
o Key Players & Accounts
Management
o Multi-projects approach
o Hunting technics & Digital
Business Best Practices
I created an appendix that includes the
various tools I used by department to ensure
getting the expected benefits. I invite you to
review this part and to contact me for further
details if needed.
c. Step 3: Select your Project
Management approach - Waterfall vs
Agile
Before moving forward, let me specify that
here, both approaches are working and I am
just summarizing what I have experienced
during my past experience. I am not a fellow of
a particular approach and the objective is not
to go in details on pros & cons about Waterfall
& Agile project management. It is a guidance
that partly describes waterfall & agile
approaches to help you selecting the best
adapted one according to your business
objective and time restriction. It worked for me
and I am glad to share these concrete
examples. I invite you to discuss with me about
this part privately if questions remain.
It is time now to operationally complete the
actions for your department. In my sense, you
have several choices depending on the
resources you have at your disposal. Like any
other organization, Timing issues and Available
resources will be critical but complex to acquire
to lead this task. Considering project
management make sense here. But once again,
you may need to consider several approaches
depending on the type of actions & insights you
have. I mean the type of project management
you will select: A plan-driven, waterfall
approach or an adaptive & agile approach.
What will ensure the faster & achievable
output?
Chuck Cobbs defined the plan-driven approach
as the best at situations where some level of
predictability and control is required over the
costs and schedules of a project and that’s
what it is most suited for. In another hand,
where an Agile approach really shines is in the
area of adaptivity to maximize the business
value it produces in an uncertain environment.
Because the customer is more directly engaged
in the project as it progresses, changes are
encouraged, and feedback is more immediate;
the final deliverables are much more likely to
provide a higher level of quality and value to
the customer.
But that’s enough for the theory. Let’s go back
to our Marketing department and its planned
actions:

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 A plan driven approach would make you
select one insight and work with a schedule
on the different actions to achieve it. Based
on the priority level, you could then select
which of the insights has to be dealt in first
and when to coordinate with other
resources for each action at a specific
moment. Not very flexible but effective to
ensure “actioning” your top priority
insight.
 An agile approach would consider all the
actions your department must conduct and
create Items that regroup actions which
could be dealt in the meantime to ensure a
faster delivery of all the actions. It would
give as well the flexibility to reorganize the
actions or items in time based on the
results of the first sprint or the changing
environment your department will deal
with – such as R&D priorities that were
modified while you were expecting your
new data set.
Each presents advantages & limitations and a
hybrid approach is of course another path that
you should consider as well. A nice tool to
select this may be a chart model answering two
questions: how should I be flexible to complete
these actions? How should I be adaptive
considering their nature and the environment
they evolve? And to confirm your choice, ask
yourself how certain you are to complete the
actions based on your environment. Lower the
certainty is, higher will be the agility to get.
With these 3 steps, you should expect to reach
your objectives and to “action” your insights.
But that’s not the end. One pillar remains and
it is definitely not the less important.
Communicating on your results will be key to
unify everyone to the “Data-To-Business”

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principle and convince your decision-makers
and business leaders to select the paths you
have detected and opened.
4. Results communication &
Key Decisions
Previously, I introduced you to 10
recurrent categories of value you can extract
from your data set. To help you building your
fourth pillar, one of these categories remains
key:
No surprise here. After all, business objectives
are to make money. And the best way to show
how your actionable insights were significant
to your organization is to provide evidences
that they did improve your processes and
increase your results. But even with this
guidance, nowadays I assume we all agree that
“KPI” means everything and anything. We can
measure whatever we want and give it the
meaning we aim to reach. And more and more,
for collaborators, “KPI” ceases to be Key-
Performance-Indicator and become “Way to
Track & Criticize my work”. I say, “It has to
end”. It may be time to discuss again –
altogether – about what it really matters.
1. Define & monitor KPIs collectively
KPIs should be seen and considered as tools
helping to detect process issues or distress
signal of collaborators – and no more as
collective / individual productivity & quality
assessment tools deserving Management
purpose. Considering this, maintaining a
discussion that set up & monitor KPIs
altogether seems more than acceptable.
So, first thing to consider is obviously to set up
these KPIs with all the departments. I mean,
all the key stakeholders identified within these
departments to ensure that what you measure
can be first of all collected (see first pillar: data
consolidation) and then if the KPIs are relevant
enough.
Moreover, KPIs are, in a way, actionable
insights. Because they force you to see Reality
as it should be. That’s your Red Pill versus the
common skepticism that old processes
implemented over time, more than ever in a
world that has drastically accelerated its
rhythm.
2. Use & rethink your KPIs to show
results, block certitudes and avoid
distress & issues
Among the numerous KPIs I handled to ensure
running healthy new business development
pipelines I have been confronted to many
situations that only KPIs highlighted over time
and against certitudes.
For instance: No-Disclosure Agreements (NDA).
When my clients asked such document to the
engaged prospects prior to sample them, 50 to
70% of business opportunities / prospect’s
projects in average were killed and rest of the
projects were ridiculously delayed thanks
(that’s ironic) to numerous email exchanges
between Sales; R&D; Legal and finally the
prospect’s organization (ie. The same
departments). Argues like “It is a way to
ensure the project is serious” cannot hold any
longer in opposition to such metric, especially
when you start to monetize the generated
revenue loss. And it may be time to discuss.
Concretely. To set up real and effective
“corrective actions”.
Finally, KPIs are great to show improvements &
success. But here, I guess we are all aligned. I
would just like to mention one important point.
Most of the time, companies have their own

The Data-To-Business Bridge Model Mathieu Rioult
15
notion of success & improvement.
Consequently, they define and apply their own
KPIs, even if classical ones cannot be avoided.
Someway, it is a good thing. But they should
rethink them or create new ones on regular
basis. For each KPI it exists a definition and an
angle. That is the same for a strategic matrix. I
am a fervent defender of the “thinking outside
the box” mindset so in my opinion, behind a KPI
can be usually found several ones that may
bring a new light on what have been
accomplished. And there can be even more like
unexpected results or insights. So, implement
KPIs of success but do not hesitate to question
their ability to fully show the value you
brought.
To summarize:
 Define & Monitor relevant KPIs with all
the departments / key stakeholders
 Detect the KPIs and rank the KPIs that
correspond to Success, Insights and
Counter-Skepticism.
 For each KPI, question yourself and the
team to check if another angle could be
applied.
Once your information is collected, take a
breath and:
- Extract from your tracked results the
key messages
- Regroup them by topic
- Link the topics to the benefits you are
seeking for, the ongoing business
challenges you face or the missions you
have.
With the right communication skills, this part
should not be the hardest one. Because your
data is already structured. Because your teams
are working together and extracting actionable
insights. Because their related actions have
generated results. Results you have tracked.
As Bandmaster, what you need is just to end
your partition without any false music note.
And positively impact your Organization & its
Business needs.

The Data-To-Business Bridge Model Mathieu Rioult
16
CONCLUSION – The Data-to-Business Bridge as a Process, Human Interactions & Structured Data as its Fuel
To summarize what we reviewed all along this article, below is the recap of the key steps to take to build your Data-to-Business Bridge:

The Data-To-Business Bridge Model Mathieu Rioult
17
As previously described in this article, I
built the data to business bridge as an answer
to manage & exploit multi-streams data that
are collected every day and ensure capitalizing
on the generated value. This bridge is based on
four pillars that translate unstructured streams
of information into day-to-day actions serving
your business strategy. To conclude this article,
I would like to take a step back here. Two key
principles were highlighted while I described
each pillar and this – on a recurrent manner:
- The need to identify & structure the data.
Fact- The massive streams of data we face is
going to extend. At some points – what was
“nice to have”, optional to deal with (such as
social medias) or not-so-important hurdle
(CRM tools used as storage facility) before
will become a requirement to handle or
urgent issue to address. Plus, among this
mass of raw information, weak signals,
potential blue oceans, new applications and
elements feeding the drivers for Innovation
are waiting. Structured data with
unidimensional entry will allow and
accelerate their extraction and translation
into insights. That’s the fuel of the machine
learning of your organization. I mean, a fuel
compatible with all assets of your
organization and that make a huge difference
if you consider raw data that can only be
translated by one Individual’s “God Feeling”.
So identify the components of your fuel and
accept to enrich it with other components
that are and will be undoubtedly the main
ones in the future.
- The importance of human interactions.
Nowadays, lot of people tend to assimilate
“data” to complex algorithms & dark
elements that only robots can deal with. It
becomes naturally a topic to avoid or to let
specific people talking about it. Well, acting
like this will definitely end up any attempt to
extract value from the data set. Numerous
LinkedIn influencers highlight that the first
problem of the digital transformation is a
human one. And automated tools will solve
some problems or challenges but they should
be seen as support and not final solution. The
secret ingredient is quite ancient but
demonstrated numerous times its efficiency:
Collaborative work ie. Talking, Exchanging,
Thinking, Arguing and Taking decisions
altogether. Cross-core competencies
meetings where a collaborative group
reviews the data set, extracts the value in it
and formulates the related insights. It seems
odd but let’s face it. Human interactions &
conclusions must prevail – and that cannot be
an individual thought anymore.
Organizations are living organisms and WE,
employees, are their cells. During such
process like extracting value from a data set,
we grow, learn and extend our skills,
reinforcing our living environment (= The
organization). So more than ever, we need
human interactions & collaborative tasks.
Digital transformation did not remove the
“Human” side or organizations. It increased
its importance and accelerated the frequency
of using it.
Consider now the big picture. Growing Cells &
“comestible” Fuel. Human Beings interacting
and learning collaboratively & Structured data
understandable and exploitable by them.
Thanks to both and the D2B bridge guidance I
provided, your organization will be able to
identify and implement actionable insights that
will positively impact your overall business
growth and organizational processes.
Moreover, your organization will build by itself
a learning machine that each individual will
beneficiate from. Actions run by someone’s
God Feeling appear now as obsolete. For sure,
Alone, we go faster. Together, we go farer.
Even so, these actions may still occur and let’s
be honest, it can be a necessity sometimes. But
the following “organic” reaction will be the
generation of the corresponding data set,

The Data-To-Business Bridge Model Mathieu Rioult
18
processed as comprehensive information and
ready to be handled by your data-sensitive
departments that will learn from this individual
move - smart or not.
One last comment about this model: it does not
aim to remain close. I see it as an open one,
that can integrate departments, structures and
even data from a wider world: Third Parties,
Direct customers turned into Partners &
feeding your model, external consultants,
market interfaces ... An open environment that
interact as both Fuel (from the data each “open
actor” could bring) and Cell (as “open actor”
being represented by specific people with
various skills).
It is time now to take a decision. Will or will you
not take and adapt this model to your New
Business Development organization? Some
have already done it. Some are still hesitating.
Some start to define jobs to take care of it. And
on this last point, the smart ones hire
experienced profiles in cross-department
management with business & digital
sensitivity, the others hire trainees or
traditional profiles unfamiliar with these
domains. My opinion about this move? Let’s
see each other in five years. The difference will
be obvious. So this is my question to you by
considering the Data-To-Business bridge:
where do you stand?
To conclude, I do propose you a self-
assessment tool below that I designed to
measure your Data-To-Business level. All you
need is just to answer the questions and
multiply your score by the answer of the
frequency quote – then make your math and
read my comment on your corresponding note.
Thank you for your attention. I would welcome
any comment you may have and am more than
open to any discussion you would like to start
with me.
Mathieu RIOULT

The Data-To-Business Bridge Model Mathieu Rioult
Appendix
Measure your Data-to-Business Level
Questions Total
Is there any discussion /
assessment with your
departments about the
business & technical data
needed and where they
do come from?
Yes – for all attributes (4pts)
Yes – but data
origin is not really
identified or hard
to capture (2pts)
No
Is there a place where
your data are centralized,
ranked AND accessible by
all to update & extract
them?
Yes – for all attributes (4pts)
Yes – but not
ranked or
accessible (2pts)
No
How often do they
review this data pool?
Are your Marketing,
Technical support and
R&D departments
discussing about the
value extracted from the
lab trials / test phase &
results obtained by your
prospects?
Yes / with Key
Stakeholder
sensitive to
Business & Digital
topics &
collaborative
mindset (3pts)
Yes / No
particular
profile
going to
these
meetings
(1pt)
No
(0pt)
Quarterly
basis or less
(x1)
Monthly
basis or
more (x2)
Do your Technical
Support, Sales & Logistics
departments meet
altogether to solve
hurdles & roadblocks that
could happen during the
prospect projects
development?
Yes / with Key
Stakeholder
sensitive to
Business & Digital
topics &
collaborative
mindset (3pts)
Yes / No
particular
profile
going to
these
meetings
(1pt)
No
(0pt)
Quarterly
basis or less
(x1)
Monthly
basis or
more (x2)
Is there any workshop
between Technical
Support, Marketing &
Sales departments to
define and update what
the acceptable and best
targets are and which
kind of information
should be collected?
Yes / with Key
Stakeholder
sensitive to
Business & Digital
topics &
collaborative
mindset (3pts)
Yes / No
particular
profile
going to
these
meetings
(1pt)
No
(0pt)
Each
semester or
less (x2)
Quarterly
basis or
more (x2)
Do you & your
departments meet
altogether and agree on
relevant KPIs, Tools &
Facts related to New
Business Development?
Yes (4pts)
Yes but KPIs / tools
/ facts are not
helping to take
decisions (2pts)
no (0pt)
TOTAL (/30)

The Data-To-Business Bridge Model Mathieu Rioult
Appendix
Comments about your result:
 25 or more: Your organization is on the right path. Being conscious that such value exists is a
thing but knowing where to find it and extracting it is definitely a strong asset that should have
already shown or will demonstrate soon its utility and positive impact on new business
development. Then, I propose you to review the appendix and to notice if you have already
observed within your organization the benefits I described as well as the tools I propose to
acquire them. Compare them to yours owns and do not hesitate to comment or add any
benefit / tool – I would be glad to discuss with you about it. Moreover, you should have noticed
already typical hurdles that slow down your business development or New spaces to go &
develop. I do recommend some lectures such as “Innovation Abyss”; “Strategy Blue Ocean” or
“Rocket Fuel” – those very insightful books may bring you answers you were seeking for.
 15 to 25: Your organization has been awakened to the Data-To-Business methodology.
However, the learning machine has to start now on a more regular basis or should go deeper
on the value extraction. Some benefits may have been already identified – but probably not
all of them and concrete figures will be expected from higher management level. This article
should then help you to structure your Data-to-Business bridge. The appendix is a list per
department of the potential benefits you can obtain from this value extraction and a set of
tools to use to reach them / measure their performance. It will be my pleasure to pursue this
discussion with more details on these benefits / tools if you are interested to learn more.
 Less than 15: Prior to review in details how you could extract any value from your data streams,
you may be interested in reading the following article from PWC
(http://www.pwc.com/us/en/advisory-services/data-possibilities/big-decision-survey.html).
In this survey (with +2000 executive respondents from various industries) they highlight that
“companies need to be faster and more sophisticated when it comes to decision-making
capabilities. They're seeking the right mix of mind and machine to leverage data, understand
risk, and gain a competitive edge.”. Extracting value from both digital & human streams is part
of it, especially when your organization decision type is focused on new business development,
new product development or new market development. I do propose you to take a look on
the potential benefits you can acquire by exploring such path and to discuss about it. Ready to
jump in a brave new world?

The Data-To-Business Bridge Model Mathieu Rioult
Appendix
Appendix / Benefits per department & Tools used:
This section has been structured by department – to clearly highlight which department can
beneficiate of what and how.
Yes, how. I have added as well a list of tools that I used or saw in action and that played a major role
in realizing the value extraction. Some tools may appear familiar to you and some very dark &
mysterious. If so, please note that I remain more than open to provide some details to facilitate their
meaning & use.
I have started this section by the most impacted departments to the less ones. Time to discover now
in details how the Data To Business can help you jumping to the next NBD level:
I. Sales Department
Benefits
 Higher Chance to convert & Better Focus of Sales Force:
o Creation & Development of consequent & structured global business opportunity
pipeline (segmented business projects)
o Accurate Forecast of pipeline revenue (Estimated Annual Volume & Revenue
correlated to pipeline momentum)
o Improved prospects profiles:
 technical & marketing fit
 project type
 insights on market momentum
o Sales
 Faster Speed to convert:
o Optimization of Sales Process:
 new or optimized resources (based on Real Full Time Equivalent)
 improved sampling
 application of best practices for each stage of the pipeline
 …
Tools
 Target profiling:
o “Target & Project” Identification workshops (with TS ; Mkt ; Sales)
o Interviews Process
 Interview approach (Prospect vs Partner)
 Qualification Scripts & Guidelines
 Insights distribution (what information to which department?)
o Target Selection Matrix
 Pipeline Development:

The Data-To-Business Bridge Model Mathieu Rioult
Appendix
o Pipeline Process (7 stages with defined entry & exit doors for each)
o Pipeline Monitoring & Management (Frequency of updates & Pipeline Review
Meetings)
o Optimization & Insights Technics
 First Contact support
 Methodology & Best Practices
 Toolkits
o Pipeline KPIs & Forecast Tool
 Momentum index
 Strike Zone
 Mature EAV / EAR
 Chance to convert
 ….
 Key Players Mapping & Multiprojects approach
II. Marketing
Benefits
 Faster Settlement of Marketing Strategy
o (Re)positioning & Value Proposition (re)definition
 Segments
 Applications
 Benchmarks
 Needs/challenges
 Market momentum
 Technology attributes
o New Market Development
 Identification & validation of new markets & applications contributing to
growth objectives
 Segmentation of opportunity profiles & triggers for each
o Partnership establishment & Case Studies Creation
 Deeper Market Understanding
o Value Pricing
o Value Chain understanding & Key actors
o Unmet needs
Tools
 Marketing Content Creation
o Case Studies with Several Positioning & Value Proposition models + link with
actuality / challenges
o Material Concepts
 Market Insights
o Data generation methodology linked with Material Concept
o Market analysis / Market research to confirm trends
o Survey Creation (questionnaire relevancy) & Analysis
o Technology watch

The Data-To-Business Bridge Model Mathieu Rioult
Appendix
 New Application Discovery
o Methodology & roadmap
o Data Collection
o Analysis of market & technical certainty
 Business Prospects Analysis
o Technical Success scorecard of lab trials + Analysis with R&D / TS for accurate
segment mapping
o Qualitative feedback
III. Technical Support
Benefits
 Faster response & higher efficiency of the Technical Support:
o Improved technical knowledge of applications & problem solving capabilities
o Development of test methods, comparative studies, applicative simulations &
other technical data- Generation of new & expected technical data for the
targeted market
 Higher chance of Technical success for the prospect’s project:
o Decrease of project development failures at the lab trials level thanks to a more
accurate technical fit during the qualification process & a clearly identified
segment with high chance of technical success
Tools
 Technical Follow-up tools (with Pipeline Data) & Pipeline KPIs:
o Technical Success Scorecard
o Lab Results follow-up table with recommendations brought
 Technical support Documentation:
o Technical support toolkit & Lab Trials development handbook creation
 Data Generation strategy:
o Database collection tool (From pipeline data)
o Data Generation Table & Roadmap (Test Methods ; Studies ; etc)
IV. Research & Development
Benefits
 Stronger Technical Response
o Product improvement or New grades development to answer technical challenges
 Accelerated & Relevant Applied Research
o Development of new products for new applications
Tools
 Technical Follow-up tools (with Pipeline Data) & Pipeline KPIs:
o Prospects profile – details on applications.
o Technical Success Scorecard

The Data-To-Business Bridge Model Mathieu Rioult
Appendix
o Lab Results follow-up table with recommendations brought or reasons of failure
 Data Generation strategy:
o Database collection tool (From pipeline data – unmet needs & desired
specifications for new applications)
V. Logistics
Benefits
o Higher chance to convert
 Faster time to sample
Tools
 Pipeline KPIs
o Time to sample
 Pipeline Monitoring & Management (Frequency of updates & Pipeline Review Meetings)
 Optimization & Insights Technics